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Visual Dependency Transformers: Dependency Tree Emerges From Reversed Attention | Mingyu Ding, Yikang Shen, Lijie Fan, Zhenfang Chen, Zitian Chen, Ping Luo, Joshua B. Tenenbaum, Chuang Gan | Humans possess a versatile mechanism for extracting structured representations of our visual world. When looking at an image, we can decompose the scene into entities and their parts as well as obtain the dependencies between them. To mimic such capability, we propose Visual Dependency Transformers (DependencyViT) that can induce visual dependencies without any labels. We achieve that with a novel neural operator called reversed attention that can naturally capture long-range visual dependencies between image patches. Specifically, we formulate it as a dependency graph where a child token in reversed attention is trained to attend to its parent tokens and send information following a normalized probability distribution rather than gathering information in conventional self-attention. With such a design, hierarchies naturally emerge from reversed attention layers, and a dependency tree is progressively induced from leaf nodes to the root node unsupervisedly. DependencyViT offers several appealing benefits. (i) Entities and their parts in an image are represented by different subtrees, enabling part partitioning from dependencies; (ii) Dynamic visual pooling is made possible. The leaf nodes which rarely send messages can be pruned without hindering the model performance, based on which we propose the lightweight DependencyViT-Lite to reduce the computational and memory footprints; (iii) DependencyViT works well on both self- and weakly-supervised pretraining paradigms on ImageNet, and demonstrates its effectiveness on 8 datasets and 5 tasks, such as unsupervised part and saliency segmentation, recognition, and detection. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ding_Visual_Dependency_Transformers_Dependency_Tree_Emerges_From_Reversed_Attention_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ding_Visual_Dependency_Transformers_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03282 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_Visual_Dependency_Transformers_Dependency_Tree_Emerges_From_Reversed_Attention_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_Visual_Dependency_Transformers_Dependency_Tree_Emerges_From_Reversed_Attention_CVPR_2023_paper.html | CVPR 2023 | null |
Differentiable Architecture Search With Random Features | Xuanyang Zhang, Yonggang Li, Xiangyu Zhang, Yongtao Wang, Jian Sun | Differentiable architecture search (DARTS) has significantly promoted the development of NAS techniques because of its high search efficiency and effectiveness but suffers from performance collapse. In this paper, we make efforts to alleviate the performance collapse problem for DARTS from two aspects. First, we investigate the expressive power of the supernet in DARTS and then derive a new setup of DARTS paradigm with only training BatchNorm. Second, we theoretically find that random features dilute the auxiliary connection role of skip-connection in supernet optimization and enable search algorithm focus on fairer operation selection, thereby solving the performance collapse problem. We instantiate DARTS and PC-DARTS with random features to build an improved version for each named RF-DARTS and RF-PCDARTS respectively. Experimental results show that RF-DARTS obtains 94.36% test accuracy on CIFAR-10 (which is the nearest optimal result in NAS-Bench-201), and achieves the newest state-of-the-art top-1 test error of 24.0% on ImageNet when transferring from CIFAR-10. Moreover, RF-DARTS performs robustly across three datasets (CIFAR-10, CIFAR-100, and SVHN) and four search spaces (S1-S4). Besides, RF-PCDARTS achieves even better results on ImageNet, that is, 23.9% top-1 and 7.1% top-5 test error, surpassing representative methods like single-path, training-free, and partial-channel paradigms directly searched on ImageNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Differentiable_Architecture_Search_With_Random_Features_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Differentiable_Architecture_Search_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2208.08835 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Differentiable_Architecture_Search_With_Random_Features_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Differentiable_Architecture_Search_With_Random_Features_CVPR_2023_paper.html | CVPR 2023 | null |
Open-Set Fine-Grained Retrieval via Prompting Vision-Language Evaluator | Shijie Wang, Jianlong Chang, Haojie Li, Zhihui Wang, Wanli Ouyang, Qi Tian | Open-set fine-grained retrieval is an emerging challenge that requires an extra capability to retrieve unknown subcategories during evaluation. However, current works are rooted in the close-set scenarios, where all the subcategories are pre-defined, and make it hard to capture discriminative knowledge from unknown subcategories, consequently failing to handle the inevitable unknown subcategories in open-world scenarios. In this work, we propose a novel Prompting vision-Language Evaluator (PLEor) framework based on the recently introduced contrastive language-image pretraining (CLIP) model, for open-set fine-grained retrieval. PLEor could leverage pre-trained CLIP model to infer the discrepancies encompassing both pre-defined and unknown subcategories, called category-specific discrepancies, and transfer them to the backbone network trained in the close-set scenarios. To make pre-trained CLIP model sensitive to category-specific discrepancies, we design a dual prompt scheme to learn a vision prompt specifying the category-specific discrepancies, and turn random vectors with category names in a text prompt into category-specific discrepancy descriptions. Moreover, a vision-language evaluator is proposed to semantically align the vision and text prompts based on CLIP model, and reinforce each other. In addition, we propose an open-set knowledge transfer to transfer the category-specific discrepancies into the backbone network using knowledge distillation mechanism. A variety of quantitative and qualitative experiments show that our PLEor achieves promising performance on open-set fine-grained retrieval datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Open-Set_Fine-Grained_Retrieval_via_Prompting_Vision-Language_Evaluator_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Open-Set_Fine-Grained_Retrieval_via_Prompting_Vision-Language_Evaluator_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Open-Set_Fine-Grained_Retrieval_via_Prompting_Vision-Language_Evaluator_CVPR_2023_paper.html | CVPR 2023 | null |
Sibling-Attack: Rethinking Transferable Adversarial Attacks Against Face Recognition | Zexin Li, Bangjie Yin, Taiping Yao, Junfeng Guo, Shouhong Ding, Simin Chen, Cong Liu | A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i.e., inaccessible gradient and parameter information to attackers. While recent research took an important step towards attacking black-box FR models through leveraging transferability, their performance is still limited, especially against online commercial FR systems that can be pessimistic (e.g., a less than 50% ASR--attack success rate on average). Motivated by this, we present Sibling-Attack, a new FR attack technique for the first time explores a novel multi-task perspective (i.e., leveraging extra information from multi-correlated tasks to boost attacking transferability). Intuitively, Sibling-Attack selects a set of tasks correlated with FR and picks the Attribute Recognition (AR) task as the task used in Sibling-Attack based on theoretical and quantitative analysis. Sibling-Attack then develops an optimization framework that fuses adversarial gradient information through (1) constraining the cross-task features to be under the same space, (2) a joint-task meta optimization framework that enhances the gradient compatibility among tasks, and (3) a cross-task gradient stabilization method which mitigates the oscillation effect during attacking. Extensive experiments demonstrate that Sibling-Attack outperforms state-of-the-art FR attack techniques by a non-trivial margin, boosting ASR by 12.61% and 55.77% on average on state-of-the-art pre-trained FR models and two well-known, widely used commercial FR systems. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Sibling-Attack_Rethinking_Transferable_Adversarial_Attacks_Against_Face_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Sibling-Attack_Rethinking_Transferable_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Sibling-Attack_Rethinking_Transferable_Adversarial_Attacks_Against_Face_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Sibling-Attack_Rethinking_Transferable_Adversarial_Attacks_Against_Face_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Enhanced Stable View Synthesis | Nishant Jain, Suryansh Kumar, Luc Van Gool | We introduce an approach to enhance the novel view synthesis from images taken from a freely moving camera. The introduced approach focuses on outdoor scenes where recovering accurate geometric scaffold and camera pose is challenging, leading to inferior results using the state-of-the-art stable view synthesis (SVS) method. SVS and related methods fail for outdoor scenes primarily due to (i) over-relying on the multiview stereo (MVS) for geometric scaffold recovery and (ii) assuming COLMAP computed camera poses as the best possible estimates, despite it being well-studied that MVS 3D reconstruction accuracy is limited to scene disparity and camera-pose accuracy is sensitive to key-point correspondence selection. This work proposes a principled way to enhance novel view synthesis solutions drawing inspiration from the basics of multiple view geometry. By leveraging the complementary behavior of MVS and monocular depth, we arrive at a better scene depth per view for nearby and far points, respectively. Moreover, our approach jointly refines camera poses with image-based rendering via multiple rotation averaging graph optimization. The recovered scene depth and the camera-pose help better view-dependent on-surface feature aggregation of the entire scene. Extensive evaluation of our approach on the popular benchmark dataset, such as Tanks and Temples, shows substantial improvement in view synthesis results compared to the prior art. For instance, our method shows 1.5 dB of PSNR improvement on the Tank and Temples. Similar statistics are observed when tested on other benchmark datasets such as FVS, Mip-NeRF 360, and DTU. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jain_Enhanced_Stable_View_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jain_Enhanced_Stable_View_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.17094 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_Enhanced_Stable_View_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_Enhanced_Stable_View_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack | Hideaki Takahashi, Jingjing Liu, Yang Liu | Federated Learning with Model Distillation (FedMD) is a nascent collaborative learning paradigm, where only output logits of public datasets are transmitted as distilled knowledge, instead of passing on private model parameters that are susceptible to gradient inversion attacks, a known privacy risk in federated learning. In this paper, we found that even though sharing output logits of public datasets is safer than directly sharing gradients, there still exists a substantial risk of data exposure caused by carefully designed malicious attacks. Our study shows that a malicious server can inject a PLI (Paired-Logits Inversion) attack against FedMD and its variants by training an inversion neural network that exploits the confidence gap between the server and client models. Experiments on multiple facial recognition datasets validate that under FedMD-like schemes, by using paired server-client logits of public datasets only, the malicious server is able to reconstruct private images on all tested benchmarks with a high success rate. | https://openaccess.thecvf.com/content/CVPR2023/papers/Takahashi_Breaching_FedMD_Image_Recovery_via_Paired-Logits_Inversion_Attack_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Takahashi_Breaching_FedMD_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.11436 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Takahashi_Breaching_FedMD_Image_Recovery_via_Paired-Logits_Inversion_Attack_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Takahashi_Breaching_FedMD_Image_Recovery_via_Paired-Logits_Inversion_Attack_CVPR_2023_paper.html | CVPR 2023 | null |
TempSAL - Uncovering Temporal Information for Deep Saliency Prediction | Bahar Aydemir, Ludo Hoffstetter, Tong Zhang, Mathieu Salzmann, Sabine Süsstrunk | Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information such as scene context, semantic relationships, gaze direction, and object dissimilarity. However, none of these models consider the temporal nature of gaze shifts during image observation. We introduce a novel saliency prediction model that learns to output saliency maps in sequential time intervals by exploiting human temporal attention patterns. Our approach locally modulates the saliency predictions by combining the learned temporal maps. Our experiments show that our method outperforms the state-of-the-art models, including a multi-duration saliency model, on the SALICON benchmark and CodeCharts1k dataset. Our code is publicly available on GitHub. | https://openaccess.thecvf.com/content/CVPR2023/papers/Aydemir_TempSAL_-_Uncovering_Temporal_Information_for_Deep_Saliency_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Aydemir_TempSAL_-_Uncovering_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Aydemir_TempSAL_-_Uncovering_Temporal_Information_for_Deep_Saliency_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Aydemir_TempSAL_-_Uncovering_Temporal_Information_for_Deep_Saliency_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
Biomechanics-Guided Facial Action Unit Detection Through Force Modeling | Zijun Cui, Chenyi Kuang, Tian Gao, Kartik Talamadupula, Qiang Ji | Existing AU detection algorithms are mainly based on appearance information extracted from 2D images, and well-established facial biomechanics that governs 3D facial skin deformation is rarely considered. In this paper, we propose a biomechanics-guided AU detection approach, where facial muscle activation forces are modelled, and are employed to predict AU activation. Specifically, our model consists of two branches: 3D physics branch and 2D image branch. In 3D physics branch, we first derive the Euler-Lagrange equation governing facial deformation. The Euler-Lagrange equation represented as an ordinary differential equation (ODE) is embedded into a differentiable ODE solver. Muscle activation forces together with other physics parameters are firstly regressed, and then are utilized to simulate 3D deformation by solving the ODE. By leveraging facial biomechanics, we obtain physically plausible facial muscle activation forces. 2D image branch compensates 3D physics branch by employing additional appearance information from 2D images. Both estimated forces and appearance features are employed for AU detection. The proposed approach achieves competitive AU detection performance on two benchmark datasets. Furthermore, by leveraging biomechanics, our approach achieves outstanding performance with reduced training data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cui_Biomechanics-Guided_Facial_Action_Unit_Detection_Through_Force_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cui_Biomechanics-Guided_Facial_Action_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cui_Biomechanics-Guided_Facial_Action_Unit_Detection_Through_Force_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cui_Biomechanics-Guided_Facial_Action_Unit_Detection_Through_Force_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
Equiangular Basis Vectors | Yang Shen, Xuhao Sun, Xiu-Shen Wei | We propose Equiangular Basis Vectors (EBVs) for classification tasks. In deep neural networks, models usually end with a k-way fully connected layer with softmax to handle different classification tasks. The learning objective of these methods can be summarized as mapping the learned feature representations to the samples' label space. While in metric learning approaches, the main objective is to learn a transformation function that maps training data points from the original space to a new space where similar points are closer while dissimilar points become farther apart. Different from previous methods, our EBVs generate normalized vector embeddings as "predefined classifiers" which are required to not only be with the equal status between each other, but also be as orthogonal as possible. By minimizing the spherical distance of the embedding of an input between its categorical EBV in training, the predictions can be obtained by identifying the categorical EBV with the smallest distance during inference. Various experiments on the ImageNet-1K dataset and other downstream tasks demonstrate that our method outperforms the general fully connected classifier while it does not introduce huge additional computation compared with classical metric learning methods. Our EBVs won the first place in the 2022 DIGIX Global AI Challenge, and our code is open-source and available at https://github.com/NJUST-VIPGroup/Equiangular-Basis-Vectors. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_Equiangular_Basis_Vectors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shen_Equiangular_Basis_Vectors_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11637 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Equiangular_Basis_Vectors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Equiangular_Basis_Vectors_CVPR_2023_paper.html | CVPR 2023 | null |
PIRLNav: Pretraining With Imitation and RL Finetuning for ObjectNav | Ram Ramrakhya, Dhruv Batra, Erik Wijmans, Abhishek Das | We study ObjectGoal Navigation -- where a virtual robot situated in a new environment is asked to navigate to an object. Prior work has shown that imitation learning (IL) using behavior cloning (BC) on a dataset of human demonstrations achieves promising results. However, this has limitations -- 1) BC policies generalize poorly to new states, since the training mimics actions not their consequences, and 2) collecting demonstrations is expensive. On the other hand, reinforcement learning (RL) is trivially scalable, but requires careful reward engineering to achieve desirable behavior. We present PIRLNav, a two-stage learning scheme for BC pretraining on human demonstrations followed by RL-finetuning. This leads to a policy that achieves a success rate of 65.0% on ObjectNav (+5.0% absolute over previous state-of-the-art). Using this BC->RL training recipe, we present a rigorous empirical analysis of design choices. First, we investigate whether human demonstrations can be replaced with 'free' (automatically generated) sources of demonstrations, e.g. shortest paths (SP) or task-agnostic frontier exploration (FE) trajectories. We find that BC->RL on human demonstrations outperforms BC->RL on SP and FE trajectories, even when controlled for the same BC-pretraining success on train, and even on a subset of val episodes where BC-pretraining success favors the SP or FE policies. Next, we study how RL-finetuning performance scales with the size of the BC pretraining dataset. We find that as we increase the size of the BC-pretraining dataset and get to high BC accuracies, the improvements from RL-finetuning are smaller, and that 90% of the performance of our best BC->RL policy can be achieved with less than half the number of BC demonstrations. Finally, we analyze failure modes of our ObjectNav policies, and present guidelines for further improving them. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ramrakhya_PIRLNav_Pretraining_With_Imitation_and_RL_Finetuning_for_ObjectNav_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ramrakhya_PIRLNav_Pretraining_With_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.07302 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ramrakhya_PIRLNav_Pretraining_With_Imitation_and_RL_Finetuning_for_ObjectNav_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ramrakhya_PIRLNav_Pretraining_With_Imitation_and_RL_Finetuning_for_ObjectNav_CVPR_2023_paper.html | CVPR 2023 | null |
Megahertz Light Steering Without Moving Parts | Adithya Pediredla, Srinivasa G. Narasimhan, Maysamreza Chamanzar, Ioannis Gkioulekas | We introduce a light steering technology that operates at megahertz frequencies, has no moving parts, and costs less than a hundred dollars. Our technology can benefit many projector and imaging systems that critically rely on high-speed, reliable, low-cost, and wavelength-independent light steering, including laser scanning projectors, LiDAR sensors, and fluorescence microscopes. Our technology uses ultrasound waves to generate a spatiotemporally-varying refractive index field inside a compressible medium, such as water, turning the medium into a dynamic traveling lens. By controlling the electrical input of the ultrasound transducers that generate the waves, we can change the lens, and thus steer light, at the speed of sound (1.5 km/s in water). We build a physical prototype of this technology, use it to realize different scanning techniques at megahertz rates (three orders of magnitude faster than commercial alternatives such as galvo mirror scanners), and demonstrate proof-of-concept projector and LiDAR applications. To encourage further innovation towards this new technology, we derive the theory for its fundamental limits and develop a physically-accurate simulator for virtual design. Our technology offers a promising solution for achieving high-speed and low-cost light steering in a variety of applications. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pediredla_Megahertz_Light_Steering_Without_Moving_Parts_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pediredla_Megahertz_Light_Steering_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pediredla_Megahertz_Light_Steering_Without_Moving_Parts_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pediredla_Megahertz_Light_Steering_Without_Moving_Parts_CVPR_2023_paper.html | CVPR 2023 | null |
Iterative Proposal Refinement for Weakly-Supervised Video Grounding | Meng Cao, Fangyun Wei, Can Xu, Xiubo Geng, Long Chen, Can Zhang, Yuexian Zou, Tao Shen, Daxin Jiang | Weakly-Supervised Video Grounding (WSVG) aims to localize events of interest in untrimmed videos with only video-level annotations. To date, most of the state-of-the-art WSVG methods follow a two-stage pipeline, i.e., firstly generating potential temporal proposals and then grounding with these proposal candidates. Despite the recent progress, existing proposal generation methods suffer from two drawbacks: 1) lack of explicit correspondence modeling; and 2) partial coverage of complex events. To this end, we propose a novel IteRative prOposal refiNement network (dubbed as IRON) to gradually distill the prior knowledge into each proposal and encourage proposals with more complete coverage. Specifically, we set up two lightweight distillation branches to uncover the cross-modal correspondence on both the semantic and conceptual levels. Then, an iterative Label Propagation (LP) strategy is devised to prevent the network from focusing excessively on the most discriminative events instead of the whole sentence content. Precisely, during each iteration, the proposal with the minimal distillation loss and its adjacent ones are regarded as the positive samples, which refines proposal confidence scores in a cascaded manner. Extensive experiments and ablation studies on two challenging WSVG datasets have attested to the effectiveness of our IRON. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Iterative_Proposal_Refinement_for_Weakly-Supervised_Video_Grounding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cao_Iterative_Proposal_Refinement_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Iterative_Proposal_Refinement_for_Weakly-Supervised_Video_Grounding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cao_Iterative_Proposal_Refinement_for_Weakly-Supervised_Video_Grounding_CVPR_2023_paper.html | CVPR 2023 | null |
SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy | Jiafeng Li, Ying Wen, Lianghua He | Convolutional Neural Networks (CNNs) have achieved remarkable performance in various computer vision tasks but this comes at the cost of tremendous computational resources, partly due to convolutional layers extracting redundant features. Recent works either compress well-trained large-scale models or explore well-designed lightweight models. In this paper, we make an attempt to exploit spatial and channel redundancy among features for CNN compression and propose an efficient convolution module, called SCConv (Spatial and Channel reconstruction Convolution), to decrease redundant computing and facilitate representative feature learning. The proposed SCConv consists of two units: spatial reconstruction unit (SRU) and channel reconstruction unit (CRU). SRU utilizes a separate-and-reconstruct method to suppress the spatial redundancy while CRU uses a split-transform-and-fuse strategy to diminish the channel redundancy. In addition, SCConv is a plug-and-play architectural unit that can be used to replace standard convolution in various convolutional neural networks directly. Experimental results show that SCConv-embedded models are able to achieve better performance by reducing redundant features with significantly lower complexity and computational costs. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_SCConv_Spatial_and_Channel_Reconstruction_Convolution_for_Feature_Redundancy_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SCConv_Spatial_and_Channel_Reconstruction_Convolution_for_Feature_Redundancy_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SCConv_Spatial_and_Channel_Reconstruction_Convolution_for_Feature_Redundancy_CVPR_2023_paper.html | CVPR 2023 | null |
StyleGene: Crossover and Mutation of Region-Level Facial Genes for Kinship Face Synthesis | Hao Li, Xianxu Hou, Zepeng Huang, Linlin Shen | High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of StyleGAN2. As cycle-like losses are designed to measure the L_2 distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-Databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_StyleGene_Crossover_and_Mutation_of_Region-Level_Facial_Genes_for_Kinship_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_StyleGene_Crossover_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_StyleGene_Crossover_and_Mutation_of_Region-Level_Facial_Genes_for_Kinship_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_StyleGene_Crossover_and_Mutation_of_Region-Level_Facial_Genes_for_Kinship_CVPR_2023_paper.html | CVPR 2023 | null |
Clothed Human Performance Capture With a Double-Layer Neural Radiance Fields | Kangkan Wang, Guofeng Zhang, Suxu Cong, Jian Yang | This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Clothed_Human_Performance_Capture_With_a_Double-Layer_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Clothed_Human_Performance_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Clothed_Human_Performance_Capture_With_a_Double-Layer_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Clothed_Human_Performance_Capture_With_a_Double-Layer_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
NeuFace: Realistic 3D Neural Face Rendering From Multi-View Images | Mingwu Zheng, Haiyu Zhang, Hongyu Yang, Di Huang | Realistic face rendering from multi-view images is beneficial to various computer vision and graphics applications. Due to the complex spatially-varying reflectance properties and geometry characteristics of faces, however, it remains challenging to recover 3D facial representations both faithfully and efficiently in the current studies. This paper presents a novel 3D face rendering model, namely NeuFace, to learn accurate and physically-meaningful underlying 3D representations by neural rendering techniques. It naturally incorporates the neural BRDFs into physically based rendering, capturing sophisticated facial geometry and appearance clues in a collaborative manner. Specifically, we introduce an approximated BRDF integration and a simple yet new low-rank prior, which effectively lower the ambiguities and boost the performance of the facial BRDFs. Extensive experiments demonstrate the superiority of NeuFace in human face rendering, along with a decent generalization ability to common objects. Code is released at https://github.com/aejion/NeuFace. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_NeuFace_Realistic_3D_Neural_Face_Rendering_From_Multi-View_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_NeuFace_Realistic_3D_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.14092 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_NeuFace_Realistic_3D_Neural_Face_Rendering_From_Multi-View_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_NeuFace_Realistic_3D_Neural_Face_Rendering_From_Multi-View_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Cross-Guided Optimization of Radiance Fields With Multi-View Image Super-Resolution for High-Resolution Novel View Synthesis | Youngho Yoon, Kuk-Jin Yoon | Novel View Synthesis (NVS) aims at synthesizing an image from an arbitrary viewpoint using multi-view images and camera poses. Among the methods for NVS, Neural Radiance Fields (NeRF) is capable of NVS for an arbitrary resolution as it learns a continuous volumetric representation. However, radiance fields rely heavily on the spectral characteristics of coordinate-based networks. Thus, there is a limit to improving the performance of high-resolution novel view synthesis (HRNVS). To solve this problem, we propose a novel framework using cross-guided optimization of the single-image super-resolution (SISR) and radiance fields. We perform multi-view image super-resolution (MVSR) on train-view images during the radiance fields optimization process. It derives the updated SR result by fusing the feature map obtained from SISR and voxel-based uncertainty fields generated by integrated errors of train-view images. By repeating the updates during radiance fields optimization, train-view images for radiance fields optimization have multi-view consistency and high-frequency details simultaneously, ultimately improving the performance of HRNVS. Experiments of HRNVS and MVSR on various benchmark datasets show that the proposed method significantly surpasses existing methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Yoon_Cross-Guided_Optimization_of_Radiance_Fields_With_Multi-View_Image_Super-Resolution_for_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yoon_Cross-Guided_Optimization_of_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yoon_Cross-Guided_Optimization_of_Radiance_Fields_With_Multi-View_Image_Super-Resolution_for_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yoon_Cross-Guided_Optimization_of_Radiance_Fields_With_Multi-View_Image_Super-Resolution_for_CVPR_2023_paper.html | CVPR 2023 | null |
Unified Pose Sequence Modeling | Lin Geng Foo, Tianjiao Li, Hossein Rahmani, Qiuhong Ke, Jun Liu | We propose a Unified Pose Sequence Modeling approach to unify heterogeneous human behavior understanding tasks based on pose data, e.g., action recognition, 3D pose estimation and 3D early action prediction. A major obstacle is that different pose-based tasks require different output data formats. Specifically, the action recognition and prediction tasks require class predictions as outputs, while 3D pose estimation requires a human pose output, which limits existing methods to leverage task-specific network architectures for each task. Hence, in this paper, we propose a novel Unified Pose Sequence (UPS) model to unify heterogeneous output formats for the aforementioned tasks by considering text-based action labels and coordinate-based human poses as language sequences. Then, by optimizing a single auto-regressive transformer, we can obtain a unified output sequence that can handle all the aforementioned tasks. Moreover, to avoid the interference brought by the heterogeneity between different tasks, a dynamic routing mechanism is also proposed to empower our UPS with the ability to learn which subsets of parameters should be shared among different tasks. To evaluate the efficacy of the proposed UPS, extensive experiments are conducted on four different tasks with four popular behavior understanding benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Foo_Unified_Pose_Sequence_Modeling_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Foo_Unified_Pose_Sequence_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Foo_Unified_Pose_Sequence_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
Probability-Based Global Cross-Modal Upsampling for Pansharpening | Zeyu Zhu, Xiangyong Cao, Man Zhou, Junhao Huang, Deyu Meng | Pansharpening is an essential preprocessing step for remote sensing image processing. Although deep learning (DL) approaches performed well on this task, current upsampling methods used in these approaches only utilize the local information of each pixel in the low-resolution multispectral (LRMS) image while neglecting to exploit its global information as well as the cross-modal information of the guiding panchromatic (PAN) image, which limits their performance improvement. To address this issue, this paper develops a novel probability-based global cross-modal upsampling (PGCU) method for pan-sharpening. Precisely, we first formulate the PGCU method from a probabilistic perspective and then design an efficient network module to implement it by fully utilizing the information mentioned above while simultaneously considering the channel specificity. The PGCU module consists of three blocks, i.e., information extraction (IE), distribution and expectation estimation (DEE), and fine adjustment (FA). Extensive experiments verify the superiority of the PGCU method compared with other popular upsampling methods. Additionally, experiments also show that the PGCU module can help improve the performance of existing SOTA deep learning pansharpening methods. The codes are available at https://github.com/Zeyu-Zhu/PGCU. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Probability-Based_Global_Cross-Modal_Upsampling_for_Pansharpening_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.13659 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Probability-Based_Global_Cross-Modal_Upsampling_for_Pansharpening_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Probability-Based_Global_Cross-Modal_Upsampling_for_Pansharpening_CVPR_2023_paper.html | CVPR 2023 | null |
Positive-Augmented Contrastive Learning for Image and Video Captioning Evaluation | Sara Sarto, Manuele Barraco, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara | The CLIP model has been recently proven to be very effective for a variety of cross-modal tasks, including the evaluation of captions generated from vision-and-language architectures. In this paper, we propose a new recipe for a contrastive-based evaluation metric for image captioning, namely Positive-Augmented Contrastive learning Score (PAC-S), that in a novel way unifies the learning of a contrastive visual-semantic space with the addition of generated images and text on curated data. Experiments spanning several datasets demonstrate that our new metric achieves the highest correlation with human judgments on both images and videos, outperforming existing reference-based metrics like CIDEr and SPICE and reference-free metrics like CLIP-Score. Finally, we test the system-level correlation of the proposed metric when considering popular image captioning approaches, and assess the impact of employing different cross-modal features. Our source code and trained models are publicly available at: https://github.com/aimagelab/pacscore. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sarto_Positive-Augmented_Contrastive_Learning_for_Image_and_Video_Captioning_Evaluation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sarto_Positive-Augmented_Contrastive_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sarto_Positive-Augmented_Contrastive_Learning_for_Image_and_Video_Captioning_Evaluation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sarto_Positive-Augmented_Contrastive_Learning_for_Image_and_Video_Captioning_Evaluation_CVPR_2023_paper.html | CVPR 2023 | null |
Rethinking Domain Generalization for Face Anti-Spoofing: Separability and Alignment | Yiyou Sun, Yaojie Liu, Xiaoming Liu, Yixuan Li, Wen-Sheng Chu | This work studies the generalization issue of face anti-spoofing (FAS) models on domain gaps, such as image resolution, blurriness and sensor variations. Most prior works regard domain-specific signals as a negative impact, and apply metric learning or adversarial losses to remove it from feature representation. Though learning a domain-invariant feature space is viable for the training data, we show that the feature shift still exists in an unseen test domain, which backfires on the generalizability of the classifier. In this work, instead of constructing a domain-invariant feature space, we encourage domain separability while aligning the live-to-spoof transition (i.e., the trajectory from live to spoof) to be the same for all domains. We formulate this FAS strategy of separability and alignment (SA-FAS) as a problem of invariant risk minimization (IRM), and learn domain-variant feature representation but domain-invariant classifier. We demonstrate the effectiveness of SA-FAS on challenging cross-domain FAS datasets and establish state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Rethinking_Domain_Generalization_for_Face_Anti-Spoofing_Separability_and_Alignment_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Rethinking_Domain_Generalization_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Rethinking_Domain_Generalization_for_Face_Anti-Spoofing_Separability_and_Alignment_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Rethinking_Domain_Generalization_for_Face_Anti-Spoofing_Separability_and_Alignment_CVPR_2023_paper.html | CVPR 2023 | null |
SMOC-Net: Leveraging Camera Pose for Self-Supervised Monocular Object Pose Estimation | Tao Tan, Qiulei Dong | Recently, self-supervised 6D object pose estimation, where synthetic images with object poses (sometimes jointly with un-annotated real images) are used for training, has attracted much attention in computer vision. Some typical works in literature employ a time-consuming differentiable renderer for object pose prediction at the training stage, so that (i) their performances on real images are generally limited due to the gap between their rendered images and real images and (ii) their training process is computationally expensive. To address the two problems, we propose a novel Network for Self-supervised Monocular Object pose estimation by utilizing the predicted Camera poses from un-annotated real images, called SMOC-Net. The proposed network is explored under a knowledge distillation framework, consisting of a teacher model and a student model. The teacher model contains a backbone estimation module for initial object pose estimation, and an object pose refiner for refining the initial object poses using a geometric constraint (called relative-pose constraint) derived from relative camera poses. The student model gains knowledge for object pose estimation from the teacher model by imposing the relative-pose constraint. Thanks to the relative-pose constraint, SMOC-Net could not only narrow the domain gap between synthetic and real data but also reduce the training cost. Experimental results on two public datasets demonstrate that SMOC-Net outperforms several state-of-the-art methods by a large margin while requiring much less training time than the differentiable-renderer-based methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tan_SMOC-Net_Leveraging_Camera_Pose_for_Self-Supervised_Monocular_Object_Pose_Estimation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_SMOC-Net_Leveraging_Camera_Pose_for_Self-Supervised_Monocular_Object_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_SMOC-Net_Leveraging_Camera_Pose_for_Self-Supervised_Monocular_Object_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
FAC: 3D Representation Learning via Foreground Aware Feature Contrast | Kangcheng Liu, Aoran Xiao, Xiaoqin Zhang, Shijian Lu, Ling Shao | Contrastive learning has recently demonstrated great potential for unsupervised pre-training in 3D scene understanding tasks. However, most existing work randomly selects point features as anchors while building contrast, leading to a clear bias toward background points that often dominate in 3D scenes. Also, object awareness and foreground-to-background discrimination are neglected, making contrastive learning less effective. To tackle these issues, we propose a general foreground-aware feature contrast (FAC) framework to learn more effective point cloud representations in pre-training. FAC consists of two novel contrast designs to construct more effective and informative contrast pairs. The first is building positive pairs within the same foreground segment where points tend to have the same semantics. The second is that we prevent over-discrimination between 3D segments/objects and encourage foreground-to-background distinctions at the segment level with adaptive feature learning in a Siamese correspondence network, which adaptively learns feature correlations within and across point cloud views effectively. Visualization with point activation maps shows that our contrast pairs capture clear correspondences among foreground regions during pre-training. Quantitative experiments also show that FAC achieves superior knowledge transfer and data efficiency in various downstream 3D semantic segmentation and object detection tasks. All codes, data, and models are available at:https://github.com/KangchengLiu/FAC_Foreground_Aware_Contrast. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_FAC_3D_Representation_Learning_via_Foreground_Aware_Feature_Contrast_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_FAC_3D_Representation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06388 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_FAC_3D_Representation_Learning_via_Foreground_Aware_Feature_Contrast_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_FAC_3D_Representation_Learning_via_Foreground_Aware_Feature_Contrast_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Visual Representation Learning Through Perceptual Understanding | Samyakh Tukra, Frederick Hoffman, Ken Chatfield | We present an extension to masked autoencoders (MAE) which improves on the representations learnt by the model by explicitly encouraging the learning of higher scene-level features. We do this by: (i) the introduction of a perceptual similarity term between generated and real images (ii) incorporating several techniques from the adversarial training literature including multi-scale training and adaptive discriminator augmentation. The combination of these results in not only better pixel reconstruction but also representations which appear to capture better higher-level details within images. More consequentially, we show how our method, Perceptual MAE, leads to better performance when used for downstream tasks outperforming previous methods. We achieve 78.1% top-1 accuracy linear probing on ImageNet-1K and up to 88.1% when fine-tuning, with similar results for other downstream tasks, all without use of additional pre-trained models or data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tukra_Improving_Visual_Representation_Learning_Through_Perceptual_Understanding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tukra_Improving_Visual_Representation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.14504 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tukra_Improving_Visual_Representation_Learning_Through_Perceptual_Understanding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tukra_Improving_Visual_Representation_Learning_Through_Perceptual_Understanding_CVPR_2023_paper.html | CVPR 2023 | null |
3D Cinemagraphy From a Single Image | Xingyi Li, Zhiguo Cao, Huiqiang Sun, Jianming Zhang, Ke Xian, Guosheng Lin | We present 3D Cinemagraphy, a new technique that marries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains both visual content animation and camera motion. We empirically find that naively combining existing 2D image animation and 3D photography methods leads to obvious artifacts or inconsistent animation. Our key insight is that representing and animating the scene in 3D space offers a natural solution to this task. To this end, we first convert the input image into feature-based layered depth images using predicted depth values, followed by unprojecting them to a feature point cloud. To animate the scene, we perform motion estimation and lift the 2D motion into the 3D scene flow. Finally, to resolve the problem of hole emergence as points move forward, we propose to bidirectionally displace the point cloud as per the scene flow and synthesize novel views by separately projecting them into target image planes and blending the results. Extensive experiments demonstrate the effectiveness of our method. A user study is also conducted to validate the compelling rendering results of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_3D_Cinemagraphy_From_a_Single_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_3D_Cinemagraphy_From_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.05724 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_3D_Cinemagraphy_From_a_Single_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_3D_Cinemagraphy_From_a_Single_Image_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Bottleneck Concepts in Image Classification | Bowen Wang, Liangzhi Li, Yuta Nakashima, Hajime Nagahara | Interpreting and explaining the behavior of deep neural networks is critical for many tasks. Explainable AI provides a way to address this challenge, mostly by providing per-pixel relevance to the decision. Yet, interpreting such explanations may require expert knowledge. Some recent attempts toward interpretability adopt a concept-based framework, giving a higher-level relationship between some concepts and model decisions. This paper proposes Bottleneck Concept Learner (BotCL), which represents an image solely by the presence/absence of concepts learned through training over the target task without explicit supervision over the concepts. It uses self-supervision and tailored regularizers so that learned concepts can be human-understandable. Using some image classification tasks as our testbed, we demonstrate BotCL's potential to rebuild neural networks for better interpretability. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Learning_Bottleneck_Concepts_in_Image_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Learning_Bottleneck_Concepts_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.10131 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Learning_Bottleneck_Concepts_in_Image_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Learning_Bottleneck_Concepts_in_Image_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
Inversion-Based Style Transfer With Diffusion Models | Yuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang, Chongyang Ma, Weiming Dong, Changsheng Xu | The artistic style within a painting is the means of expression, which includes not only the painting material, colors, and brushstrokes, but also the high-level attributes, including semantic elements and object shapes. Previous arbitrary example-guided artistic image generation methods often fail to control shape changes or convey elements. Pre-trained text-to-image synthesis diffusion probabilistic models have achieved remarkable quality but often require extensive textual descriptions to accurately portray the attributes of a particular painting.The uniqueness of an artwork lies in the fact that it cannot be adequately explained with normal language. Our key idea is to learn the artistic style directly from a single painting and then guide the synthesis without providing complex textual descriptions. Specifically, we perceive style as a learnable textual description of a painting.We propose an inversion-based style transfer method (InST), which can efficiently and accurately learn the key information of an image, thus capturing and transferring the artistic style of a painting. We demonstrate the quality and efficiency of our method on numerous paintings of various artists and styles. Codes are available at https://github.com/zyxElsa/InST. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Inversion-Based_Style_Transfer_With_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Inversion-Based_Style_Transfer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.13203 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Inversion-Based_Style_Transfer_With_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Inversion-Based_Style_Transfer_With_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Human Mesh Recovery in 3D Scenes | Zehong Shen, Zhi Cen, Sida Peng, Qing Shuai, Hujun Bao, Xiaowei Zhou | We present a novel method for recovering the absolute pose and shape of a human in a pre-scanned scene given a single image. Unlike previous methods that perform sceneaware mesh optimization, we propose to first estimate absolute position and dense scene contacts with a sparse 3D CNN, and later enhance a pretrained human mesh recovery network by cross-attention with the derived 3D scene cues. Joint learning on images and scene geometry enables our method to reduce the ambiguity caused by depth and occlusion, resulting in more reasonable global postures and contacts. Encoding scene-aware cues in the network also allows the proposed method to be optimization-free, and opens up the opportunity for real-time applications. The experiments show that the proposed network is capable of recovering accurate and physically-plausible meshes by a single forward pass and outperforms state-of-the-art methods in terms of both accuracy and speed. Code is available on our project page: https://zju3dv.github.io/sahmr/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_Learning_Human_Mesh_Recovery_in_3D_Scenes_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Learning_Human_Mesh_Recovery_in_3D_Scenes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Learning_Human_Mesh_Recovery_in_3D_Scenes_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Locally Editable Virtual Humans | Hsuan-I Ho, Lixin Xue, Jie Song, Otmar Hilliges | In this paper, we propose a novel hybrid representation and end-to-end trainable network architecture to model fully editable and customizable neural avatars. At the core of our work lies a representation that combines the modeling power of neural fields with the ease of use and inherent 3D consistency of skinned meshes. To this end, we construct a trainable feature codebook to store local geometry and texture features on the vertices of a deformable body model, thus exploiting its consistent topology under articulation. This representation is then employed in a generative auto-decoder architecture that admits fitting to unseen scans and sampling of realistic avatars with varied appearances and geometries. Furthermore, our representation allows local editing by swapping local features between 3D assets. To verify our method for avatar creation and editing, we contribute a new high-quality dataset, dubbed CustomHumans, for training and evaluation. Our experiments quantitatively and qualitatively show that our method generates diverse detailed avatars and achieves better model fitting performance compared to state-of-the-art methods. Our code and dataset are available at https://ait.ethz.ch/custom-humans. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ho_Learning_Locally_Editable_Virtual_Humans_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ho_Learning_Locally_Editable_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.00121 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ho_Learning_Locally_Editable_Virtual_Humans_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ho_Learning_Locally_Editable_Virtual_Humans_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Imbalanced Data With Vision Transformers | Zhengzhuo Xu, Ruikang Liu, Shuo Yang, Zenghao Chai, Chun Yuan | The real-world data tends to be heavily imbalanced and severely skew the data-driven deep neural networks, which makes Long-Tailed Recognition (LTR) a massive challenging task. Existing LTR methods seldom train Vision Transformers (ViTs) with Long-Tailed (LT) data, while the off-the-shelf pretrain weight of ViTs always leads to unfair comparisons. In this paper, we systematically investigate the ViTs' performance in LTR and propose LiVT to train ViTs from scratch only with LT data. With the observation that ViTs suffer more severe LTR problems, we conduct Masked Generative Pretraining (MGP) to learn generalized features. With ample and solid evidence, we show that MGP is more robust than supervised manners. Although Binary Cross Entropy (BCE) loss performs well with ViTs, it struggles on the LTR tasks. We further propose the balanced BCE to ameliorate it with strong theoretical groundings. Specially, we derive the unbiased extension of Sigmoid and compensate extra logit margins for deploying it. Our Bal-BCE contributes to the quick convergence of ViTs in just a few epochs. Extensive experiments demonstrate that with MGP and Bal-BCE, LiVT successfully trains ViTs well without any additional data and outperforms comparable state-of-the-art methods significantly, e.g., our ViT-B achieves 81.0% Top-1 accuracy in iNaturalist 2018 without bells and whistles. Code is available at https://github.com/XuZhengzhuo/LiVT. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Learning_Imbalanced_Data_With_Vision_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Learning_Imbalanced_Data_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.02015 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_Imbalanced_Data_With_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_Imbalanced_Data_With_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning | Runqi Wang, Xiaoyue Duan, Guoliang Kang, Jianzhuang Liu, Shaohui Lin, Songcen Xu, Jinhu Lü, Baochang Zhang | Continual learning aims to enable a model to incrementally learn knowledge from sequentially arrived data. Previous works adopt the conventional classification architecture, which consists of a feature extractor and a classifier. The feature extractor is shared across sequentially arrived tasks or classes, but one specific group of weights of the classifier corresponding to one new class should be incrementally expanded. Consequently, the parameters of a continual learner gradually increase. Moreover, as the classifier contains all historical arrived classes, a certain size of the memory is usually required to store rehearsal data to mitigate classifier bias and catastrophic forgetting. In this paper, we propose a non-incremental learner, named AttriCLIP, to incrementally extract knowledge of new classes or tasks. Specifically, AttriCLIP is built upon the pre-trained visual-language model CLIP. Its image encoder and text encoder are fixed to extract features from both images and text prompts. Each text prompt consists of a category name and a fixed number of learnable parameters which are selected from our designed attribute bank and serve as attributes. As we compute the visual and textual similarity for classification, AttriCLIP is a non-incremental learner. The attribute prompts, which encode the common knowledge useful for classification, can effectively mitigate the catastrophic forgetting and avoid constructing a replay memory. We empirically evaluate our AttriCLIP and compare it with CLIP-based and previous state-of-the-art continual learning methods in realistic settings with domain-shift and long-sequence learning. The results show that our method performs favorably against previous state-of-the-arts. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_AttriCLIP_A_Non-Incremental_Learner_for_Incremental_Knowledge_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_AttriCLIP_A_Non-Incremental_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_AttriCLIP_A_Non-Incremental_Learner_for_Incremental_Knowledge_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_AttriCLIP_A_Non-Incremental_Learner_for_Incremental_Knowledge_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
PHA: Patch-Wise High-Frequency Augmentation for Transformer-Based Person Re-Identification | Guiwei Zhang, Yongfei Zhang, Tianyu Zhang, Bo Li, Shiliang Pu | Although recent studies empirically show that injecting Convolutional Neural Networks (CNNs) into Vision Transformers (ViTs) can improve the performance of person re-identification, the rationale behind it remains elusive. From a frequency perspective, we reveal that ViTs perform worse than CNNs in preserving key high-frequency components (e.g, clothes texture details) since high-frequency components are inevitably diluted by low-frequency ones due to the intrinsic Self-Attention within ViTs. To remedy such inadequacy of the ViT, we propose a Patch-wise High-frequency Augmentation (PHA) method with two core designs. First, to enhance the feature representation ability of high-frequency components, we split patches with high-frequency components by the Discrete Haar Wavelet Transform, then empower the ViT to take the split patches as auxiliary input. Second, to prevent high-frequency components from being diluted by low-frequency ones when taking the entire sequence as input during network optimization, we propose a novel patch-wise contrastive loss. From the view of gradient optimization, it acts as an implicit augmentation to improve the representation ability of key high-frequency components. This benefits the ViT to capture key high-frequency components to extract discriminative person representations. PHA is necessary during training and can be removed during inference, without bringing extra complexity. Extensive experiments on widely-used ReID datasets validate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_PHA_Patch-Wise_High-Frequency_Augmentation_for_Transformer-Based_Person_Re-Identification_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PHA_Patch-Wise_High-Frequency_Augmentation_for_Transformer-Based_Person_Re-Identification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PHA_Patch-Wise_High-Frequency_Augmentation_for_Transformer-Based_Person_Re-Identification_CVPR_2023_paper.html | CVPR 2023 | null |
StyleRes: Transforming the Residuals for Real Image Editing With StyleGAN | Hamza Pehlivan, Yusuf Dalva, Aysegul Dundar | We present a novel image inversion framework and a training pipeline to achieve high-fidelity image inversion with high-quality attribute editing. Inverting real images into StyleGAN's latent space is an extensively studied problem, yet the trade-off between the image reconstruction fidelity and image editing quality remains an open challenge. The low-rate latent spaces are limited in their expressiveness power for high-fidelity reconstruction. On the other hand, high-rate latent spaces result in degradation in editing quality. In this work, to achieve high-fidelity inversion, we learn residual features in higher latent codes that lower latent codes were not able to encode. This enables preserving image details in reconstruction. To achieve high-quality editing, we learn how to transform the residual features for adapting to manipulations in latent codes. We train the framework to extract residual features and transform them via a novel architecture pipeline and cycle consistency losses. We run extensive experiments and compare our method with state-of-the-art inversion methods. Qualitative metrics and visual comparisons show significant improvements. | https://openaccess.thecvf.com/content/CVPR2023/papers/Pehlivan_StyleRes_Transforming_the_Residuals_for_Real_Image_Editing_With_StyleGAN_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pehlivan_StyleRes_Transforming_the_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.14359 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pehlivan_StyleRes_Transforming_the_Residuals_for_Real_Image_Editing_With_StyleGAN_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pehlivan_StyleRes_Transforming_the_Residuals_for_Real_Image_Editing_With_StyleGAN_CVPR_2023_paper.html | CVPR 2023 | null |
Diffusion Video Autoencoders: Toward Temporally Consistent Face Video Editing via Disentangled Video Encoding | Gyeongman Kim, Hajin Shim, Hyunsu Kim, Yunjey Choi, Junho Kim, Eunho Yang | Inspired by the impressive performance of recent face image editing methods, several studies have been naturally proposed to extend these methods to the face video editing task. One of the main challenges here is temporal consistency among edited frames, which is still unresolved. To this end, we propose a novel face video editing framework based on diffusion autoencoders that can successfully extract the decomposed features - for the first time as a face video editing model - of identity and motion from a given video. This modeling allows us to edit the video by simply manipulating the temporally invariant feature to the desired direction for the consistency. Another unique strength of our model is that, since our model is based on diffusion models, it can satisfy both reconstruction and edit capabilities at the same time, and is robust to corner cases in wild face videos (e.g. occluded faces) unlike the existing GAN-based methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Diffusion_Video_Autoencoders_Toward_Temporally_Consistent_Face_Video_Editing_via_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Diffusion_Video_Autoencoders_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.02802 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Diffusion_Video_Autoencoders_Toward_Temporally_Consistent_Face_Video_Editing_via_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Diffusion_Video_Autoencoders_Toward_Temporally_Consistent_Face_Video_Editing_via_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce | Yang Jin, Yongzhi Li, Zehuan Yuan, Yadong Mu | This paper aims to establish a generic multi-modal foundation model that has the scalable capability to massive downstream applications in E-commerce. Recently, large-scale vision-language pretraining approaches have achieved remarkable advances in the general domain. However, due to the significant differences between natural and product images, directly applying these frameworks for modeling image-level representations to E-commerce will be inevitably sub-optimal. To this end, we propose an instance-centric multi-modal pretraining paradigm called ECLIP in this work. In detail, we craft a decoder architecture that introduces a set of learnable instance queries to explicitly aggregate instance-level semantics. Moreover, to enable the model to focus on the desired product instance without reliance on expensive manual annotations, two specially configured pretext tasks are further proposed. Pretrained on the 100 million E-commerce-related data, ECLIP successfully extracts more generic, semantic-rich, and robust representations. Extensive experimental results show that, without further fine-tuning, ECLIP surpasses existing methods by a large margin on a broad range of downstream tasks, demonstrating the strong transferability to real-world E-commerce applications. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_Learning_Instance-Level_Representation_for_Large-Scale_Multi-Modal_Pretraining_in_E-Commerce_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jin_Learning_Instance-Level_Representation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Learning_Instance-Level_Representation_for_Large-Scale_Multi-Modal_Pretraining_in_E-Commerce_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Learning_Instance-Level_Representation_for_Large-Scale_Multi-Modal_Pretraining_in_E-Commerce_CVPR_2023_paper.html | CVPR 2023 | null |
Conditional Text Image Generation With Diffusion Models | Yuanzhi Zhu, Zhaohai Li, Tianwei Wang, Mengchao He, Cong Yao | Current text recognition systems, including those for handwritten scripts and scene text, have relied heavily on image synthesis and augmentation, since it is difficult to realize real-world complexity and diversity through collecting and annotating enough real text images. In this paper, we explore the problem of text image generation, by taking advantage of the powerful abilities of Diffusion Models in generating photo-realistic and diverse image samples with given conditions, and propose a method called Conditional Text Image Generation with Diffusion Models (CTIG-DM for short). To conform to the characteristics of text images, we devise three conditions: image condition, text condition, and style condition, which can be used to control the attributes, contents, and styles of the samples in the image generation process. Specifically, four text image generation modes, namely: (1) synthesis mode, (2) augmentation mode, (3) recovery mode, and (4) imitation mode, can be derived by combining and configuring these three conditions. Extensive experiments on both handwritten and scene text demonstrate that the proposed CTIG-DM is able to produce image samples that simulate real-world complexity and diversity, and thus can boost the performance of existing text recognizers. Besides, CTIG-DM shows its appealing potential in domain adaptation and generating images containing Out-Of-Vocabulary (OOV) words. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Conditional_Text_Image_Generation_With_Diffusion_Models_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Conditional_Text_Image_Generation_With_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Conditional_Text_Image_Generation_With_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
AnchorFormer: Point Cloud Completion From Discriminative Nodes | Zhikai Chen, Fuchen Long, Zhaofan Qiu, Ting Yao, Wengang Zhou, Jiebo Luo, Tao Mei | Point cloud completion aims to recover the completed 3D shape of an object from its partial observation. A common strategy is to encode the observed points to a global feature vector and then predict the complete points through a generative process on this vector. Nevertheless, the results may suffer from the high-quality shape generation problem due to the fact that a global feature vector cannot sufficiently characterize diverse patterns in one object. In this paper, we present a new shape completion architecture, namely AnchorFormer, that innovatively leverages pattern-aware discriminative nodes, i.e., anchors, to dynamically capture regional information of objects. Technically, AnchorFormer models the regional discrimination by learning a set of anchors based on the point features of the input partial observation. Such anchors are scattered to both observed and unobserved locations through estimating particular offsets, and form sparse points together with the down-sampled points of the input observation. To reconstruct the fine-grained object patterns, AnchorFormer further employs a modulation scheme to morph a canonical 2D grid at individual locations of the sparse points into a detailed 3D structure. Extensive experiments on the PCN, ShapeNet-55/34 and KITTI datasets quantitatively and qualitatively demonstrate the efficacy of AnchorFormer over the state-of-the-art point cloud completion approaches. Source code is available at https://github.com/chenzhik/AnchorFormer. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_AnchorFormer_Point_Cloud_Completion_From_Discriminative_Nodes_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_AnchorFormer_Point_Cloud_Completion_From_Discriminative_Nodes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_AnchorFormer_Point_Cloud_Completion_From_Discriminative_Nodes_CVPR_2023_paper.html | CVPR 2023 | null |
Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM | Hengyi Wang, Jingwen Wang, Lourdes Agapito | We present Co-SLAM, a neural RGB-D SLAM system based on a hybrid representation, that performs robust camera tracking and high-fidelity surface reconstruction in real time. Co-SLAM represents the scene as a multi-resolution hash-grid to exploit its high convergence speed and ability to represent high-frequency local features. In addition, Co-SLAM incorporates one-blob encoding, to encourage surface coherence and completion in unobserved areas. This joint parametric-coordinate encoding enables real-time and robust performance by bringing the best of both worlds: fast convergence and surface hole filling. Moreover, our ray sampling strategy allows Co-SLAM to perform global bundle adjustment over all keyframes instead of requiring keyframe selection to maintain a small number of active keyframes as competing neural SLAM approaches do. Experimental results show that Co-SLAM runs at 10-17Hz and achieves state-of-the-art scene reconstruction results, and competitive tracking performance in various datasets and benchmarks (ScanNet, TUM, Replica, Synthetic RGBD). Project page: https://hengyiwang.github.io/projects/CoSLAM | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Co-SLAM_Joint_Coordinate_and_Sparse_Parametric_Encodings_for_Neural_Real-Time_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Co-SLAM_Joint_Coordinate_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Co-SLAM_Joint_Coordinate_and_Sparse_Parametric_Encodings_for_Neural_Real-Time_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Co-SLAM_Joint_Coordinate_and_Sparse_Parametric_Encodings_for_Neural_Real-Time_CVPR_2023_paper.html | CVPR 2023 | null |
SIM: Semantic-Aware Instance Mask Generation for Box-Supervised Instance Segmentation | Ruihuang Li, Chenhang He, Yabin Zhang, Shuai Li, Liyi Chen, Lei Zhang | Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention. Most of the current efforts leverage low-level image features as extra supervision without explicitly exploiting the high-level semantic information of the objects, which will become ineffective when the foreground objects have similar appearances to the background or other objects nearby. We propose a new box-supervised instance segmentation approach by developing a Semantic-aware Instance Mask (SIM) generation paradigm. Instead of heavily relying on local pair-wise affinities among neighboring pixels, we construct a group of category-wise feature centroids as prototypes to identify foreground objects and assign them semantic-level pseudo labels. Considering that the semantic-aware prototypes cannot distinguish different instances of the same semantics, we propose a self-correction mechanism to rectify the falsely activated regions while enhancing the correct ones. Furthermore, to handle the occlusions between objects, we tailor the Copy-Paste operation for the weakly-supervised instance segmentation task to augment challenging training data. Extensive experimental results demonstrate the superiority of our proposed SIM approach over other state-of-the-art methods. The source code: https://github.com/lslrh/SIM. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_SIM_Semantic-Aware_Instance_Mask_Generation_for_Box-Supervised_Instance_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_SIM_Semantic-Aware_Instance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08578 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SIM_Semantic-Aware_Instance_Mask_Generation_for_Box-Supervised_Instance_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SIM_Semantic-Aware_Instance_Mask_Generation_for_Box-Supervised_Instance_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Compression-Aware Video Super-Resolution | Yingwei Wang, Takashi Isobe, Xu Jia, Xin Tao, Huchuan Lu, Yu-Wing Tai | Videos stored on mobile devices or delivered on the Internet are usually in compressed format and are of various unknown compression parameters, but most video super-resolution (VSR) methods often assume ideal inputs resulting in large performance gap between experimental settings and real-world applications. In spite of a few pioneering works being proposed recently to super-resolve the compressed videos, they are not specially designed to deal with videos of various levels of compression. In this paper, we propose a novel and practical compression-aware video super-resolution model, which could adapt its video enhancement process to the estimated compression level. A compression encoder is designed to model compression levels of input frames, and a base VSR model is then conditioned on the implicitly computed representation by inserting compression-aware modules. In addition, we propose to further strengthen the VSR model by taking full advantage of meta data that is embedded naturally in compressed video streams in the procedure of information fusion. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method on compressed VSR benchmarks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Compression-Aware_Video_Super-Resolution_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Compression-Aware_Video_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Compression-Aware_Video_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds | Jinyu Li, Chenxu Luo, Xiaodong Yang | In order to deal with the sparse and unstructured raw point clouds, most LiDAR based 3D object detection research focuses on designing dedicated local point aggregators for fine-grained geometrical modeling. In this paper, we revisit the local point aggregators from the perspective of allocating computational resources. We find that the simplest pillar based models perform surprisingly well considering both accuracy and latency. Additionally, we show that minimal adaptions from the success of 2D object detection, such as enlarging receptive field, significantly boost the performance. Extensive experiments reveal that our pillar based networks with modernized designs in terms of architecture and training render the state-of-the-art performance on two popular benchmarks: Waymo Open Dataset and nuScenes. Our results challenge the common intuition that detailed geometry modeling is essential to achieve high performance for 3D object detection. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_PillarNeXt_Rethinking_Network_Designs_for_3D_Object_Detection_in_LiDAR_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_PillarNeXt_Rethinking_Network_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.04925 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_PillarNeXt_Rethinking_Network_Designs_for_3D_Object_Detection_in_LiDAR_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_PillarNeXt_Rethinking_Network_Designs_for_3D_Object_Detection_in_LiDAR_CVPR_2023_paper.html | CVPR 2023 | null |
Regularization of Polynomial Networks for Image Recognition | Grigorios G. Chrysos, Bohan Wang, Jiankang Deng, Volkan Cevher | Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising performance and improved interpretability but have yet to reach the performance of the powerful DNN baselines. In this work, we aim to close this performance gap. We introduce a class of PNs, which are able to reach the performance of ResNet across a range of six benchmarks. We demonstrate that strong regularization is critical and conduct an extensive study of the exact regularization schemes required to match performance. To further motivate the regularization schemes, we introduce D-PolyNets that achieve a higher-degree of expansion than previously proposed polynomial networks. D-PolyNets are more parameter-efficient while achieving a similar performance as other polynomial networks. We expect that our new models can lead to an understanding of the role of elementwise activation functions (which are no longer required for training PNs). The source code is available at https://github.com/grigorisg9gr/regularized_polynomials. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chrysos_Regularization_of_Polynomial_Networks_for_Image_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chrysos_Regularization_of_Polynomial_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13896 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chrysos_Regularization_of_Polynomial_Networks_for_Image_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chrysos_Regularization_of_Polynomial_Networks_for_Image_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Incremental 3D Semantic Scene Graph Prediction From RGB Sequences | Shun-Cheng Wu, Keisuke Tateno, Nassir Navab, Federico Tombari | 3D semantic scene graphs are a powerful holistic representation as they describe the individual objects and depict the relation between them. They are compact high-level graphs that enable many tasks requiring scene reasoning. In real-world settings, existing 3D estimation methods produce robust predictions that mostly rely on dense inputs. In this work, we propose a real-time framework that incrementally builds a consistent 3D semantic scene graph of a scene given an RGB image sequence. Our method consists of a novel incremental entity estimation pipeline and a scene graph prediction network. The proposed pipeline simultaneously reconstructs a sparse point map and fuses entity estimation from the input images. The proposed network estimates 3D semantic scene graphs with iterative message passing using multi-view and geometric features extracted from the scene entities. Extensive experiments on the 3RScan dataset show the effectiveness of the proposed method in this challenging task, outperforming state-of-the-art approaches. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Incremental_3D_Semantic_Scene_Graph_Prediction_From_RGB_Sequences_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Incremental_3D_Semantic_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2305.02743 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Incremental_3D_Semantic_Scene_Graph_Prediction_From_RGB_Sequences_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Incremental_3D_Semantic_Scene_Graph_Prediction_From_RGB_Sequences_CVPR_2023_paper.html | CVPR 2023 | null |
EfficientViT: Memory Efficient Vision Transformer With Cascaded Group Attention | Xinyu Liu, Houwen Peng, Ningxin Zheng, Yuqing Yang, Han Hu, Yixuan Yuan | Vision transformers have shown great success due to their high model capabilities. However, their remarkable performance is accompanied by heavy computation costs, which makes them unsuitable for real-time applications. In this paper, we propose a family of high-speed vision transformers named EfficientViT. We find that the speed of existing transformer models is commonly bounded by memory inefficient operations, especially the tensor reshaping and element-wise functions in MHSA. Therefore, we design a new building block with a sandwich layout, i.e., using a single memory-bound MHSA between efficient FFN layers, which improves memory efficiency while enhancing channel communication. Moreover, we discover that the attention maps share high similarities across heads, leading to computational redundancy. To address this, we present a cascaded group attention module feeding attention heads with different splits of the full feature, which not only saves computation cost but also improves attention diversity. Comprehensive experiments demonstrate EfficientViT outperforms existing efficient models, striking a good trade-off between speed and accuracy. For instance, our EfficientViT-M5 surpasses MobileNetV3-Large by 1.9% in accuracy, while getting 40.4% and 45.2% higher throughput on Nvidia V100 GPU and Intel Xeon CPU, respectively. Compared to the recent efficient model MobileViT-XXS, EfficientViT-M2 achieves 1.8% superior accuracy, while running 5.8x/3.7x faster on the GPU/CPU, and 7.4x faster when converted to ONNX format. Code and models will be available soon. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_EfficientViT_Memory_Efficient_Vision_Transformer_With_Cascaded_Group_Attention_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_EfficientViT_Memory_Efficient_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.07027 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_EfficientViT_Memory_Efficient_Vision_Transformer_With_Cascaded_Group_Attention_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_EfficientViT_Memory_Efficient_Vision_Transformer_With_Cascaded_Group_Attention_CVPR_2023_paper.html | CVPR 2023 | null |
VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision | Mengyin Liu, Jie Jiang, Chao Zhu, Xu-Cheng Yin | Detecting pedestrians accurately in urban scenes is significant for realistic applications like autonomous driving or video surveillance. However, confusing human-like objects often lead to wrong detections, and small scale or heavily occluded pedestrians are easily missed due to their unusual appearances. To address these challenges, only object regions are inadequate, thus how to fully utilize more explicit and semantic contexts becomes a key problem. Meanwhile, previous context-aware pedestrian detectors either only learn latent contexts with visual clues, or need laborious annotations to obtain explicit and semantic contexts. Therefore, we propose in this paper a novel approach via Vision-Language semantic self-supervision for context-aware Pedestrian Detection (VLPD) to model explicitly semantic contexts without any extra annotations. Firstly, we propose a self-supervised Vision-Language Semantic (VLS) segmentation method, which learns both fully-supervised pedestrian detection and contextual segmentation via self-generated explicit labels of semantic classes by vision-language models. Furthermore, a self-supervised Prototypical Semantic Contrastive (PSC) learning method is proposed to better discriminate pedestrians and other classes, based on more explicit and semantic contexts obtained from VLS. Extensive experiments on popular benchmarks show that our proposed VLPD achieves superior performances over the previous state-of-the-arts, particularly under challenging circumstances like small scale and heavy occlusion. Code is available at https://github.com/lmy98129/VLPD. | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_VLPD_Context-Aware_Pedestrian_Detection_via_Vision-Language_Semantic_Self-Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_VLPD_Context-Aware_Pedestrian_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03135 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_VLPD_Context-Aware_Pedestrian_Detection_via_Vision-Language_Semantic_Self-Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_VLPD_Context-Aware_Pedestrian_Detection_via_Vision-Language_Semantic_Self-Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation | Hanzhi Chen, Fabian Manhardt, Nassir Navab, Benjamin Busam | In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images. Our major contribution is a novel learning scheme which removes the drawbacks of previous works, namely the strong dependency on co-modalities or additional refinement. These have been previously necessary to provide training signals for convergence. We formulate such a scheme as two sub-optimisation problems on texture learning and pose learning. We separately learn to predict realistic texture of objects from real image collections and learn pose estimation from pixel-perfect synthetic data. Combining these two capabilities allows then to synthesise photorealistic novel views to supervise the pose estimator with accurate geometry. To alleviate pose noise and segmentation imperfection present during the texture learning phase, we propose a surfel-based adversarial training loss together with texture regularisation from synthetic data. We demonstrate that the proposed approach significantly outperforms the recent state-of-the-art methods without ground-truth pose annotations and demonstrates substantial generalisation improvements towards unseen scenes. Remarkably, our scheme improves the adopted pose estimators substantially even when initialised with much inferior performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_TexPose_Neural_Texture_Learning_for_Self-Supervised_6D_Object_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_TexPose_Neural_Texture_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.12902 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_TexPose_Neural_Texture_Learning_for_Self-Supervised_6D_Object_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_TexPose_Neural_Texture_Learning_for_Self-Supervised_6D_Object_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
LINe: Out-of-Distribution Detection by Leveraging Important Neurons | Yong Hyun Ahn, Gyeong-Moon Park, Seong Tae Kim | It is important to quantify the uncertainty of input samples, especially in mission-critical domains such as autonomous driving and healthcare, where failure predictions on out-of-distribution (OOD) data are likely to cause big problems. OOD detection problem fundamentally begins in that the model cannot express what it is not aware of. Post-hoc OOD detection approaches are widely explored because they do not require an additional re-training process which might degrade the model's performance and increase the training cost. In this study, from the perspective of neurons in the deep layer of the model representing high-level features, we introduce a new aspect for analyzing the difference in model outputs between in-distribution data and OOD data. We propose a novel method, Leveraging Important Neurons (LINe), for post-hoc Out of distribution detection. Shapley value-based pruning reduces the effects of noisy outputs by selecting only high-contribution neurons for predicting specific classes of input data and masking the rest. Activation clipping fixes all values above a certain threshold into the same value, allowing LINe to treat all the class-specific features equally and just consider the difference between the number of activated feature differences between in-distribution and OOD data. Comprehensive experiments verify the effectiveness of the proposed method by outperforming state-of-the-art post-hoc OOD detection methods on CIFAR-10, CIFAR-100, and ImageNet datasets. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ahn_LINe_Out-of-Distribution_Detection_by_Leveraging_Important_Neurons_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ahn_LINe_Out-of-Distribution_Detection_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13995 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ahn_LINe_Out-of-Distribution_Detection_by_Leveraging_Important_Neurons_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ahn_LINe_Out-of-Distribution_Detection_by_Leveraging_Important_Neurons_CVPR_2023_paper.html | CVPR 2023 | null |
DynIBaR: Neural Dynamic Image-Based Rendering | Zhengqi Li, Qianqian Wang, Forrester Cole, Richard Tucker, Noah Snavely | We address the problem of synthesizing novel views from a monocular video depicting a complex dynamic scene. State-of-the-art methods based on temporally varying Neural Radiance Fields (aka dynamic NeRFs) have shown impressive results on this task. However, for long videos with complex object motions and uncontrolled camera trajectories,these methods can produce blurry or inaccurate renderings, hampering their use in real-world applications. Instead of encoding the entire dynamic scene within the weights of MLPs, we present a new approach that addresses these limitations by adopting a volumetric image-based rendering framework that synthesizes new viewpoints by aggregating features from nearby views in a scene motion-aware manner.Our system retains the advantages of prior methods in its ability to model complex scenes and view-dependent effects,but also enables synthesizing photo-realistic novel views from long videos featuring complex scene dynamics with unconstrained camera trajectories. We demonstrate significant improvements over state-of-the-art methods on dynamic scene datasets, and also apply our approach to in-the-wild videos with challenging camera and object motion, where prior methods fail to produce high-quality renderings | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_DynIBaR_Neural_Dynamic_Image-Based_Rendering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_DynIBaR_Neural_Dynamic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11082 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_DynIBaR_Neural_Dynamic_Image-Based_Rendering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_DynIBaR_Neural_Dynamic_Image-Based_Rendering_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Object Localization: Observing the Background To Discover Objects | Oriane Siméoni, Chloé Sekkat, Gilles Puy, Antonín Vobecký, Éloi Zablocki, Patrick Pérez | Recent advances in self-supervised visual representation learning have paved the way for unsupervised methods tackling tasks such as object discovery and instance segmentation. However, discovering objects in an image with no supervision is a very hard task; what are the desired objects, when to separate them into parts, how many are there, and of what classes? The answers to these questions depend on the tasks and datasets of evaluation. In this work, we take a different approach and propose to look for the background instead. This way, the salient objects emerge as a by-product without any strong assumption on what an object should be. We propose FOUND, a simple model made of a single conv 1x1 initialized with coarse background masks extracted from self-supervised patch-based representations. After fast training and refining these seed masks, the model reaches state-of-the-art results on unsupervised saliency detection and object discovery benchmarks. Moreover, we show that our approach yields good results in the unsupervised semantic segmentation retrieval task. The code to reproduce our results is available at https://github.com/valeoai/FOUND. | https://openaccess.thecvf.com/content/CVPR2023/papers/Simeoni_Unsupervised_Object_Localization_Observing_the_Background_To_Discover_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Simeoni_Unsupervised_Object_Localization_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Simeoni_Unsupervised_Object_Localization_Observing_the_Background_To_Discover_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Simeoni_Unsupervised_Object_Localization_Observing_the_Background_To_Discover_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
Transforming Radiance Field With Lipschitz Network for Photorealistic 3D Scene Stylization | Zicheng Zhang, Yinglu Liu, Congying Han, Yingwei Pan, Tiande Guo, Ting Yao | Recent advances in 3D scene representation and novel view synthesis have witnessed the rise of Neural Radiance Fields (NeRFs). Nevertheless, it is not trivial to exploit NeRF for the photorealistic 3D scene stylization task, which aims to generate visually consistent and photorealistic stylized scenes from novel views. Simply coupling NeRF with photorealistic style transfer (PST) will result in cross-view inconsistency and degradation of stylized view syntheses. Through a thorough analysis, we demonstrate that this non-trivial task can be simplified in a new light: When transforming the appearance representation of a pre-trained NeRF with Lipschitz mapping, the consistency and photorealism across source views will be seamlessly encoded into the syntheses. That motivates us to build a concise and flexible learning framework namely LipRF, which upgrades arbitrary 2D PST methods with Lipschitz mapping tailored for the 3D scene. Technically, LipRF first pre-trains a radiance field to reconstruct the 3D scene, and then emulates the style on each view by 2D PST as the prior to learn a Lipschitz network to stylize the pre-trained appearance. In view of that Lipschitz condition highly impacts the expressivity of the neural network, we devise an adaptive regularization to balance the reconstruction and stylization. A gradual gradient aggregation strategy is further introduced to optimize LipRF in a cost-efficient manner. We conduct extensive experiments to show the high quality and robust performance of LipRF on both photorealistic 3D stylization and object appearance editing. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Transforming_Radiance_Field_With_Lipschitz_Network_for_Photorealistic_3D_Scene_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Transforming_Radiance_Field_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.13232 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Transforming_Radiance_Field_With_Lipschitz_Network_for_Photorealistic_3D_Scene_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Transforming_Radiance_Field_With_Lipschitz_Network_for_Photorealistic_3D_Scene_CVPR_2023_paper.html | CVPR 2023 | null |
BEV-LaneDet: An Efficient 3D Lane Detection Based on Virtual Camera via Key-Points | Ruihao Wang, Jian Qin, Kaiying Li, Yaochen Li, Dong Cao, Jintao Xu | 3D lane detection which plays a crucial role in vehicle routing, has recently been a rapidly developing topic in autonomous driving. Previous works struggle with practicality due to their complicated spatial transformations and inflexible representations of 3D lanes. Faced with the issues, our work proposes an efficient and robust monocular 3D lane detection called BEV-LaneDet with three main contributions. First, we introduce the Virtual Camera that unifies the in/extrinsic parameters of cameras mounted on different vehicles to guarantee the consistency of the spatial relationship among cameras. It can effectively promote the learning procedure due to the unified visual space. We secondly propose a simple but efficient 3D lane representation called Key-Points Representation. This module is more suitable to represent the complicated and diverse 3D lane structures. At last, we present a light-weight and chip-friendly spatial transformation module named Spatial Transformation Pyramid to transform multiscale front-view features into BEV features. Experimental results demonstrate that our work outperforms the state-of-the-art approaches in terms of F-Score, being 10.6% higher on the OpenLane dataset and 4.0% higher on the Apollo 3D synthetic dataset, with a speed of 185 FPS. Code is released at https://github.com/gigo-team/bev_lane_det. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_BEV-LaneDet_An_Efficient_3D_Lane_Detection_Based_on_Virtual_Camera_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_BEV-LaneDet_An_Efficient_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_BEV-LaneDet_An_Efficient_3D_Lane_Detection_Based_on_Virtual_Camera_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_BEV-LaneDet_An_Efficient_3D_Lane_Detection_Based_on_Virtual_Camera_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Supervised 3D Scene Flow Estimation Guided by Superpoints | Yaqi Shen, Le Hui, Jin Xie, Jian Yang | 3D scene flow estimation aims to estimate point-wise motions between two consecutive frames of point clouds. Superpoints, i.e., points with similar geometric features, are usually employed to capture similar motions of local regions in 3D scenes for scene flow estimation. However, in existing methods, superpoints are generated with the offline clustering methods, which cannot characterize local regions with similar motions for complex 3D scenes well, leading to inaccurate scene flow estimation. To this end, we propose an iterative end-to-end superpoint based scene flow estimation framework, where the superpoints can be dynamically updated to guide the point-level flow prediction. Specifically, our framework consists of a flow guided superpoint generation module and a superpoint guided flow refinement module. In our superpoint generation module, we utilize the bidirectional flow information at the previous iteration to obtain the matching points of points and superpoint centers for soft point-to-superpoint association construction, in which the superpoints are generated for pairwise point clouds. With the generated superpoints, we first reconstruct the flow for each point by adaptively aggregating the superpoint-level flow, and then encode the consistency between the reconstructed flow of pairwise point clouds. Finally, we feed the consistency encoding along with the reconstructed flow into GRU to refine point-level flow. Extensive experiments on several different datasets show that our method can achieve promising performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_Self-Supervised_3D_Scene_Flow_Estimation_Guided_by_Superpoints_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2305.02528 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Self-Supervised_3D_Scene_Flow_Estimation_Guided_by_Superpoints_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shen_Self-Supervised_3D_Scene_Flow_Estimation_Guided_by_Superpoints_CVPR_2023_paper.html | CVPR 2023 | null |
DiffCollage: Parallel Generation of Large Content With Diffusion Models | Qinsheng Zhang, Jiaming Song, Xun Huang, Yongxin Chen, Ming-Yu Liu | We present DiffCollage, a compositional diffusion model that can generate large content by leveraging diffusion models trained on generating pieces of the large content. Our approach is based on a factor graph representation where each factor node represents a portion of the content and a variable node represents their overlap. This representation allows us to aggregate intermediate outputs from diffusion models defined on individual nodes to generate content of arbitrary size and shape in parallel without resorting to an autoregressive generation procedure. We apply DiffCollage to various tasks, including infinite image generation, panorama image generation, and long-duration text-guided motion generation. Extensive experimental results with a comparison to strong autoregressive baselines verify the effectiveness of our approach. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_DiffCollage_Parallel_Generation_of_Large_Content_With_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_DiffCollage_Parallel_Generation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17076 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_DiffCollage_Parallel_Generation_of_Large_Content_With_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_DiffCollage_Parallel_Generation_of_Large_Content_With_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Second-Order Plane Adjustment | Lipu Zhou | Planes are generally used in 3D reconstruction for depth sensors, such as RGB-D cameras and LiDARs. This paper focuses on the problem of estimating the optimal planes and sensor poses to minimize the point-to-plane distance. The resulting least-squares problem is referred to as plane adjustment (PA) in the literature, which is the counterpart of bundle adjustment (BA) in visual reconstruction. Iterative methods are adopted to solve these least-squares problems. Typically, Newton's method is rarely used for a large-scale least-squares problem, due to the high computational complexity of the Hessian matrix. Instead, methods using an approximation of the Hessian matrix, such as the Levenberg-Marquardt (LM) method, are generally adopted. This paper adopts the Newton's method to efficiently solve the PA problem. Specifically, given poses, the optimal plane have a close-form solution. Thus we can eliminate planes from the cost function, which significantly reduces the number of variables. Furthermore, as the optimal planes are functions of poses, this method actually ensures that the optimal planes for the current estimated poses can be obtained at each iteration, which benefits the convergence. The difficulty lies in how to efficiently compute the Hessian matrix and the gradient of the resulting cost. This paper provides an efficient solution. Empirical evaluation shows that our algorithm outperforms the state-of-the-art algorithms. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Efficient_Second-Order_Plane_Adjustment_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Efficient_Second-Order_Plane_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11542 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Efficient_Second-Order_Plane_Adjustment_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Efficient_Second-Order_Plane_Adjustment_CVPR_2023_paper.html | CVPR 2023 | null |
Guided Depth Super-Resolution by Deep Anisotropic Diffusion | Nando Metzger, Rodrigo Caye Daudt, Konrad Schindler | Performing super-resolution of a depth image using the guidance from an RGB image is a problem that concerns several fields, such as robotics, medical imaging, and remote sensing. While deep learning methods have achieved good results in this problem, recent work highlighted the value of combining modern methods with more formal frameworks. In this work we propose a novel approach which combines guided anisotropic diffusion with a deep convolutional network and advances the state of the art for guided depth super-resolution. The edge transferring/enhancing properties of the diffusion are boosted by the contextual reasoning capabilities of modern networks, and a strict adjustment step guarantees perfect adherence to the source image. We achieve unprecedented results in three commonly used benchmarks for guided depth super resolution. The performance gain compared to other methods is the largest at larger scales, such as x32 scaling. Code for the proposed method will be made available to promote reproducibility of our results. | https://openaccess.thecvf.com/content/CVPR2023/papers/Metzger_Guided_Depth_Super-Resolution_by_Deep_Anisotropic_Diffusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Metzger_Guided_Depth_Super-Resolution_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.11592 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Metzger_Guided_Depth_Super-Resolution_by_Deep_Anisotropic_Diffusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Metzger_Guided_Depth_Super-Resolution_by_Deep_Anisotropic_Diffusion_CVPR_2023_paper.html | CVPR 2023 | null |
Fresnel Microfacet BRDF: Unification of Polari-Radiometric Surface-Body Reflection | Tomoki Ichikawa, Yoshiki Fukao, Shohei Nobuhara, Ko Nishino | Computer vision applications have heavily relied on the linear combination of Lambertian diffuse and microfacet specular reflection models for representing reflected radiance, which turns out to be physically incompatible and limited in applicability. In this paper, we derive a novel analytical reflectance model, which we refer to as Fresnel Microfacet BRDF model, that is physically accurate and generalizes to various real-world surfaces. Our key idea is to model the Fresnel reflection and transmission of the surface microgeometry with a collection of oriented mirror facets, both for body and surface reflections. We carefully derive the Fresnel reflection and transmission for each microfacet as well as the light transport between them in the subsurface. This physically-grounded modeling also allows us to express the polarimetric behavior of reflected light in addition to its radiometric behavior. That is, FMBRDF unifies not only body and surface reflections but also light reflection in radiometry and polarization and represents them in a single model. Experimental results demonstrate its effectiveness in accuracy, expressive power, image-based estimation, and geometry recovery. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ichikawa_Fresnel_Microfacet_BRDF_Unification_of_Polari-Radiometric_Surface-Body_Reflection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ichikawa_Fresnel_Microfacet_BRDF_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04483 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ichikawa_Fresnel_Microfacet_BRDF_Unification_of_Polari-Radiometric_Surface-Body_Reflection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ichikawa_Fresnel_Microfacet_BRDF_Unification_of_Polari-Radiometric_Surface-Body_Reflection_CVPR_2023_paper.html | CVPR 2023 | null |
A Unified Pyramid Recurrent Network for Video Frame Interpolation | Xin Jin, Longhai Wu, Jie Chen, Youxin Chen, Jayoon Koo, Cheul-hee Hahm | Flow-guided synthesis provides a common framework for frame interpolation, where optical flow is estimated to guide the synthesis of intermediate frames between consecutive inputs. In this paper, we present UPR-Net, a novel Unified Pyramid Recurrent Network for frame interpolation. Cast in a flexible pyramid framework, UPR-Net exploits lightweight recurrent modules for both bi-directional flow estimation and intermediate frame synthesis. At each pyramid level, it leverages estimated bi-directional flow to generate forward-warped representations for frame synthesis; across pyramid levels, it enables iterative refinement for both optical flow and intermediate frame. In particular, we show that our iterative synthesis strategy can significantly improve the robustness of frame interpolation on large motion cases. Despite being extremely lightweight (1.7M parameters), our base version of UPR-Net achieves excellent performance on a large range of benchmarks. Code and trained models of our UPR-Net series are available at: https://github.com/srcn-ivl/UPR-Net. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_A_Unified_Pyramid_Recurrent_Network_for_Video_Frame_Interpolation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.03456 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_A_Unified_Pyramid_Recurrent_Network_for_Video_Frame_Interpolation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_A_Unified_Pyramid_Recurrent_Network_for_Video_Frame_Interpolation_CVPR_2023_paper.html | CVPR 2023 | null |
Mofusion: A Framework for Denoising-Diffusion-Based Motion Synthesis | Rishabh Dabral, Muhammad Hamza Mughal, Vladislav Golyanik, Christian Theobalt | Conventional methods for human motion synthesis have either been deterministic or have had to struggle with the trade-off between motion diversity vs motion quality. In response to these limitations, we introduce MoFusion, i.e., a new denoising-diffusion-based framework for high-quality conditional human motion synthesis that can synthesise long, temporally plausible, and semantically accurate motions based on a range of conditioning contexts (such as music and text). We also present ways to introduce well-known kinematic losses for motion plausibility within the motion-diffusion framework through our scheduled weighting strategy. The learned latent space can be used for several interactive motion-editing applications like in-betweening, seed-conditioning, and text-based editing, thus, providing crucial abilities for virtual-character animation and robotics. Through comprehensive quantitative evaluations and a perceptual user study, we demonstrate the effectiveness of MoFusion compared to the state-of-the-art on established benchmarks in the literature. We urge the reader to watch our supplementary video. The source code will be released. | https://openaccess.thecvf.com/content/CVPR2023/papers/Dabral_Mofusion_A_Framework_for_Denoising-Diffusion-Based_Motion_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dabral_Mofusion_A_Framework_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2212.04495 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dabral_Mofusion_A_Framework_for_Denoising-Diffusion-Based_Motion_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dabral_Mofusion_A_Framework_for_Denoising-Diffusion-Based_Motion_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation | Qitao Zhao, Ce Zheng, Mengyuan Liu, Pichao Wang, Chen Chen | Recently, transformer-based methods have gained significant success in sequential 2D-to-3D lifting human pose estimation. As a pioneering work, PoseFormer captures spatial relations of human joints in each video frame and human dynamics across frames with cascaded transformer layers and has achieved impressive performance. However, in real scenarios, the performance of PoseFormer and its follow-ups is limited by two factors: (a) The length of the input joint sequence; (b) The quality of 2D joint detection. Existing methods typically apply self-attention to all frames of the input sequence, causing a huge computational burden when the frame number is increased to obtain advanced estimation accuracy, and they are not robust to noise naturally brought by the limited capability of 2D joint detectors. In this paper, we propose PoseFormerV2, which exploits a compact representation of lengthy skeleton sequences in the frequency domain to efficiently scale up the receptive field and boost robustness to noisy 2D joint detection. With minimum modifications to PoseFormer, the proposed method effectively fuses features both in the time domain and frequency domain, enjoying a better speed-accuracy trade-off than its precursor. Extensive experiments on two benchmark datasets (i.e., Human3.6M and MPI-INF-3DHP) demonstrate that the proposed approach significantly outperforms the original PoseFormer and other transformer-based variants. Code is released at https://github.com/QitaoZhao/PoseFormerV2. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_PoseFormerV2_Exploring_Frequency_Domain_for_Efficient_and_Robust_3D_Human_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_PoseFormerV2_Exploring_Frequency_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17472 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_PoseFormerV2_Exploring_Frequency_Domain_for_Efficient_and_Robust_3D_Human_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_PoseFormerV2_Exploring_Frequency_Domain_for_Efficient_and_Robust_3D_Human_CVPR_2023_paper.html | CVPR 2023 | null |
Mask3D: Pre-Training 2D Vision Transformers by Learning Masked 3D Priors | Ji Hou, Xiaoliang Dai, Zijian He, Angela Dai, Matthias Nießner | Current popular backbones in computer vision, such as Vision Transformers (ViT) and ResNets are trained to perceive the world from 2D images. However, to more effectively understand 3D structural priors in 2D backbones, we propose Mask3D to leverage existing large-scale RGB-D data in a self-supervised pre-training to embed these 3D priors into 2D learned feature representations. In contrast to traditional 3D contrastive learning paradigms requiring 3D reconstructions or multi-view correspondences, our approach is simple: we formulate a pre-text reconstruction task by masking RGB and depth patches in individual RGB-D frames. We demonstrate the Mask3D is particularly effective in embedding 3D priors into the powerful 2D ViT backbone, enabling improved representation learn- ing for various scene understanding tasks, such as semantic segmentation, instance segmentation and object detection. Experiments show that Mask3D notably outperforms exist- ing self-supervised 3D pre-training approaches on ScanNet, NYUv2, and Cityscapes image understanding tasks, with an improvement of +6.5% mIoU against the state-of-the-art Pri3D on ScanNet image semantic segmentation. | https://openaccess.thecvf.com/content/CVPR2023/papers/Hou_Mask3D_Pre-Training_2D_Vision_Transformers_by_Learning_Masked_3D_Priors_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hou_Mask3D_Pre-Training_2D_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2302.14746 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hou_Mask3D_Pre-Training_2D_Vision_Transformers_by_Learning_Masked_3D_Priors_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hou_Mask3D_Pre-Training_2D_Vision_Transformers_by_Learning_Masked_3D_Priors_CVPR_2023_paper.html | CVPR 2023 | null |
Physically Adversarial Infrared Patches With Learnable Shapes and Locations | Xingxing Wei, Jie Yu, Yao Huang | Owing to the extensive application of infrared object detectors in the safety-critical tasks, it is necessary to evaluate their robustness against adversarial examples in the real world. However, current few physical infrared attacks are complicated to implement in practical application because of their complex transformation from digital world to physical world. To address this issue, in this paper, we propose a physically feasible infrared attack method called "adversarial infrared patches". Considering the imaging mechanism of infrared cameras by capturing objects' thermal radiation, adversarial infrared patches conduct attacks by attaching a patch of thermal insulation materials on the target object to manipulate its thermal distribution. To enhance adversarial attacks, we present a novel aggregation regularization to guide the simultaneous learning for the patch' shape and location on the target object. Thus, a simple gradient-based optimization can be adapted to solve for them. We verify adversarial infrared patches in different object detection tasks with various object detectors. Experimental results show that our method achieves more than 90% Attack Success Rate (ASR) versus the pedestrian detector and vehicle detector in the physical environment, where the objects are captured in different angles, distances, postures, and scenes. More importantly, adversarial infrared patch is easy to implement, and it only needs 0.5 hour to be constructed in the physical world, which verifies its effectiveness and efficiency. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Physically_Adversarial_Infrared_Patches_With_Learnable_Shapes_and_Locations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wei_Physically_Adversarial_Infrared_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13868 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Physically_Adversarial_Infrared_Patches_With_Learnable_Shapes_and_Locations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Physically_Adversarial_Infrared_Patches_With_Learnable_Shapes_and_Locations_CVPR_2023_paper.html | CVPR 2023 | null |
DiffusioNeRF: Regularizing Neural Radiance Fields With Denoising Diffusion Models | Jamie Wynn, Daniyar Turmukhambetov | Under good conditions, Neural Radiance Fields (NeRFs) have shown impressive results on novel view synthesis tasks. NeRFs learn a scene's color and density fields by minimizing the photometric discrepancy between training views and differentiable renderings of the scene. Once trained from a sufficient set of views, NeRFs can generate novel views from arbitrary camera positions. However, the scene geometry and color fields are severely under-constrained, which can lead to artifacts, especially when trained with few input views. To alleviate this problem we learn a prior over scene geometry and color, using a denoising diffusion model (DDM). Our DDM is trained on RGBD patches of the synthetic Hypersim dataset and can be used to predict the gradient of the logarithm of a joint probability distribution of color and depth patches. We show that, these gradients of logarithms of RGBD patch priors serve to regularize geometry and color of a scene. During NeRF training, random RGBD patches are rendered and the estimated gradient of the log-likelihood is backpropagated to the color and density fields. Evaluations on LLFF, the most relevant dataset, show that our learned prior achieves improved quality in the reconstructed geometry and improved generalization to novel views. Evaluations on DTU show improved reconstruction quality among NeRF methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wynn_DiffusioNeRF_Regularizing_Neural_Radiance_Fields_With_Denoising_Diffusion_Models_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2302.12231 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wynn_DiffusioNeRF_Regularizing_Neural_Radiance_Fields_With_Denoising_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wynn_DiffusioNeRF_Regularizing_Neural_Radiance_Fields_With_Denoising_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Exemplar-FreeSOLO: Enhancing Unsupervised Instance Segmentation With Exemplars | Taoseef Ishtiak, Qing En, Yuhong Guo | Instance segmentation seeks to identify and segment each object from images, which often relies on a large number of dense annotations for model training. To alleviate this burden, unsupervised instance segmentation methods have been developed to train class-agnostic instance segmentation models without any annotation. In this paper, we propose a novel unsupervised instance segmentation approach, Exemplar-FreeSOLO, to enhance unsupervised instance segmentation by exploiting a limited number of unannotated and unsegmented exemplars. The proposed framework offers a new perspective on directly perceiving top-down information without annotations. Specifically, Exemplar-FreeSOLO introduces a novel exemplarknowledge abstraction module to acquire beneficial top-down guidance knowledge for instances using unsupervised exemplar object extraction. Moreover, a new exemplar embedding contrastive module is designed to enhance the discriminative capability of the segmentation model by exploiting the contrastive exemplar-based guidance knowledge in the embedding space. To evaluate the proposed ExemplarFreeSOLO, we conduct comprehensive experiments and perform in-depth analyses on three image instance segmentation datasets. The experimental results demonstrate that the proposed approach is effective and outperforms the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ishtiak_Exemplar-FreeSOLO_Enhancing_Unsupervised_Instance_Segmentation_With_Exemplars_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ishtiak_Exemplar-FreeSOLO_Enhancing_Unsupervised_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ishtiak_Exemplar-FreeSOLO_Enhancing_Unsupervised_Instance_Segmentation_With_Exemplars_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ishtiak_Exemplar-FreeSOLO_Enhancing_Unsupervised_Instance_Segmentation_With_Exemplars_CVPR_2023_paper.html | CVPR 2023 | null |
Multimodal Prompting With Missing Modalities for Visual Recognition | Yi-Lun Lee, Yi-Hsuan Tsai, Wei-Chen Chiu, Chen-Yu Lee | In this paper, we tackle two challenges in multimodal learning for visual recognition: 1) when missing-modality occurs either during training or testing in real-world situations; and 2) when the computation resources are not available to finetune on heavy transformer models. To this end, we propose to utilize prompt learning and mitigate the above two challenges together. Specifically, our modality-missing-aware prompts can be plugged into multimodal transformers to handle general missing-modality cases, while only requiring less than 1% learnable parameters compared to training the entire model. We further explore the effect of different prompt configurations and analyze the robustness to missing modality. Extensive experiments are conducted to show the effectiveness of our prompt learning framework that improves the performance under various missing-modality cases, while alleviating the requirement of heavy model re-training. Code is available. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Multimodal_Prompting_With_Missing_Modalities_for_Visual_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_Multimodal_Prompting_With_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03369 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Multimodal_Prompting_With_Missing_Modalities_for_Visual_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Multimodal_Prompting_With_Missing_Modalities_for_Visual_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Edge-Aware Regional Message Passing Controller for Image Forgery Localization | Dong Li, Jiaying Zhu, Menglu Wang, Jiawei Liu, Xueyang Fu, Zheng-Jun Zha | Digital image authenticity has promoted research on image forgery localization. Although deep learning-based methods achieve remarkable progress, most of them usually suffer from severe feature coupling between the forged and authentic regions. In this work, we propose a two-step Edge-aware Regional Message Passing Controlling strategy to address the above issue. Specifically, the first step is to account for fully exploiting the edge information. It consists of two core designs: context-enhanced graph construction and threshold-adaptive differentiable binarization edge algorithm. The former assembles the global semantic information to distinguish the features between the forged and authentic regions, while the latter stands on the output of the former to provide the learnable edges. In the second step, guided by the learnable edges, a region message passing controller is devised to weaken the message passing between the forged and authentic regions. In this way, our ERMPC is capable of explicitly modeling the inconsistency between the forged and authentic regions and enabling it to perform well on refined forged images. Extensive experiments on several challenging benchmarks show that our method is superior to state-of-the-art image forgery localization methods qualitatively and quantitatively. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Edge-Aware_Regional_Message_Passing_Controller_for_Image_Forgery_Localization_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Edge-Aware_Regional_Message_Passing_Controller_for_Image_Forgery_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Edge-Aware_Regional_Message_Passing_Controller_for_Image_Forgery_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Koopman Pooling: Control-Inspired Temporal Dynamics Encoding for Skeleton-Based Action Recognition | Xinghan Wang, Xin Xu, Yadong Mu | Skeleton-based human action recognition is becoming increasingly important in a variety of fields. Most existing works train a CNN or GCN based backbone to extract spatial-temporal features, and use temporal average/max pooling to aggregate the information. However, these pooling methods fail to capture high-order dynamics information. To address the problem, we propose a plug-and-play module called Koopman pooling, which is a parameterized high-order pooling technique based on Koopman theory. The Koopman operator linearizes a non-linear dynamics system, thus providing a way to represent the complex system through the dynamics matrix, which can be used for classification. We also propose an eigenvalue normalization method to encourage the learned dynamics to be non-decaying and stable. Besides, we also show that our Koopman pooling framework can be easily extended to one-shot action recognition when combined with Dynamic Mode Decomposition. The proposed method is evaluated on three benchmark datasets, namely NTU RGB+D 60, 120 and NW-UCLA. Our experiments clearly demonstrate that Koopman pooling significantly improves the performance under both full-dataset and one-shot settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Neural_Koopman_Pooling_Control-Inspired_Temporal_Dynamics_Encoding_for_Skeleton-Based_Action_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Neural_Koopman_Pooling_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Neural_Koopman_Pooling_Control-Inspired_Temporal_Dynamics_Encoding_for_Skeleton-Based_Action_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Neural_Koopman_Pooling_Control-Inspired_Temporal_Dynamics_Encoding_for_Skeleton-Based_Action_CVPR_2023_paper.html | CVPR 2023 | null |
Simulated Annealing in Early Layers Leads to Better Generalization | Amir M. Sarfi, Zahra Karimpour, Muawiz Chaudhary, Nasir M. Khalid, Mirco Ravanelli, Sudhir Mudur, Eugene Belilovsky | Recently, a number of iterative learning methods have been introduced to improve generalization. These typically rely on training for longer periods of time in exchange for improved generalization. LLF (later-layer-forgetting) is a state-of-the-art method in this category. It strengthens learning in early layers by periodically re-initializing the last few layers of the network. Our principal innovation in this work is to use Simulated annealing in EArly Layers (SEAL) of the network in place of re-initialization of later layers. Essentially, later layers go through the normal gradient descent process, while the early layers go through short stints of gradient ascent followed by gradient descent. Extensive experiments on the popular Tiny-ImageNet dataset benchmark and a series of transfer learning and few-shot learning tasks show that we outperform LLF by a significant margin. We further show that, compared to normal training, LLF features, although improving on the target task, degrade the transfer learning performance across all datasets we explored. In comparison, our method outperforms LLF across the same target datasets by a large margin. We also show that the prediction depth of our method is significantly lower than that of LLF and normal training, indicating on average better prediction performance. | https://openaccess.thecvf.com/content/CVPR2023/papers/Sarfi_Simulated_Annealing_in_Early_Layers_Leads_to_Better_Generalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sarfi_Simulated_Annealing_in_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.04858 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sarfi_Simulated_Annealing_in_Early_Layers_Leads_to_Better_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sarfi_Simulated_Annealing_in_Early_Layers_Leads_to_Better_Generalization_CVPR_2023_paper.html | CVPR 2023 | null |
Spatiotemporal Self-Supervised Learning for Point Clouds in the Wild | Yanhao Wu, Tong Zhang, Wei Ke, Sabine Süsstrunk, Mathieu Salzmann | Self-supervised learning (SSL) has the potential to benefit many applications, particularly those where manually annotating data is cumbersome. One such situation is the semantic segmentation of point clouds. In this context, existing methods employ contrastive learning strategies and define positive pairs by performing various augmentation of point clusters in a single frame. As such, these methods do not exploit the temporal nature of LiDAR data. In this paper, we introduce an SSL strategy that leverages positive pairs in both the spatial and temporal domains. To this end, we design (i) a point-to-cluster learning strategy that aggregates spatial information to distinguish objects; and (ii) a cluster-to-cluster learning strategy based on unsupervised object tracking that exploits temporal correspondences. We demonstrate the benefits of our approach via extensive experiments performed by self-supervised training on two large-scale LiDAR datasets and transferring the resulting models to other point cloud segmentation benchmarks. Our results evidence that our method outperforms the state-of-the-art point cloud SSL methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Spatiotemporal_Self-Supervised_Learning_for_Point_Clouds_in_the_Wild_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.16235 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Spatiotemporal_Self-Supervised_Learning_for_Point_Clouds_in_the_Wild_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Spatiotemporal_Self-Supervised_Learning_for_Point_Clouds_in_the_Wild_CVPR_2023_paper.html | CVPR 2023 | null |
Semi-Supervised Learning Made Simple With Self-Supervised Clustering | Enrico Fini, Pietro Astolfi, Karteek Alahari, Xavier Alameda-Pineda, Julien Mairal, Moin Nabi, Elisa Ricci | Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations. However, in many real-world scenarios, labels are partially available, motivating a recent line of work on semi-supervised methods inspired by self-supervised principles. In this paper, we propose a conceptually simple yet empirically powerful approach to turn clustering-based self-supervised methods such as SwAV or DINO into semi-supervised learners. More precisely, we introduce a multi-task framework merging a supervised objective using ground-truth labels and a self-supervised objective relying on clustering assignments with a single cross-entropy loss. This approach may be interpreted as imposing the cluster centroids to be class prototypes. Despite its simplicity, we provide empirical evidence that our approach is highly effective and achieves state-of-the-art performance on CIFAR100 and ImageNet. | https://openaccess.thecvf.com/content/CVPR2023/papers/Fini_Semi-Supervised_Learning_Made_Simple_With_Self-Supervised_Clustering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fini_Semi-Supervised_Learning_Made_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fini_Semi-Supervised_Learning_Made_Simple_With_Self-Supervised_Clustering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fini_Semi-Supervised_Learning_Made_Simple_With_Self-Supervised_Clustering_CVPR_2023_paper.html | CVPR 2023 | null |
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective | Weixia Zhang, Guangtao Zhai, Ying Wei, Xiaokang Yang, Kede Ma | We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information. We develop a general and automated multitask learning scheme for BIQA to exploit auxiliary knowledge from other tasks, in a way that the model parameter sharing and the loss weighting are determined automatically. Specifically, we first describe all candidate label combinations (from multiple tasks) using a textual template, and compute the joint probability from the cosine similarities of the visual-textual embeddings. Predictions of each task can be inferred from the joint distribution, and optimized by carefully designed loss functions. Through comprehensive experiments on learning three tasks - BIQA, scene classification, and distortion type identification, we verify that the proposed BIQA method 1) benefits from the scene classification and distortion type identification tasks and outperforms the state-of-the-art on multiple IQA datasets, 2) is more robust in the group maximum differentiation competition, and 3) realigns the quality annotations from different IQA datasets more effectively. The source code is available at https://github.com/zwx8981/LIQE. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Blind_Image_Quality_Assessment_via_Vision-Language_Correspondence_A_Multitask_Learning_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.14968 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Blind_Image_Quality_Assessment_via_Vision-Language_Correspondence_A_Multitask_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Blind_Image_Quality_Assessment_via_Vision-Language_Correspondence_A_Multitask_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Exploring Data Geometry for Continual Learning | Zhi Gao, Chen Xu, Feng Li, Yunde Jia, Mehrtash Harandi, Yuwei Wu | Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such, the commonly used Euclidean space cannot gracefully capture non-Euclidean geometric structures of data, leading to inferior results. In this paper, we study continual learning from a novel perspective by exploring data geometry for the non-stationary stream of data. Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and prevents forgetting by keeping geometric structures of old data into account. In doing so, we make use of the mixed-curvature space and propose an incremental search scheme, through which the growing geometric structures are encoded. Then, we introduce an angular-regularization loss and a neighbor-robustness loss to train the model, capable of penalizing the change of global geometric structures and local geometric structures. Experiments show that our method achieves better performance than baseline methods designed in Euclidean space. | https://openaccess.thecvf.com/content/CVPR2023/papers/Gao_Exploring_Data_Geometry_for_Continual_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gao_Exploring_Data_Geometry_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03931 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Exploring_Data_Geometry_for_Continual_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gao_Exploring_Data_Geometry_for_Continual_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Frequency-Modulated Point Cloud Rendering With Easy Editing | Yi Zhang, Xiaoyang Huang, Bingbing Ni, Teng Li, Wenjun Zhang | We develop an effective point cloud rendering pipeline for novel view synthesis, which enables high fidelity local detail reconstruction, real-time rendering and user-friendly editing. In the heart of our pipeline is an adaptive frequency modulation module called Adaptive Frequency Net (AFNet), which utilizes a hypernetwork to learn the local texture frequency encoding that is consecutively injected into adaptive frequency activation layers to modulate the implicit radiance signal. This mechanism improves the frequency expressive ability of the network with richer frequency basis support, only at a small computational budget. To further boost performance, a preprocessing module is also proposed for point cloud geometry optimization via point opacity estimation. In contrast to implicit rendering, our pipeline supports high-fidelity interactive editing based on point cloud manipulation. Extensive experimental results on NeRF-Synthetic, ScanNet, DTU and Tanks and Temples datasets demonstrate the superior performances achieved by our method in terms of PSNR, SSIM and LPIPS, in comparison to the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Frequency-Modulated_Point_Cloud_Rendering_With_Easy_Editing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Frequency-Modulated_Point_Cloud_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07596 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Frequency-Modulated_Point_Cloud_Rendering_With_Easy_Editing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Frequency-Modulated_Point_Cloud_Rendering_With_Easy_Editing_CVPR_2023_paper.html | CVPR 2023 | null |
Integral Neural Networks | Kirill Solodskikh, Azim Kurbanov, Ruslan Aydarkhanov, Irina Zhelavskaya, Yury Parfenov, Dehua Song, Stamatios Lefkimmiatis | We introduce a new family of deep neural networks. Instead of the conventional representation of network layers as N-dimensional weight tensors, we use continuous layer representation along the filter and channel dimensions. We call such networks Integral Neural Networks (INNs). In particular, the weights of INNs are represented as continuous functions defined on N-dimensional hypercubes, and the discrete transformations of inputs to the layers are replaced by continuous integration operations, accordingly. During the inference stage, our continuous layers can be converted into the traditional tensor representation via numerical integral quadratures. Such kind of representation allows the discretization of a network to an arbitrary size with various discretization intervals for the integral kernels. This approach can be applied to prune the model directly on the edge device while featuring only a small performance loss at high rates of structural pruning without any fine-tuning. To evaluate the practical benefits of our proposed approach, we have conducted experiments using various neural network architectures for multiple tasks. Our reported results show that the proposed INNs achieve the same performance with their conventional discrete counterparts, while being able to preserve approximately the same performance (2 % accuracy loss for ResNet18 on Imagenet) at a high rate (up to 30%) of structural pruning without fine-tuning, compared to 65 % accuracy loss of the conventional pruning methods under the same conditions. | https://openaccess.thecvf.com/content/CVPR2023/papers/Solodskikh_Integral_Neural_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Solodskikh_Integral_Neural_Networks_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Solodskikh_Integral_Neural_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Solodskikh_Integral_Neural_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Neural Parametric Head Models | Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos, Martin Rünz, Lourdes Agapito, Matthias Nießner | We propose a novel 3D morphable model for complete human heads based on hybrid neural fields. At the core of our model lies a neural parametric representation that disentangles identity and expressions in disjoint latent spaces. To this end, we capture a person's identity in a canonical space as a signed distance field (SDF), and model facial expressions with a neural deformation field. In addition, our representation achieves high-fidelity local detail by introducing an ensemble of local fields centered around facial anchor points. To facilitate generalization, we train our model on a newly-captured dataset of over 3700 head scans from 203 different identities using a custom high-end 3D scanning setup. Our dataset significantly exceeds comparable existing datasets, both with respect to quality and completeness of geometry, averaging around 3.5M mesh faces per scan. Finally, we demonstrate that our approach outperforms state-of-the-art methods in terms of fitting error and reconstruction quality. | https://openaccess.thecvf.com/content/CVPR2023/papers/Giebenhain_Learning_Neural_Parametric_Head_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Giebenhain_Learning_Neural_Parametric_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.02761 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Giebenhain_Learning_Neural_Parametric_Head_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Giebenhain_Learning_Neural_Parametric_Head_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Removing Objects From Neural Radiance Fields | Silvan Weder, Guillermo Garcia-Hernando, Áron Monszpart, Marc Pollefeys, Gabriel J. Brostow, Michael Firman, Sara Vicente | Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved with the current NeRF editing frameworks. We propose a framework to remove objects from a NeRF representation created from an RGB-D sequence. Our NeRF inpainting method leverages recent work in 2D image inpainting and is guided by a user-provided mask. Our algorithm is underpinned by a confidence based view selection procedure. It chooses which of the individual 2D inpainted images to use in the creation of the NeRF, so that the resulting inpainted NeRF is 3D consistent. We show that our method for NeRF editing is effective for synthesizing plausible inpaintings in a multi-view coherent manner, outperforming competing methods. We validate our approach by proposing a new and still-challenging dataset for the task of NeRF inpainting. | https://openaccess.thecvf.com/content/CVPR2023/papers/Weder_Removing_Objects_From_Neural_Radiance_Fields_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2212.11966 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Weder_Removing_Objects_From_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Weder_Removing_Objects_From_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Few-Shot Referring Relationships in Videos | Yogesh Kumar, Anand Mishra | Interpreting visual relationships is a core aspect of comprehensive video understanding. Given a query visual relationship as <subject, predicate, object> and a test video, our objective is to localize the subject and object that are connected via the predicate. Given modern visio-lingual understanding capabilities, solving this problem is achievable, provided that there are large-scale annotated training examples available. However, annotating for every combination of subject, object, and predicate is cumbersome, expensive, and possibly infeasible. Therefore, there is a need for models that can learn to spatially and temporally localize subjects and objects that are connected via an unseen predicate using only a few support set videos sharing the common predicate. We address this challenging problem, referred to as few-shot referring relationships in videos for the first time. To this end, we pose the problem as a minimization of an objective function defined over a T-partite random field. Here, the vertices of the random field correspond to candidate bounding boxes for the subject and object, and T represents the number of frames in the test video. This objective function is composed of frame level and visual relationship similarity potentials. To learn these potentials, we use a relation network that takes query-conditioned translational relationship embedding as inputs and is meta-trained using support set videos in an episodic manner. Further, the objective function is minimized using a belief propagation-based message passing on the random field to obtain the spatiotemporal localization or subject and object trajectories. We perform extensive experiments using two public benchmarks, namely ImageNet-VidVRD and VidOR, and compare the proposed approach with competitive baselines to assess its efficacy. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kumar_Few-Shot_Referring_Relationships_in_Videos_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kumar_Few-Shot_Referring_Relationships_in_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kumar_Few-Shot_Referring_Relationships_in_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
Structural Multiplane Image: Bridging Neural View Synthesis and 3D Reconstruction | Mingfang Zhang, Jinglu Wang, Xiao Li, Yifei Huang, Yoichi Sato, Yan Lu | The Multiplane Image (MPI), containing a set of fronto-parallel RGBA layers, is an effective and efficient representation for view synthesis from sparse inputs. Yet, its fixed structure limits the performance, especially for surfaces imaged at oblique angles. We introduce the Structural MPI (S-MPI), where the plane structure approximates 3D scenes concisely. Conveying RGBA contexts with geometrically-faithful structures, the S-MPI directly bridges view synthesis and 3D reconstruction. It can not only overcome the critical limitations of MPI, i.e., discretization artifacts from sloped surfaces and abuse of redundant layers, and can also acquire planar 3D reconstruction. Despite the intuition and demand of applying S-MPI, great challenges are introduced, e.g., high-fidelity approximation for both RGBA layers and plane poses, multi-view consistency, non-planar regions modeling, and efficient rendering with intersected planes. Accordingly, we propose a transformer-based network based on a segmentation model. It predicts compact and expressive S-MPI layers with their corresponding masks, poses, and RGBA contexts. Non-planar regions are inclusively handled as a special case in our unified framework. Multi-view consistency is ensured by sharing global proxy embeddings, which encode plane-level features covering the complete 3D scenes with aligned coordinates. Intensive experiments show that our method outperforms both previous state-of-the-art MPI-based view synthesis methods and planar reconstruction methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Structural_Multiplane_Image_Bridging_Neural_View_Synthesis_and_3D_Reconstruction_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.05937 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Structural_Multiplane_Image_Bridging_Neural_View_Synthesis_and_3D_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Structural_Multiplane_Image_Bridging_Neural_View_Synthesis_and_3D_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Harmonious Teacher for Cross-Domain Object Detection | Jinhong Deng, Dongli Xu, Wen Li, Lixin Duan | Self-training approaches recently achieved promising results in cross-domain object detection, where people iteratively generate pseudo labels for unlabeled target domain samples with a model, and select high-confidence samples to refine the model. In this work, we reveal that the consistency of classification and localization predictions are crucial to measure the quality of pseudo labels, and propose a new Harmonious Teacher approach to improve the self-training for cross-domain object detection. In particular, we first propose to enhance the quality of pseudo labels by regularizing the consistency of the classification and localization scores when training the detection model. The consistency losses are defined for both labeled source samples and the unlabeled target samples. Then, we further remold the traditional sample selection method by a sample reweighing strategy based on the consistency of classification and localization scores to improve the ranking of predictions. This allows us to fully exploit all instance predictions from the target domain without abandoning valuable hard examples. Without bells and whistles, our method shows superior performance in various cross-domain scenarios compared with the state-of-the-art baselines, which validates the effectiveness of our Harmonious Teacher. Our codes will be available at https://github.com/kinredon/Harmonious-Teacher. | https://openaccess.thecvf.com/content/CVPR2023/papers/Deng_Harmonious_Teacher_for_Cross-Domain_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Deng_Harmonious_Teacher_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Deng_Harmonious_Teacher_for_Cross-Domain_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Deng_Harmonious_Teacher_for_Cross-Domain_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
3D Human Pose Estimation via Intuitive Physics | Shashank Tripathi, Lea Müller, Chun-Hao P. Huang, Omid Taheri, Michael J. Black, Dimitrios Tzionas | Estimating 3D humans from images often produces implausible bodies that lean, float, or penetrate the floor. Such methods ignore the fact that bodies are typically supported by the scene. A physics engine can be used to enforce physical plausibility, but these are not differentiable, rely on unrealistic proxy bodies, and are difficult to integrate into existing optimization and learning frameworks. In contrast, we exploit novel intuitive-physics (IP) terms that can be inferred from a 3D SMPL body interacting with the scene. Inspired by biomechanics, we infer the pressure heatmap on the body, the Center of Pressure (CoP) from the heatmap, and the SMPL body's Center of Mass (CoM). With these, we develop IPMAN, to estimate a 3D body from a color image in a "stable" configuration by encouraging plausible floor contact and overlapping CoP and CoM. Our IP terms are intuitive, easy to implement, fast to compute, differentiable, and can be integrated into existing optimization and regression methods. We evaluate IPMAN on standard datasets and MoYo, a new dataset with synchronized multi-view images, ground-truth 3D bodies with complex poses, body-floor contact, CoM and pressure. IPMAN produces more plausible results than the state of the art, improving accuracy for static poses, while not hurting dynamic ones. Code and data are available for research at https://ipman.is.tue.mpg.de/. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tripathi_3D_Human_Pose_Estimation_via_Intuitive_Physics_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tripathi_3D_Human_Pose_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tripathi_3D_Human_Pose_Estimation_via_Intuitive_Physics_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tripathi_3D_Human_Pose_Estimation_via_Intuitive_Physics_CVPR_2023_paper.html | CVPR 2023 | null |
SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries | Ahmed Imtiaz Humayun, Randall Balestriero, Guha Balakrishnan, Richard G. Baraniuk | Current Deep Network (DN) visualization and interpretability methods rely heavily on data space visualizations such as scoring which dimensions of the data are responsible for their associated prediction or generating new data features or samples that best match a given DN unit or representation. In this paper, we go one step further by developing the first provably exact method for computing the geometry of a DN's mapping -- including its decision boundary -- over a specified region of the data space. By leveraging the theory of Continuous Piecewise Linear (CPWL) spline DNs, SplineCam exactly computes a DN's geometry without resorting to approximations such as sampling or architecture simplification. SplineCam applies to any DN architecture based on CPWL activation nonlinearities, including (leaky) ReLU, absolute value, maxout, and max-pooling and can also be applied to regression DNs such as implicit neural representations. Beyond decision boundary visualization and characterization, SplineCam enables one to compare architectures, measure generalizability, and sample from the decision boundary on or off the data manifold. Project website: https://bit.ly/splinecam | https://openaccess.thecvf.com/content/CVPR2023/papers/Humayun_SplineCam_Exact_Visualization_and_Characterization_of_Deep_Network_Geometry_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Humayun_SplineCam_Exact_Visualization_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.12828 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Humayun_SplineCam_Exact_Visualization_and_Characterization_of_Deep_Network_Geometry_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Humayun_SplineCam_Exact_Visualization_and_Characterization_of_Deep_Network_Geometry_and_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data | Nilesh Kulkarni, Linyi Jin, Justin Johnson, David F. Fouhey | We introduce a method that can learn to predict scene-level implicit functions for 3D reconstruction from posed RGBD data. At test time, our system maps a previously unseen RGB image to a 3D reconstruction of a scene via implicit functions. While implicit functions for 3D reconstruction have often been tied to meshes, we show that we can train one using only a set of posed RGBD images. This setting may help 3D reconstruction unlock the sea of accelerometer+RGBD data that is coming with new phones. Our system, D2-DRDF, can match and sometimes outperform current methods that use mesh supervision and shows better robustness to sparse data. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kulkarni_Learning_To_Predict_Scene-Level_Implicit_3D_From_Posed_RGBD_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kulkarni_Learning_To_Predict_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kulkarni_Learning_To_Predict_Scene-Level_Implicit_3D_From_Posed_RGBD_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kulkarni_Learning_To_Predict_Scene-Level_Implicit_3D_From_Posed_RGBD_Data_CVPR_2023_paper.html | CVPR 2023 | null |
EXCALIBUR: Encouraging and Evaluating Embodied Exploration | Hao Zhu, Raghav Kapoor, So Yeon Min, Winson Han, Jiatai Li, Kaiwen Geng, Graham Neubig, Yonatan Bisk, Aniruddha Kembhavi, Luca Weihs | Experience precedes understanding. Humans constantly explore and learn about their environment out of curiosity, gather information, and update their models of the world. On the other hand, machines are either trained to learn passively from static and fixed datasets, or taught to complete specific goal-conditioned tasks. To encourage the development of exploratory interactive agents, we present the EXCALIBUR benchmark. EXCALIBUR allows agents to explore their environment for long durations and then query their understanding of the physical world via inquiries like: "is the small heavy red bowl made from glass?" or "is there a silver spoon heavier than the egg?". This design encourages agents to perform free-form home exploration without myopia induced by goal conditioning. Once the agents have answered a series of questions, they can renter the scene to refine their knowledge, update their beliefs, and improve their performance on the questions. Our experiments demonstrate the challenges posed by this dataset for the present-day state-of-the-art embodied systems and the headroom afforded to develop new innovative methods. Finally, we present a virtual reality interface that enables humans to seamlessly interact within the simulated world and use it to gather human performance measures. EXCALIBUR affords unique challenges in comparison to present-day benchmarks and represents the next frontier for embodied AI research. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_EXCALIBUR_Encouraging_and_Evaluating_Embodied_Exploration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_EXCALIBUR_Encouraging_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_EXCALIBUR_Encouraging_and_Evaluating_Embodied_Exploration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_EXCALIBUR_Encouraging_and_Evaluating_Embodied_Exploration_CVPR_2023_paper.html | CVPR 2023 | null |
Visual DNA: Representing and Comparing Images Using Distributions of Neuron Activations | Benjamin Ramtoula, Matthew Gadd, Paul Newman, Daniele De Martini | Selecting appropriate datasets is critical in modern computer vision. However, no general-purpose tools exist to evaluate the extent to which two datasets differ. For this, we propose representing images -- and by extension datasets -- using Distributions of Neuron Activations (DNAs). DNAs fit distributions, such as histograms or Gaussians, to activations of neurons in a pre-trained feature extractor through which we pass the image(s) to represent. This extractor is frozen for all datasets, and we rely on its generally expressive power in feature space. By comparing two DNAs, we can evaluate the extent to which two datasets differ with granular control over the comparison attributes of interest, providing the ability to customise the way distances are measured to suit the requirements of the task at hand. Furthermore, DNAs are compact, representing datasets of any size with less than 15 megabytes. We demonstrate the value of DNAs by evaluating their applicability on several tasks, including conditional dataset comparison, synthetic image evaluation, and transfer learning, and across diverse datasets, ranging from synthetic cat images to celebrity faces and urban driving scenes. | https://openaccess.thecvf.com/content/CVPR2023/papers/Ramtoula_Visual_DNA_Representing_and_Comparing_Images_Using_Distributions_of_Neuron_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ramtoula_Visual_DNA_Representing_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.10036 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ramtoula_Visual_DNA_Representing_and_Comparing_Images_Using_Distributions_of_Neuron_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ramtoula_Visual_DNA_Representing_and_Comparing_Images_Using_Distributions_of_Neuron_CVPR_2023_paper.html | CVPR 2023 | null |
Recognizability Embedding Enhancement for Very Low-Resolution Face Recognition and Quality Estimation | Jacky Chen Long Chai, Tiong-Sik Ng, Cheng-Yaw Low, Jaewoo Park, Andrew Beng Jin Teoh | Very low-resolution face recognition (VLRFR) poses unique challenges, such as tiny regions of interest and poor resolution due to extreme standoff distance or wide viewing angle of the acquisition device. In this paper, we study principled approaches to elevate the recognizability of a face in the embedding space instead of the visual quality. We first formulate a robust learning-based face recognizability measure, namely recognizability index (RI), based on two criteria: (i) proximity of each face embedding against the unrecognizable faces cluster center and (ii) closeness of each face embedding against its positive and negative class prototypes. We then devise an index diversion loss to push the hard-to-recognize face embedding with low RI away from unrecognizable faces cluster to boost the RI, which reflects better recognizability. Additionally, a perceptibility-aware attention mechanism is introduced to attend to the salient recognizable face regions, which offers better explanatory and discriminative content for embedding learning. Our proposed model is trained end-to-end and simultaneously serves recognizability-aware embedding learning and face quality estimation. To address VLRFR, extensive evaluations on three challenging low-resolution datasets and face quality assessment demonstrate the superiority of the proposed model over the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Chai_Recognizability_Embedding_Enhancement_for_Very_Low-Resolution_Face_Recognition_and_Quality_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chai_Recognizability_Embedding_Enhancement_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.10066 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chai_Recognizability_Embedding_Enhancement_for_Very_Low-Resolution_Face_Recognition_and_Quality_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chai_Recognizability_Embedding_Enhancement_for_Very_Low-Resolution_Face_Recognition_and_Quality_CVPR_2023_paper.html | CVPR 2023 | null |
Physical-World Optical Adversarial Attacks on 3D Face Recognition | Yanjie Li, Yiquan Li, Xuelong Dai, Songtao Guo, Bin Xiao | The success rate of current adversarial attacks remains low on real-world 3D face recognition tasks because the 3D-printing attacks need to meet the requirement that the generated points should be adjacent to the surface, which limits the adversarial example' searching space. Additionally, they have not considered unpredictable head movements or the non-homogeneous nature of skin reflectance in the real world. To address the real-world challenges, we propose a novel structured-light attack against structured-light-based 3D face recognition. We incorporate the 3D reconstruction process and skin's reflectance in the optimization process to get the end-to-end attack and present 3D transform invariant loss and sensitivity maps to improve robustness. Our attack enables adversarial points to be placed in any position and is resilient to random head movements while maintaining the perturbation unnoticeable. Experiments show that our new method can attack point-cloud-based and depth-image-based 3D face recognition systems with a high success rate, using fewer perturbations than previous physical 3D adversarial attacks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Physical-World_Optical_Adversarial_Attacks_on_3D_Face_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Physical-World_Optical_Adversarial_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2205.13412 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Physical-World_Optical_Adversarial_Attacks_on_3D_Face_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Physical-World_Optical_Adversarial_Attacks_on_3D_Face_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Accelerating Dataset Distillation via Model Augmentation | Lei Zhang, Jie Zhang, Bowen Lei, Subhabrata Mukherjee, Xiang Pan, Bo Zhao, Caiwen Ding, Yao Li, Dongkuan Xu | Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but efficient synthetic training datasets from large ones. Existing DD methods based on gradient matching achieve leading performance; however, they are extremely computationally intensive as they require continuously optimizing a dataset among thousands of randomly initialized models. In this paper, we assume that training the synthetic data with diverse models leads to better generalization performance. Thus we propose two model augmentation techniques, i.e. using early-stage models and parameter perturbation to learn an informative synthetic set with significantly reduced training cost. Extensive experiments demonstrate that our method achieves up to 20x speedup and comparable performance on par with state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Accelerating_Dataset_Distillation_via_Model_Augmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Accelerating_Dataset_Distillation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.06152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Accelerating_Dataset_Distillation_via_Model_Augmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Accelerating_Dataset_Distillation_via_Model_Augmentation_CVPR_2023_paper.html | CVPR 2023 | null |
SE-ORNet: Self-Ensembling Orientation-Aware Network for Unsupervised Point Cloud Shape Correspondence | Jiacheng Deng, Chuxin Wang, Jiahao Lu, Jianfeng He, Tianzhu Zhang, Jiyang Yu, Zhe Zhang | Unsupervised point cloud shape correspondence aims to obtain dense point-to-point correspondences between point clouds without manually annotated pairs. However, humans and some animals have bilateral symmetry and various orientations, which leads to severe mispredictions of symmetrical parts. Besides, point cloud noise disrupts consistent representations for point cloud and thus degrades the shape correspondence accuracy. To address the above issues, we propose a Self-Ensembling ORientation-aware Network termed SE-ORNet. The key of our approach is to exploit an orientation estimation module with a domain adaptive discriminator to align the orientations of point cloud pairs, which significantly alleviates the mispredictions of symmetrical parts. Additionally, we design a self-ensembling framework for unsupervised point cloud shape correspondence. In this framework, the disturbances of point cloud noise are overcome by perturbing the inputs of the student and teacher networks with different data augmentations and constraining the consistency of predictions. Extensive experiments on both human and animal datasets show that our SE-ORNet can surpass state-of-the-art unsupervised point cloud shape correspondence methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Deng_SE-ORNet_Self-Ensembling_Orientation-Aware_Network_for_Unsupervised_Point_Cloud_Shape_Correspondence_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Deng_SE-ORNet_Self-Ensembling_Orientation-Aware_Network_for_Unsupervised_Point_Cloud_Shape_Correspondence_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Deng_SE-ORNet_Self-Ensembling_Orientation-Aware_Network_for_Unsupervised_Point_Cloud_Shape_Correspondence_CVPR_2023_paper.html | CVPR 2023 | null |
Raw Image Reconstruction With Learned Compact Metadata | Yufei Wang, Yi Yu, Wenhan Yang, Lanqing Guo, Lap-Pui Chau, Alex C. Kot, Bihan Wen | While raw images exhibit advantages over sRGB images (e.g. linearity and fine-grained quantization level), they are not widely used by common users due to the large storage requirements. Very recent works propose to compress raw images by designing the sampling masks in the raw image pixel space, leading to suboptimal image representations and redundant metadata. In this paper, we propose a novel framework to learn a compact representation in the latent space serving as the metadata in an end-to-end manner. Furthermore, we propose a novel sRGB-guided context model with the improved entropy estimation strategies, which leads to better reconstruction quality, smaller size of metadata, and faster speed. We illustrate how the proposed raw image compression scheme can adaptively allocate more bits to image regions that are important from a global perspective. The experimental results show that the proposed method can achieve superior raw image reconstruction results using a smaller size of the metadata on both uncompressed sRGB images and JPEG images. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Raw_Image_Reconstruction_With_Learned_Compact_Metadata_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Raw_Image_Reconstruction_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.12995 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Raw_Image_Reconstruction_With_Learned_Compact_Metadata_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Raw_Image_Reconstruction_With_Learned_Compact_Metadata_CVPR_2023_paper.html | CVPR 2023 | null |
Semi-Supervised Video Inpainting With Cycle Consistency Constraints | Zhiliang Wu, Hanyu Xuan, Changchang Sun, Weili Guan, Kang Zhang, Yan Yan | Deep learning-based video inpainting has yielded promising results and gained increasing attention from researchers. Generally, these methods usually assume that the corrupted region masks of each frame are known and easily obtained. However, the annotation of these masks are labor-intensive and expensive, which limits the practical application of current methods. Therefore, we expect to relax this assumption by defining a new semi-supervised inpainting setting, making the networks have the ability of completing the corrupted regions of the whole video using the annotated mask of only one frame. Specifically, in this work, we propose an end-to-end trainable framework consisting of completion network and mask prediction network, which are designed to generate corrupted contents of the current frame using the known mask and decide the regions to be filled of the next frame, respectively. Besides, we introduce a cycle consistency loss to regularize the training parameters of these two networks. In this way, the completion network and the mask prediction network can constrain each other, and hence the overall performance of the trained model can be maximized. Furthermore, due to the natural existence of prior knowledge (e.g., corrupted contents and clear borders), current video inpainting datasets are not suitable in the context of semi-supervised video inpainting. Thus, we create a new dataset by simulating the corrupted video of real-world scenarios. Extensive experimental results are reported to demonstrate the superiority of our model in the video inpainting task. Remarkably, although our model is trained in a semi-supervised manner, it can achieve comparable performance as fully-supervised methods. | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Semi-Supervised_Video_Inpainting_With_Cycle_Consistency_Constraints_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2208.06807 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Semi-Supervised_Video_Inpainting_With_Cycle_Consistency_Constraints_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Semi-Supervised_Video_Inpainting_With_Cycle_Consistency_Constraints_CVPR_2023_paper.html | CVPR 2023 | null |
Frame-Event Alignment and Fusion Network for High Frame Rate Tracking | Jiqing Zhang, Yuanchen Wang, Wenxi Liu, Meng Li, Jinpeng Bai, Baocai Yin, Xin Yang | Most existing RGB-based trackers target low frame rate benchmarks of around 30 frames per second. This setting restricts the tracker's functionality in the real world, especially for fast motion. Event-based cameras as bioinspired sensors provide considerable potential for high frame rate tracking due to their high temporal resolution. However, event-based cameras cannot offer fine-grained texture information like conventional cameras. This unique complementarity motivates us to combine conventional frames and events for high frame rate object tracking under various challenging conditions. In this paper, we propose an end-to-end network consisting of multi-modality alignment and fusion modules to effectively combine meaningful information from both modalities at different measurement rates. The alignment module is responsible for cross-modality and cross-frame-rate alignment between frame and event modalities under the guidance of the moving cues furnished by events. While the fusion module is accountable for emphasizing valuable features and suppressing noise information by the mutual complement between the two modalities. Extensive experiments show that the proposed approach outperforms state-of-the-art trackers by a significant margin in high frame rate tracking. With the FE240hz dataset, our approach achieves high frame rate tracking up to 240Hz. | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Frame-Event_Alignment_and_Fusion_Network_for_High_Frame_Rate_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Frame-Event_Alignment_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Frame-Event_Alignment_and_Fusion_Network_for_High_Frame_Rate_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Frame-Event_Alignment_and_Fusion_Network_for_High_Frame_Rate_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
A Bag-of-Prototypes Representation for Dataset-Level Applications | Weijie Tu, Weijian Deng, Tom Gedeon, Liang Zheng | This work investigates dataset vectorization for two dataset-level tasks: assessing training set suitability and test set difficulty. The former measures how suitable a training set is for a target domain, while the latter studies how challenging a test set is for a learned model. Central of the two tasks is measuring the underlying relationship between datasets. This needs a desirable dataset vectorization scheme, which should preserve as much discriminative dataset information as possible so that the distance between the resulting dataset vectors can reflect dataset-to-dataset similarity. To this end, we propose a bag-of-prototypes (BoP) dataset representation that extends the image level bag consisting of patch descriptors to dataset-level bag consisting of semantic prototypes. Specifically, we develop a codebook consisting of K prototypes clustered from a reference dataset. Given a dataset to be encoded, we quantize each of its image features to a certain prototype in the codebook and obtain a K-dimensional histogram feature. Without assuming access to dataset labels, the BoP representation provides rich characterization of dataset semantic distribution. Further, BoP representations cooperates well with Jensen-Shannon divergence for measuring dataset-to-dataset similarity. Albeit very simple, BoP consistently shows its advantage over existing representations on a series of benchmarks for two dataset-level tasks. | https://openaccess.thecvf.com/content/CVPR2023/papers/Tu_A_Bag-of-Prototypes_Representation_for_Dataset-Level_Applications_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tu_A_Bag-of-Prototypes_Representation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13251 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tu_A_Bag-of-Prototypes_Representation_for_Dataset-Level_Applications_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tu_A_Bag-of-Prototypes_Representation_for_Dataset-Level_Applications_CVPR_2023_paper.html | CVPR 2023 | null |
Level-S$^2$fM: Structure From Motion on Neural Level Set of Implicit Surfaces | Yuxi Xiao, Nan Xue, Tianfu Wu, Gui-Song Xia | This paper presents a neural incremental Structure-from-Motion (SfM) approach, Level-S2fM, which estimates the camera poses and scene geometry from a set of uncalibrated images by learning coordinate MLPs for the implicit surfaces and the radiance fields from the established keypoint correspondences. Our novel formulation poses some new challenges due to inevitable two-view and few-view configurations in the incremental SfM pipeline, which complicates the optimization of coordinate MLPs for volumetric neural rendering with unknown camera poses. Nevertheless, we demonstrate that the strong inductive basis conveying in the 2D correspondences is promising to tackle those challenges by exploiting the relationship between the ray sampling schemes. Based on this, we revisit the pipeline of incremental SfM and renew the key components, including two-view geometry initialization, the camera poses registration, the 3D points triangulation, and Bundle Adjustment, with a fresh perspective based on neural implicit surfaces. By unifying the scene geometry in small MLP networks through coordinate MLPs, our Level-S2fM treats the zero-level set of the implicit surface as an informative top-down regularization to manage the reconstructed 3D points, reject the outliers in correspondences via querying SDF, and refine the estimated geometries by NBA (Neural BA). Not only does our Level-S2fM lead to promising results on camera pose estimation and scene geometry reconstruction, but it also shows a promising way for neural implicit rendering without knowing camera extrinsic beforehand. | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiao_Level-S2fM_Structure_From_Motion_on_Neural_Level_Set_of_Implicit_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xiao_Level-S2fM_Structure_From_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_Level-S2fM_Structure_From_Motion_on_Neural_Level_Set_of_Implicit_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_Level-S2fM_Structure_From_Motion_on_Neural_Level_Set_of_Implicit_CVPR_2023_paper.html | CVPR 2023 | null |
Neuron Structure Modeling for Generalizable Remote Physiological Measurement | Hao Lu, Zitong Yu, Xuesong Niu, Ying-Cong Chen | Remote photoplethysmography (rPPG) technology has drawn increasing attention in recent years. It can extract Blood Volume Pulse (BVP) from facial videos, making many applications like health monitoring and emotional analysis more accessible. However, as the BVP signal is easily affected by environmental changes, existing methods struggle to generalize well for unseen domains. In this paper, we systematically address the domain shift problem in the rPPG measurement task. We show that most domain generalization methods do not work well in this problem, as domain labels are ambiguous in complicated environmental changes. In light of this, we propose a domain-label-free approach called NEuron STructure modeling (NEST). NEST improves the generalization capacity by maximizing the coverage of feature space during training, which reduces the chance for under-optimized feature activation during inference. Besides, NEST can also enrich and enhance domain invariant features across multi-domain. We create and benchmark a large-scale domain generalization protocol for the rPPG measurement task. Extensive experiments show that our approach outperforms the state-of-the-art methods on both cross-dataset and intra-dataset settings. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Neuron_Structure_Modeling_for_Generalizable_Remote_Physiological_Measurement_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.05955 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Neuron_Structure_Modeling_for_Generalizable_Remote_Physiological_Measurement_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_Neuron_Structure_Modeling_for_Generalizable_Remote_Physiological_Measurement_CVPR_2023_paper.html | CVPR 2023 | null |
Shape-Aware Text-Driven Layered Video Editing | Yao-Chih Lee, Ji-Ze Genevieve Jang, Yi-Ting Chen, Elizabeth Qiu, Jia-Bin Huang | Temporal consistency is essential for video editing applications. Existing work on layered representation of videos allows propagating edits consistently to each frame. These methods, however, can only edit object appearance rather than object shape changes due to the limitation of using a fixed UV mapping field for texture atlas. We present a shape-aware, text-driven video editing method to tackle this challenge. To handle shape changes in video editing, we first propagate the deformation field between the input and edited keyframe to all frames. We then leverage a pre-trained text-conditioned diffusion model as guidance for refining shape distortion and completing unseen regions. The experimental results demonstrate that our method can achieve shape-aware consistent video editing and compare favorably with the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Shape-Aware_Text-Driven_Layered_Video_Editing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_Shape-Aware_Text-Driven_Layered_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.13173 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Shape-Aware_Text-Driven_Layered_Video_Editing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Shape-Aware_Text-Driven_Layered_Video_Editing_CVPR_2023_paper.html | CVPR 2023 | null |
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation | Zesen Cheng, Pengchong Qiao, Kehan Li, Siheng Li, Pengxu Wei, Xiangyang Ji, Li Yuan, Chang Liu, Jie Chen | Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted. Although tremendous efforts have been made to recall precise and complete locations for each class, existing methods still commonly suffer from the unsolicited Out-of-Candidate (OC) error predictions that do not belong to the label candidates, which could be avoidable since the contradiction with image-level class tags is easy to be detected. In this paper, we develop a group ranking-based Out-of-Candidate Rectification (OCR) mechanism in a plug-and-play fashion. Firstly, we adaptively split the semantic categories into In-Candidate (IC) and OC groups for each OC pixel according to their prior annotation correlation and posterior prediction correlation. Then, we derive a differentiable rectification loss to force OC pixels to shift to the IC group. Incorporating OCR with seminal baselines (e.g., AffinityNet, SEAM, MCTformer), we can achieve remarkable performance gains on both Pascal VOC (+3.2%, +3.3%, +0.8% mIoU) and MS COCO (+1.0%, +1.3%, +0.5% mIoU) datasets with negligible extra training overhead, which justifies the effectiveness and generality of OCR. | https://openaccess.thecvf.com/content/CVPR2023/papers/Cheng_Out-of-Candidate_Rectification_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cheng_Out-of-Candidate_Rectification_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12268 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_Out-of-Candidate_Rectification_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_Out-of-Candidate_Rectification_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Solving Relaxations of MAP-MRF Problems: Combinatorial In-Face Frank-Wolfe Directions | Vladimir Kolmogorov | We consider the problem of solving LP relaxations of MAP-MRF inference problems, and in particular the method proposed recently in (Swoboda, Kolmogorov 2019; Kolmogorov, Pock 2021). As a key computational subroutine, it uses a variant of the Frank-Wolfe (FW) method to minimize a smooth convex function over a combinatorial polytope. We propose an efficient implementation of this subproutine based on in-face Frank-Wolfe directions, introduced in (Freund et al. 2017) in a different context. More generally, we define an abstract data structure for a combinatorial subproblem that enables in-face FW directions, and describe its specialization for tree-structured MAP-MRF inference subproblems. Experimental results indicate that the resulting method is the current state-of-art LP solver for some classes of problems. Our code is available at pub.ist.ac.at/ vnk/papers/IN-FACE-FW.html. | https://openaccess.thecvf.com/content/CVPR2023/papers/Kolmogorov_Solving_Relaxations_of_MAP-MRF_Problems_Combinatorial_In-Face_Frank-Wolfe_Directions_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2010.09567 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kolmogorov_Solving_Relaxations_of_MAP-MRF_Problems_Combinatorial_In-Face_Frank-Wolfe_Directions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kolmogorov_Solving_Relaxations_of_MAP-MRF_Problems_Combinatorial_In-Face_Frank-Wolfe_Directions_CVPR_2023_paper.html | CVPR 2023 | null |
MEGANE: Morphable Eyeglass and Avatar Network | Junxuan Li, Shunsuke Saito, Tomas Simon, Stephen Lombardi, Hongdong Li, Jason Saragih | Eyeglasses play an important role in the perception of identity. Authentic virtual representations of faces can benefit greatly from their inclusion. However, modeling the geometric and appearance interactions of glasses and the face of virtual representations of humans is challenging. Glasses and faces affect each other's geometry at their contact points, and also induce appearance changes due to light transport. Most existing approaches do not capture these physical interactions since they model eyeglasses and faces independently. Others attempt to resolve interactions as a 2D image synthesis problem and suffer from view and temporal inconsistencies. In this work, we propose a 3D compositional morphable model of eyeglasses that accurately incorporates high-fidelity geometric and photometric interaction effects. To support the large variation in eyeglass topology efficiently, we employ a hybrid representation that combines surface geometry and a volumetric representation. Unlike volumetric approaches, our model naturally retains correspondences across glasses, and hence explicit modification of geometry, such as lens insertion and frame deformation, is greatly simplified. In addition, our model is relightable under point lights and natural illumination, supporting high-fidelity rendering of various frame materials, including translucent plastic and metal within a single morphable model. Importantly, our approach models global light transport effects, such as casting shadows between faces and glasses. Our morphable model for eyeglasses can also be fit to novel glasses via inverse rendering. We compare our approach to state-of-the-art methods and demonstrate significant quality improvements. | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_MEGANE_Morphable_Eyeglass_and_Avatar_Network_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_MEGANE_Morphable_Eyeglass_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.04868 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MEGANE_Morphable_Eyeglass_and_Avatar_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MEGANE_Morphable_Eyeglass_and_Avatar_Network_CVPR_2023_paper.html | CVPR 2023 | null |
Leverage Interactive Affinity for Affordance Learning | Hongchen Luo, Wei Zhai, Jing Zhang, Yang Cao, Dacheng Tao | Perceiving potential "action possibilities" (i.e., affordance) regions of images and learning interactive functionalities of objects from human demonstration is a challenging task due to the diversity of human-object interactions. Prevailing affordance learning algorithms often adopt the label assignment paradigm and presume that there is a unique relationship between functional region and affordance label, yielding poor performance when adapting to unseen environments with large appearance variations. In this paper, we propose to leverage interactive affinity for affordance learning, i.e., extracting interactive affinity from human-object interaction and transferring it to non-interactive objects. Interactive affinity, which represents the contacts between different parts of the human body and local regions of the target object, can provide inherent cues of interconnectivity between humans and objects, thereby reducing the ambiguity of the perceived action possibilities. Specifically, we propose a pose-aided interactive affinity learning framework that exploits human pose to guide the network to learn the interactive affinity from human-object interactions. Particularly, a keypoint heuristic perception (KHP) scheme is devised to exploit the keypoint association of human pose to alleviate the uncertainties due to interaction diversities and contact occlusions. Besides, a contact-driven affordance learning (CAL) dataset is constructed by collecting and labeling over 5,000 images. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. Code and dataset: github.com/lhc1224/PIAL-Net. | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_Leverage_Interactive_Affinity_for_Affordance_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luo_Leverage_Interactive_Affinity_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Leverage_Interactive_Affinity_for_Affordance_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Leverage_Interactive_Affinity_for_Affordance_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Enhancing Multiple Reliability Measures via Nuisance-Extended Information Bottleneck | Jongheon Jeong, Sihyun Yu, Hankook Lee, Jinwoo Shin | In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition (i.e., less generalizable), so that one cannot prevent a model from co-adapting on such (so-called) "shortcut" signals: this makes the model fragile in various distribution shifts. To bypass such failure modes, we consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. This motivates us to extend the standard information bottleneck to additionally model the nuisance information. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training concerning both convolutional- and Transformer-based architectures. Our experimental results show that the proposed scheme improves robustness of learned representations (remarkably without using any domain-specific knowledge), with respect to multiple challenging reliability measures. For example, our model could advance the state-of-the-art on a recent challenging OBJECTS benchmark in novelty detection by 78.4% -> 87.2% in AUROC, while simultaneously enjoying improved corruption, background and (certified) adversarial robustness. Code is available at https://github.com/jh-jeong/nuisance_ib. | https://openaccess.thecvf.com/content/CVPR2023/papers/Jeong_Enhancing_Multiple_Reliability_Measures_via_Nuisance-Extended_Information_Bottleneck_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jeong_Enhancing_Multiple_Reliability_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14096 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jeong_Enhancing_Multiple_Reliability_Measures_via_Nuisance-Extended_Information_Bottleneck_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jeong_Enhancing_Multiple_Reliability_Measures_via_Nuisance-Extended_Information_Bottleneck_CVPR_2023_paper.html | CVPR 2023 | null |
Rethinking the Approximation Error in 3D Surface Fitting for Point Cloud Normal Estimation | Hang Du, Xuejun Yan, Jingjing Wang, Di Xie, Shiliang Pu | Most existing approaches for point cloud normal estimation aim to locally fit a geometric surface and calculate the normal from the fitted surface. Recently, learning-based methods have adopted a routine of predicting point-wise weights to solve the weighted least-squares surface fitting problem. Despite achieving remarkable progress, these methods overlook the approximation error of the fitting problem, resulting in a less accurate fitted surface. In this paper, we first carry out in-depth analysis of the approximation error in the surface fitting problem. Then, in order to bridge the gap between estimated and precise surface normals, we present two basic design principles: 1) applies the Z-direction Transform to rotate local patches for a better surface fitting with a lower approximation error; 2) models the error of the normal estimation as a learnable term. We implement these two principles using deep neural networks, and integrate them with the state-of-the-art (SOTA) normal estimation methods in a plug-and-play manner. Extensive experiments verify our approaches bring benefits to point cloud normal estimation and push the frontier of state-of-the-art performance on both synthetic and real-world datasets. The code is available at https://github.com/hikvision-research/3DVision. | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Rethinking_the_Approximation_Error_in_3D_Surface_Fitting_for_Point_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_Rethinking_the_Approximation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17167 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Rethinking_the_Approximation_Error_in_3D_Surface_Fitting_for_Point_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Rethinking_the_Approximation_Error_in_3D_Surface_Fitting_for_Point_CVPR_2023_paper.html | CVPR 2023 | null |
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