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4DComplete: Non-Rigid Motion Estimation Beyond the Observable Surface | null | Tracking non-rigidly deforming scenes using range sensors has numerous applications including computer vision, AR/VR, and robotics. However, due to occlusions and physical limitations of range sensors, existing methods only handle the visible surface, thus causing discontinuities and incompleteness in the motion field. To this end, we introduce 4DComplete, a novel data-driven approach that estimates the non-rigid motion for the unobserved geometry. 4DComplete takes as input a partial shape and motion observation, extracts 4D time-space embedding, and jointly infers the missing geometry and motion field using a sparse fully-convolutional network. For network training, we constructed a large-scale synthetic dataset called DeformingThings4D, which consists of 1,972 animation sequences spanning 31 different animals or humanoid categories with dense 4D annotation. Experiments show that 4DComplete 1) reconstructs high-resolution volumetric shape and motion field from a partial observation, 2) learns an entangled 4D feature representation that benefits both shape and motion estimation, 3) yields more accurate and natural deformation than classic non-rigid priors such as As-RigidAs-Possible (ARAP) deformation, and 4) generalizes well to unseen objects in real-world sequences. | Yang Li, Hikari Takehara, Takafumi Taketomi, Bo Zheng, Matthias Nießner; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12706-12716 | null | null | 2,021 | iccv |
Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation | null | Full-motion cost volumes play a central role in current state-of-the-art optical flow methods. However, constructed using simple feature correlations, they lack the ability to encapsulate prior, or even non-local, knowledge. This creates artifacts in poorly constrained, ambiguous regions, such as occluded and textureless areas. We propose a separable cost volume module, a drop-in replacement to correlation cost volumes, that uses non-local aggregation layers to exploit global context cues and prior knowledge, in order to disambiguate motions in these regions. Our method leads both the now standard Sintel and KITTI optical flow benchmarks in terms of accuracy, and is also shown to generalize better from synthetic to real data. | Feihu Zhang, Oliver J. Woodford, Victor Adrian Prisacariu, Philip H.S. Torr; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10807-10817 | null | null | 2,021 | iccv |
Detecting Invisible People | null | Monocular object detection and tracking have improved drastically in recent years, but rely on a key assumption: that objects are visible to the camera. Many offline tracking approaches reason about occluded objects post-hoc, by linking together tracklets after the object re-appears, making use of reidentification (ReID). However, online tracking in embodied robotic agents (such as a self-driving vehicle) fundamentally requires object permanence, which is the ability to reason about occluded objects before they re-appear. In this work, we re-purpose tracking benchmarks and propose new metrics for the task of detecting invisible objects, focusing on the illustrative case of people. We demonstrate that current detection and tracking systems perform dramatically worse on this task. We introduce two key innovations to recover much of this performance drop. We treat occluded object detection in temporal sequences as a short-term forecasting challenge, bringing to bear tools from dynamic sequence prediction. Second, we build dynamic models that explicitly reason in 3D from monocular videos without calibration, using observations produced by monocular depth estimators. To our knowledge, ours is the first work to demonstrate the effectiveness of monocular depth estimation for the task of tracking and detecting occluded objects. Our approach strongly improves by 11.4% over the baseline in ablations and by 5.0% over the state-of-the-art in F1 score. | Tarasha Khurana, Achal Dave, Deva Ramanan; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3174-3184 | null | null | 2,021 | iccv |
Transparent Object Tracking Benchmark | null | Visual tracking has achieved considerable progress in recent years. However, current research in the field mainly focuses on tracking of opaque objects, while little attention is paid to transparent object tracking. In this paper, we make the first attempt in exploring this problem by proposing a Transparent Object Tracking Benchmark (TOTB). Specifically, TOTB consists of 225 videos (86K frames) from 15 diverse transparent object categories. Each sequence is manually labeled with axis-aligned bounding boxes. To the best of our knowledge, TOTB is the first benchmark dedicated to transparent object tracking. In order to understand how existing trackers perform and to provide comparison for future research on TOTB, we extensively evaluate 25 state-of-the-art tracking algorithms. The evaluation results exhibit that more efforts are needed to improve transparent object tracking. Besides, we observe some nontrivial findings from the evaluation that are discrepant with some common beliefs in opaque object tracking. For example, we find that deep(er) features are not always good for improvements. Moreover, to encourage future research, we introduce a novel tracker, named TransATOM, which leverages transparency features for tracking and surpasses all 25 evaluated approaches by a large margin. By releasing TOTB, we expect to facilitate future research and application of transparent object tracking in both the academia and industry. The TOTB and evaluation results as well as TransATOM are available at https://hengfan2010.github.io/projects/TOTB/. | Heng Fan, Halady Akhilesha Miththanthaya, Harshit, Siranjiv Ramana Rajan, Xiaoqiong Liu, Zhilin Zou, Yuewei Lin, Haibin Ling; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10734-10743 | null | null | 2,021 | iccv |
CDS: Cross-Domain Self-Supervised Pre-Training | null | We present a two-stage pre-training approach that improves the generalization ability of standard single-domain pre-training. While standard pre-training on a single large dataset (such as ImageNet) can provide a good initial representation for transfer learning tasks, this approach may result in biased representations that impact the success of learning with new multi-domain data (e.g., different artistic styles) via methods like domain adaptation. We propose a novel pre-training approach called Cross-Domain Self-supervision (CDS), which directly employs unlabeled multi-domain data for downstream domain transfer tasks. Our approach uses self-supervision not only within a single domain but also across domains. In-domain instance discrimination is used to learn discriminative features on new data in a domain-adaptive manner, while cross-domain matching is used to learn domain-invariant features. We apply our method as a second pre-training step (after ImageNet pre-training), resulting in a significant target accuracy boost to diverse domain transfer tasks compared to standard one-stage pre-training. | Donghyun Kim, Kuniaki Saito, Tae-Hyun Oh, Bryan A. Plummer, Stan Sclaroff, Kate Saenko; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9123-9132 | null | null | 2,021 | iccv |
Spatial-Temporal Consistency Network for Low-Latency Trajectory Forecasting | null | Trajectory forecasting is a crucial step for autonomous vehicles and mobile robots in order to navigate and interact safely. In order to handle the spatial interactions between objects, graph-based approaches have been proposed. These methods, however, model motion on a frame-to-frame basis and do not provide a strong temporal model. To overcome this limitation, we propose a compact model called Spatial-Temporal Consistency Network (STC-Net). In STC-Net, dilated temporal convolutions are introduced to model long-range dependencies along each trajectory for better temporal modeling while graph convolutions are employed to model the spatial interaction among different trajectories. Furthermore, we propose a feature-wise convolution to generate the predicted trajectories in one pass and refine the forecast trajectories together with the reconstructed observed trajectories. We demonstrate that STC-Net generates spatially and temporally consistent trajectories and outperforms other graph-based methods. Since STC-Net requires only 0.7k parameters and forecasts the future with a latency of only 1.3ms, it advances the state-of-the-art and satisfies the requirements for realistic applications. | Shijie Li, Yanying Zhou, Jinhui Yi, Juergen Gall; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1940-1949 | null | null | 2,021 | iccv |
Incorporating Convolution Designs Into Visual Transformers | null | Motivated by the success of Transformers in natural language processing (NLP) tasks, there exist some attempts (e.g., ViT and DeiT) to apply Transformers to the vision domain. However, pure Transformer architectures often require a large amount of training data or extra supervision to obtain comparable performance with convolutional neural networks (CNNs). To overcome these limitations, we analyze the potential drawbacks when directly borrowing Transformer architectures from NLP. Then we propose a new Convolution-enhanced image Transformer (CeiT) which combines the advantages of CNNs in extracting low-level features, strengthening locality, and the advantages of Transformers in establishing long-range dependencies. Three modifications are made to the original Transformer: 1) instead of the straightforward tokenization from raw input images, we design an Image-to-Tokens (I2T) module that extracts patches from generated low-level features; 2) the feed-froward network in each encoder block is replaced with a Locally-enhanced Feed-Forward (LeFF) layer that promotes the correlation among neighboring tokens in the spatial dimension; 3) a Layer-wise Class token Attention (LCA) is attached at the top of the Transformer that utilizes the multi-level representations. Experimental results on ImageNet and seven downstream tasks show the effectiveness and generalization ability compared with previous Transformers and state-of-the-art CNNs, without requiring a large amount of training data and extra CNN teachers. Besides, CeiT models also demonstrate better convergence with 3xfewer training iterations, which can reduce the training cost significantly. | Kun Yuan, Shaopeng Guo, Ziwei Liu, Aojun Zhou, Fengwei Yu, Wei Wu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 579-588 | null | null | 2,021 | iccv |
CaT: Weakly Supervised Object Detection With Category Transfer | null | A large gap exists between fully-supervised object detection and weakly-supervised object detection. To narrow this gap, some methods consider knowledge transfer from additional fully-supervised dataset. But these methods do not fully exploit discriminative category information in the fully-supervised dataset, thus causing low mAP. To solve this issue, we propose a novel category transfer framework for weakly supervised object detection. The intuition is to fully leverage both visually-discriminative and semantically-correlated category information in the fully-supervised dataset to enhance the object-classification ability of a weakly-supervised detector. To handle overlapping category transfer, we propose a double-supervision mean teacher to gather common category information and bridge the domain gap between two datasets. To handle non-overlapping category transfer, we propose a semantic graph convolutional network to promote the aggregation of semantic features between correlated categories. Experiments are conducted with Pascal VOC 2007 as the target weakly-supervised dataset and COCO as the source fully-supervised dataset. Our category transfer framework achieves 63.5% mAP and 80.3% CorLoc with 5 overlapping categories between two datasets, which outperforms the state-of-the-art methods. Codes are avaliable at https://github.com/MediaBrain-SJTU/CaT. | Tianyue Cao, Lianyu Du, Xiaoyun Zhang, Siheng Chen, Ya Zhang, Yan-Feng Wang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3070-3079 | null | null | 2,021 | iccv |
Watch Only Once: An End-to-End Video Action Detection Framework | null | We propose an end-to-end pipeline, named Watch Once Only (WOO), for video action detection. Current methods either decouple video action detection task into separated stages of actor localization and action classification or train two separated models within one stage. In contrast, our approach solves the actor localization and action classification simultaneously in a unified network. The whole pipeline is significantly simplified by unifying the backbone network and eliminating many hand-crafted components. WOO takes a unified video backbone to simultaneously extract features for actor location and action classification. In addition, we introduce spatial-temporal action embeddings into our framework and design a spatial-temporal fusion module to obtain more discriminative features with richer information, which further boosts the action classification performance. Extensive experiments on AVA and JHMDB datasets show that WOO achieves state-of-the-art performance, while still reduces up to 16.7% GFLOPs compared with existing methods. We hope our work can inspire rethinking the convention of action detection and serve as a solid baseline for end-to-end action detection. Code is available. | Shoufa Chen, Peize Sun, Enze Xie, Chongjian Ge, Jiannan Wu, Lan Ma, Jiajun Shen, Ping Luo; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8178-8187 | null | null | 2,021 | iccv |
Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU | null | The state-of-the-art object detection and image classification methods can perform impressively on more than 9k and 10k classes respectively. In contrast, the number of classes in semantic segmentation datasets is relatively limited. This is not surprising when the restrictions caused by the lack of labelled data and high computation demand for segmentation are considered. In this paper, we propose a novel training methodology to train and scale the existing semantic segmentation models for a large number of semantic classes without increasing the memory overhead. In our approach, we reduce the space complexity of the segmentation model's output from O(C) to O(1), propose an approximation method for ground-truth class probability, and use it to compute cross-entropy loss. The proposed approach is general and can be adopted by any state-of-the-art segmentation model to gracefully scale it for any number of semantic classes with only one GPU. Our approach achieves similar, and in some cases even better mIoU for Cityscapes, Pascal VOC and ADE20k dataset when adopted to DeeplabV3+ model with different backbones. We demonstrate a clear benefit of our approach on a dataset with 1284 classes, bootstrapped from LVIS and COCO annotations, with almost three times better mIoU when compared to DeeplabV3+. Code is available at: https://github.com/shipra25jain/ESSNet. | Shipra Jain, Danda Pani Paudel, Martin Danelljan, Luc Van Gool; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 7426-7436 | null | null | 2,021 | iccv |
AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting | null | Predicting accurate future trajectories of multiple agents is essential for autonomous systems but is challenging due to the complex interaction between agents and the uncertainty in each agent's future behavior. Forecasting multi-agent trajectories requires modeling two key dimensions: (1) time dimension, where we model the influence of past agent states over future states; (2) social dimension, where we model how the state of each agent affects others. Most prior methods model these two dimensions separately, e.g., first using a temporal model to summarize features over time for each agent independently and then modeling the interaction of the summarized features with a social model. This approach is suboptimal since independent feature encoding over either the time or social dimension can result in a loss of information. Instead, we would prefer a method that allows an agent's state at one time to directly affect another agent's state at a future time. To this end, we propose a new Transformer, termed AgentFormer, that simultaneously models the time and social dimensions. The model leverages a sequence representation of multi-agent trajectories by flattening trajectory features across time and agents. Since standard attention operations disregard the agent identity of each element in the sequence, AgentFormer uses a novel agent-aware attention mechanism that preserves agent identities by attending to elements of the same agent differently than elements of other agents. Based on AgentFormer, we propose a stochastic multi-agent trajectory prediction model that can attend to features of any agent at any previous timestep when inferring an agent's future position. The latent intent of all agents is also jointly modeled, allowing the stochasticity in one agent's behavior to affect other agents. Extensive experiments show that our method significantly improves the state of the art on well-established pedestrian and autonomous driving datasets. | Ye Yuan, Xinshuo Weng, Yanglan Ou, Kris M. Kitani; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9813-9823 | null | null | 2,021 | iccv |
DepthTrack: Unveiling the Power of RGBD Tracking | null | RGBD (RGB plus depth) object tracking is gaining momentum as RGBD sensors have become popular in many application fields such as robotics. However, the best RGBD trackers are extensions of the state-of-the-art deep RGB trackers. They are trained with RGB data and the depth channel is used as a sidekick for subtleties such as occlusion detection. This can be explained by the fact that there are no sufficiently large RGBD datasets to 1) train "deep depth trackers" and to 2) challenge RGB trackers with sequences for which the depth cue is essential. This work introduces a new RGBD tracking dataset - DepthTrack - that has twice as many sequences (200) and scene types (40) than in the largest existing dataset, and three times more objects (90). In addition, the average length of the sequences (1473), the number of deformable objects (16) and the number of annotated tracking attributes (15) have been increased. Furthermore, by running the SotA RGB and RGBD trackers on DepthTrack, we propose a new RGBD tracking baseline, namely DeT, which reveals that deep RGBD tracking indeed benefits from genuine training data. The code and dataset is available at https://github.com/xiaozai/DeT. | Song Yan, Jinyu Yang, Jani Käpylä, Feng Zheng, Aleš Leonardis, Joni-Kristian Kämäräinen; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10725-10733 | null | null | 2,021 | iccv |
Cortical Surface Shape Analysis Based on Alexandrov Polyhedra | null | Shape analysis has been playing an important role in early diagnosis and prognosis of neurodegenerative diseases such as Alzheimer's diseases (AD). However, obtaining effective shape representations remains challenging. This paper proposes to use the Alexandrov polyhedra as surface-based shape signatures for cortical morphometry analysis. Given a closed genus-0 surface, its Alexandrov polyhedron is a convex representation that encodes its intrinsic geometry information. We propose to compute the polyhedra via a novel spherical optimal transport (OT) computation. In our experiments, we observe that the Alexandrov polyhedra of cortical surfaces between pathology-confirmed AD and cognitively unimpaired individuals are significantly different. Moreover, we propose a visualization method by comparing local geometry differences across cortical surfaces. We show that the proposed method is effective in pinpointing regional cortical structural changes impacted by AD. | Min Zhang, Yang Guo, Na Lei, Zhou Zhao, Jianfeng Wu, Xiaoyin Xu, Yalin Wang, Xianfeng Gu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14244-14252 | null | null | 2,021 | iccv |
Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation From Images in the Wild | null | This paper addresses the problem of 3D human body shape and pose estimation from an RGB image. This is often an ill-posed problem, since multiple plausible 3D bodies may match the visual evidence present in the input - particularly when the subject is occluded. Thus, it is desirable to estimate a distribution over 3D body shape and pose conditioned on the input image instead of a single 3D reconstruction. We train a deep neural network to estimate a hierarchical matrix-Fisher distribution over relative 3D joint rotation matrices (i.e. body pose), which exploits the human body's kinematic tree structure, as well as a Gaussian distribution over SMPL body shape parameters. To further ensure that the predicted shape and pose distributions match the visual evidence in the input image, we implement a differentiable rejection sampler to impose a reprojection loss between ground-truth 2D joint coordinates and samples from the predicted distributions, projected onto the image plane. We show that our method is competitive with the state-of-the-art in terms of 3D shape and pose metrics on the SSP-3D and 3DPW datasets, while also yielding a structured probability distribution over 3D body shape and pose, with which we can meaningfully quantify prediction uncertainty and sample multiple plausible 3D reconstructions to explain a given input image. | Akash Sengupta, Ignas Budvytis, Roberto Cipolla; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11219-11229 | null | null | 2,021 | iccv |
On Compositions of Transformations in Contrastive Self-Supervised Learning | null | In the image domain, excellent representations can be learned by inducing invariance to content-preserving transformations via noise contrastive learning. In this paper, we generalize contrastive learning to a wider set of transformations, and their compositions, for which either invariance or distinctiveness is sought. We show that it is not immediately obvious how existing methods such as SimCLR can be extended to do so. Instead, we introduce a number of formal requirements that all contrastive formulations must satisfy, and propose a practical construction which satisfies these requirements. In order to maximise the reach of this analysis, we express all components of noise contrastive formulations as the choice of certain generalized transformations of the data (GDTs), including data sampling. We then consider videos as an example of data in which a large variety of transformations are applicable, accounting for the extra modalities -- for which we analyze audio and text -- and the dimension of time. We find that being invariant to certain transformations and distinctive to others is critical to learning effective video representations, improving the state-of-the-art for multiple benchmarks by a large margin, and even surpassing supervised pretraining. | Mandela Patrick, Yuki M. Asano, Polina Kuznetsova, Ruth Fong, João F. Henriques, Geoffrey Zweig, Andrea Vedaldi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9577-9587 | null | null | 2,021 | iccv |
Handwriting Transformers | null | We propose a novel transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement as well as global and local style patterns. The proposed HWT captures the long and short range relationships within the style examples through a self-attention mechanism, thereby encoding both global and local style patterns. Further, the proposed transformer-based HWT comprises an encoder-decoder attention that enables style-content entanglement by gathering the style features of each query character. To the best of our knowledge, we are the first to introduce a transformer-based network for styled handwritten text generation. Our proposed HWT generates realistic styled handwritten text images and outperforms the state-of-the-art demonstrated through extensive qualitative, quantitative and human-based evaluations. The proposed HWT can handle arbitrary length of text and any desired writing style in a few-shot setting. Further, our HWT generalizes well to the challenging scenario where both words and writing style are unseen during training, generating realistic styled handwritten text images. | Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Mubarak Shah; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1086-1094 | null | null | 2,021 | iccv |
Dynamic DETR: End-to-End Object Detection With Dynamic Attention | null | In this paper, we present a novel Dynamic DETR (Detection with Transformers) approach by introducing dynamic attentions into both the encoder and decoder stages of DETR to break its two limitations on small feature resolution and slow training convergence. To address the first limitation, which is due to the quadratic computational complexity of the self-attention module in Transformer encoders, we propose a dynamic encoder to approximate the Transformer encoder's attention mechanism using a convolution-based dynamic encoder with various attention types. Such an encoder can dynamically adjust attentions based on multiple factors such as scale importance, spatial importance, and representation (i.e., feature dimension) importance. To mitigate the second limitation of learning difficulty, we introduce a dynamic decoder by replacing the cross-attention module with a ROI-based dynamic attention in the Transformer decoder. Such a decoder effectively assists Transformers to focus on region of interests from a coarse-to-fine manner and dramatically lowers the learning difficulty, leading to a much faster convergence with fewer training epochs. We conduct a series of experiments to demonstrate our advantages. Our Dynamic DETR significantly reduces the training epochs (by \bf 14x ), yet results in a much better performance (by \bf 3.6 on mAP). Meanwhile, in the standard 1x setup with ResNet-50 backbone, we archive a new state-of-the-art performance that further proves the learning effectiveness of the proposed approach. Code will be released soon. | Xiyang Dai, Yinpeng Chen, Jianwei Yang, Pengchuan Zhang, Lu Yuan, Lei Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2988-2997 | null | null | 2,021 | iccv |
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation | null | Surface normal estimation from a single image is an important task in 3D scene understanding. In this paper, we address two limitations shared by the existing methods: the inability to estimate the aleatoric uncertainty and lack of detail in the prediction. The proposed network estimates the per-pixel surface normal probability distribution. We introduce a new parameterization for the distribution, such that its negative log-likelihood is the angular loss with learned attenuation. The expected value of the angular error is then used as a measure of the aleatoric uncertainty. We also present a novel decoder framework where pixel-wise multi-layer perceptrons are trained on a subset of pixels sampled based on the estimated uncertainty. The proposed uncertainty-guided sampling prevents the bias in training towards large planar surfaces and improves the quality of prediction, especially near object boundaries and on small structures. Experimental results show that the proposed method outperforms the state-of-the-art in ScanNet and NYUv2, and that the estimated uncertainty correlates well with the prediction error. Code is available at https://github.com/baegwangbin/surface_normal_uncertainty. | Gwangbin Bae, Ignas Budvytis, Roberto Cipolla; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13137-13146 | null | null | 2,021 | iccv |
FcaNet: Frequency Channel Attention Networks | null | Attention mechanism, especially channel attention, has gained great success in the computer vision field. Many works focus on how to design efficient channel attention mechanisms while ignoring a fundamental problem, i.e., channel attention mechanism uses scalar to represent channel, which is difficult due to massive information loss. In this work, we start from a different view and regard the channel representation problem as a compression process using frequency analysis. Based on the frequency analysis, we mathematically prove that the conventional global average pooling is a special case of the feature decomposition in the frequency domain. With the proof, we naturally generalize the compression of the channel attention mechanism in the frequency domain and propose our method with multi-spectral channel attention, termed as FcaNet. FcaNet is simple but effective. We can change a few lines of code in the calculation to implement our method within existing channel attention methods. Moreover, the proposed method achieves state-of-the-art results compared with other channel attention methods on image classification, object detection, and instance segmentation tasks. Our method could consistently outperform the baseline SENet, with the same number of parameters and the same computational cost. Our code and models are publicly available at https://github.com/cfzd/FcaNet. | Zequn Qin, Pengyi Zhang, Fei Wu, Xi Li; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 783-792 | null | null | 2,021 | iccv |
ReDAL: Region-Based and Diversity-Aware Active Learning for Point Cloud Semantic Segmentation | null | Despite the success of deep learning on supervised point cloud semantic segmentation, obtaining large-scale point-by-point manual annotations is still a significant challenge. To reduce the huge annotation burden, we propose a Region-based and Diversity-aware Active Learning (ReDAL), a general framework for many deep learning approaches, aiming to automatically select only informative and diverse sub-scene regions for label acquisition. Observing that only a small portion of annotated regions are sufficient for 3D scene understanding with deep learning, we use softmax entropy, color discontinuity, and structural complexity to measure the information of sub-scene regions. A diversity-aware selection algorithm is also developed to avoid redundant annotations resulting from selecting informative but similar regions in a querying batch. Extensive experiments show that our method highly outperforms previous active learning strategies, and we achieve the performance of 90% fully supervised learning, while less than 15% and 5% annotations are required on S3DIS and SemanticKITTI datasets, respectively. | Tsung-Han Wu, Yueh-Cheng Liu, Yu-Kai Huang, Hsin-Ying Lee, Hung-Ting Su, Ping-Chia Huang, Winston H. Hsu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15510-15519 | null | null | 2,021 | iccv |
MUSIQ: Multi-Scale Image Quality Transformer | null | Image quality assessment (IQA) is an important research topic for understanding and improving visual experience. The current state-of-the-art IQA methods are based on convolutional neural networks (CNNs). The performance of CNN-based models is often compromised by the fixed shape constraint in batch training. To accommodate this, the input images are usually resized and cropped to a fixed shape, causing image quality degradation. To address this, we design a multi-scale image quality Transformer (MUSIQ) to process native resolution images with varying sizes and aspect ratios. With a multi-scale image representation, our proposed method can capture image quality at different granularities. Furthermore, a novel hash-based 2D spatial embedding and a scale embedding is proposed to support the positional embedding in the multi-scale representation. Experimental results verify that our method can achieve state-of-the-art performance on multiple large scale IQA datasets such as PaQ-2-PiQ, SPAQ and KonIQ-10k. | Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, Feng Yang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5148-5157 | null | null | 2,021 | iccv |
Explaining in Style: Training a GAN To Explain a Classifier in StyleSpace | null | Image classification models can depend on multiple different semantic attributes of the image. An explanation of the decision of the classifier needs to both discover and visualize these properties. Here we present StylEx, a method for doing this, by training a generative model to specifically explain multiple attributes that underlie classifier decisions. A natural source for such attributes is the StyleSpace of StyleGAN, which is known to generate semantically meaningful dimensions in the image. However, because standard GAN training is not dependent on the classifier, it may not represent those attributes which are important for the classifier decision, and the dimensions of StyleSpace may represent irrelevant attributes. To overcome this, we propose a training procedure for a StyleGAN, which incorporates the classifier model, in order to learn a classifier-specific StyleSpace. Explanatory attributes are then selected from this space. These can be used to visualize the effect of changing multiple attributes per image, thus providing image-specific explanations. We apply StylEx to multiple domains, including animals, leaves, faces and retinal images. For these, we show how an image can be modified in different ways to change its classifier output. Our results show that the method finds attributes that align well with semantic ones, generate meaningful image-specific explanations, and are human-interpretable as measured in user-studies. | Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T. Freeman, Phillip Isola, Amir Globerson, Michal Irani, Inbar Mosseri; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 693-702 | null | null | 2,021 | iccv |
Matching in the Dark: A Dataset for Matching Image Pairs of Low-Light Scenes | null | This paper considers matching images of low-light scenes, aiming to widen the frontier of SfM and visual SLAM applications. Recent image sensors can record the brightness of scenes with more than eight-bit precision, available in their RAW-format image. We are interested in making full use of such high-precision information to match extremely low-light scene images that conventional methods cannot handle. For extreme low-light scenes, even if some of their brightness information exists in the RAW format images' low bits, the standard raw image processing fails to utilize them properly. As was recently shown by Chen et al., CNNs can learn to produce images with a natural appearance from such RAW-format images. To consider if and how well we can utilize such information stored in RAW-format images for image matching, we have created a new dataset named MID (matching in the dark). Using it, we experimentally evaluated combinations of eight image-enhancing methods and eleven image matching methods consisting of classical/neural local descriptors and classical/neural initial point-matching methods. The results show the advantage of using the RAW-format images and the strengths and weaknesses of the above component methods. They also imply there is room for further research. | Wenzheng Song, Masanori Suganuma, Xing Liu, Noriyuki Shimobayashi, Daisuke Maruta, Takayuki Okatani; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6029-6038 | null | null | 2,021 | iccv |
BV-Person: A Large-Scale Dataset for Bird-View Person Re-Identification | null | Person Re-IDentification (ReID) aims at re-identifying persons from non-overlapping cameras. Existing person ReID studies focus on horizontal-view ReID tasks, in which the person images are captured by the cameras from a (nearly) horizontal view. In this work we introduce a new ReID task, bird-view person ReID, which aims at searching for a person in a gallery of horizontal-view images with the query images taken from a bird's-eye view, i.e., an elevated view of an object from above. The task is important because there are a large number of video surveillance cameras capturing persons from such an elevated view at public places. However, it is a challenging task in that the images from the bird view (i) provide limited person appearance information and (ii) have a large discrepancy compared to the persons in the horizontal view. We aim to facilitate the development of person ReID from this line by introducing a large-scale real-world dataset for this task. The proposed dataset, named BV-Person, contains 114k images of 18k identities in which nearly 20k images of 7.4k identities are taken from the bird's-eye view. We further introduce a novel model for this new ReID task. Large-scale experiments are performed to evaluate our model and 11 current state-of-the-art ReID models on BV-Person to establish performance benchmarks from multiple perspectives. The empirical results show that our model consistently and substantially outperforms the state-of-the-arts on all five datasets derived from BV-Person. Our model also achieves state-of-the-art performance on two general ReID datasets. Our code and dataset will be made publicly available. | Cheng Yan, Guansong Pang, Lei Wang, Jile Jiao, Xuetao Feng, Chunhua Shen, Jingjing Li; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10943-10952 | null | null | 2,021 | iccv |
Solving Inefficiency of Self-Supervised Representation Learning | null | Self-supervised learning (especially contrastive learning) has attracted great interest due to its huge potential in learning discriminative representations in an unsupervised manner. Despite the acknowledged successes, existing contrastive learning methods suffer from very low learning efficiency, e.g., taking about ten times more training epochs than supervised learning for comparable recognition accuracy. In this paper, we reveal two contradictory phenomena in contrastive learning that we call under-clustering and over-clustering problems, which are major obstacles to learning efficiency. Under-clustering means that the model cannot efficiently learn to discover the dissimilarity between inter-class samples when the negative sample pairs for contrastive learning are insufficient to differentiate all the actual object classes. Over-clustering implies that the model cannot efficiently learn features from excessive negative sample pairs, forcing the model to over-cluster samples of the same actual classes into different clusters. To simultaneously overcome these two problems, we propose a novel self-supervised learning framework using a truncated triplet loss. Precisely, we employ a triplet loss tending to maximize the relative distance between the positive pair and negative pairs to address the under-clustering problem; and we construct the negative pair by selecting a negative sample deputy from all negative samples to avoid the over-clustering problem, guaranteed by the Bernoulli Distribution model. We extensively evaluate our framework in several large-scale benchmarks (e.g., ImageNet, SYSU-30k, and COCO). The results demonstrate our model's superiority (e.g., the learning efficiency) over the latest state-of-the-art methods by a clear margin. See Codes at: https://github.com/wanggrun/triplet. | Guangrun Wang, Keze Wang, Guangcong Wang, Philip H.S. Torr, Liang Lin; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9505-9515 | null | null | 2,021 | iccv |
Stochastic Scene-Aware Motion Prediction | null | A long-standing goal in computer vision is to capture, model, and realistically synthesize human behavior. Specifically, by learning from data, our goal is to enable virtual humans to navigate within cluttered indoor scenes and naturally interact with objects. Such embodied behavior has applications in virtual reality, computer games, and robotics, while synthesized behavior can be used as a source of training data. This is challenging because real human motion is diverse and adapts to the scene. For example, a person can sit or lie on a sofa in many places and with varying styles. It is necessary to model this diversity when synthesizing virtual humans that realistically perform human-scene interactions. We present a novel data-driven, stochastic motion synthesis method that models different styles of performing a given action with a target object. Our method, called SAMP, for Scene-Aware Motion Prediction, generalizes to target objects of various geometries while enabling the character to navigate in cluttered scenes. To train our method, we collected MoCap data covering various sitting, lying down, walking, and running styles. We demonstrate our method on complex indoor scenes and achieve superior performance compared to existing solutions. Our code and data are available for research at https://samp.is.tue.mpg.de. | Mohamed Hassan, Duygu Ceylan, Ruben Villegas, Jun Saito, Jimei Yang, Yi Zhou, Michael J. Black; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11374-11384 | null | null | 2,021 | iccv |
Point Cloud Augmentation With Weighted Local Transformations | null | Despite the extensive usage of point clouds in 3D vision, relatively limited data are available for training deep neural networks. Although data augmentation is a standard approach to compensate for the scarcity of data, it has been less explored in the point cloud literature. In this paper, we propose a simple and effective augmentation method called PointWOLF for point cloud augmentation. The proposed method produces smoothly varying non-rigid deformations by locally weighted transformations centered at multiple anchor points. The smooth deformations allow diverse and realistic augmentations. Furthermore, in order to minimize the manual efforts to search the optimal hyperparameters for augmentation, we present AugTune, which generates augmented samples of desired difficulties producing targeted confidence scores. Our experiments show that our framework consistently improves the performance for both shape classification and part segmentation tasks. In particular, with PointNet++, PointWOLF achieves the state-of-the-art 89.7 accuracy on shape classification with the real-world ScanObjectNN dataset. The code is available at https://github.com/mlvlab/PointWOLF. | Sihyeon Kim, Sanghyeok Lee, Dasol Hwang, Jaewon Lee, Seong Jae Hwang, Hyunwoo J. Kim; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 548-557 | null | null | 2,021 | iccv |
XVFI: eXtreme Video Frame Interpolation | null | In this paper, we firstly present a dataset (X4K1000FPS) of 4K videos of 1000 fps with the extreme motion to the research community for video frame interpolation (VFI), and propose an extreme VFI network, called XVFI-Net, that first handles the VFI for 4K videos with large motion. The XVFI-Net is based on a recursive multi-scale shared structure that consists of two cascaded modules for bidirectional optical flow learning between two input frames (BiOF-I) and for bidirectional optical flow learning from target to input frames (BiOF-T). The optical flows are stably approximated by a complementary flow reversal (CFR) proposed in BiOF-T module. During inference, the BiOF-I module can start at any scale of input while the BiOF-T module only operates at the original input scale so that the inference can be accelerated while maintaining highly accurate VFI performance. Extensive experimental results show that our XVFI-Net can successfully capture the essential information of objects with extremely large motions and complex textures while the state-of-the-art methods exhibit poor performance. Furthermore, our XVFI-Net framework also performs comparably on the previous lower resolution benchmark dataset, which shows a robustness of our algorithm as well. All source codes, pre-trained models, and proposed X4K1000FPS datasets are publicly available at https://github.com/JihyongOh/XVFI. | Hyeonjun Sim, Jihyong Oh, Munchurl Kim; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14489-14498 | null | null | 2,021 | iccv |
Point Transformer | null | Self-attention networks have revolutionized natural language processing and are making impressive strides in image analysis tasks such as image classification and object detection. Inspired by this success, we investigate the application of self-attention networks to 3D point cloud processing. We design self-attention layers for point clouds and use these to construct self-attention networks for tasks such as semantic scene segmentation, object part segmentation, and object classification. Our Point Transformer design improves upon prior work across domains and tasks. For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70.4% on Area 5, outperforming the strongest prior model by 3.3 absolute percentage points and crossing the 70% mIoU threshold for the first time. | Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip H.S. Torr, Vladlen Koltun; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 16259-16268 | null | null | 2,021 | iccv |
Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation | null | How to make the appearance and motion information interact effectively to accommodate complex scenarios is a fundamental issue in flow-based zero-shot video object segmentation. In this paper, we propose an Attentive Multi-Modality Collaboration Network (AMC-Net) to utilize appearance and motion information uniformly. Specifically, AMC-Net fuses robust information from multi-modality features and promotes their collaboration in two stages. First, we propose a Multi-Modality Co-Attention Gate (MCG) on the bilateral encoder branches, in which a gate function is used to formulate co-attention scores for balancing the contributions of multi-modality features and suppressing the redundant and misleading information. Then, we propose a Motion Correction Module (MCM) with a visual-motion attention mechanism, which is constructed to emphasize the features of foreground objects by incorporating the spatio-temporal correspondence between appearance and motion cues. Extensive experiments on three public challenging benchmark datasets verify that our proposed network performs favorably against existing state-of-the-art methods via training with fewer data. | Shu Yang, Lu Zhang, Jinqing Qi, Huchuan Lu, Shuo Wang, Xiaoxing Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1564-1573 | null | null | 2,021 | iccv |
CrossDet: Crossline Representation for Object Detection | null | Object detection aims to accurately locate and classify objects in an image, which requires precise object representations. Existing methods usually use rectangular anchor boxes or a set of points to represent objects. However, these methods either introduce background noise or miss the continuous appearance information inside the object, and thus cause incorrect detection results. In this paper, we propose a novel anchor-free object detection network, called CrossDet, which uses a set of growing cross lines along horizontal and vertical axes as object representations. An object can be flexibly represented as cross lines in different combinations. It not only can effectively reduce the interference of noise, but also takes into account the continuous object information, which is useful to enhance the discriminability of object features and find the object boundaries. Based on the learned cross lines, we propose a crossline extraction module to adaptively capture features of cross lines. Furthermore, we design a decoupled regression mechanism to regress the localization along the horizontal and vertical directions respectively, which helps to decrease the optimization difficulty because the optimization space is limited to a specific direction. Our method achieves consistently improvement on the PASCAL VOC and MS-COCO datasets. The experiment results demonstrate the effectiveness of our proposed method. | Heqian Qiu, Hongliang Li, Qingbo Wu, Jianhua Cui, Zichen Song, Lanxiao Wang, Minjian Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3195-3204 | null | null | 2,021 | iccv |
E-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks | null | Recently, there has been an increasing number of efforts to introduce models capable of generating natural language explanations (NLEs) for their predictions on vision-language (VL) tasks. Such models are appealing, because they can provide human-friendly and comprehensive explanations. However, there is a lack of comparison between existing methods, which is due to a lack of re-usable evaluation frameworks and a scarcity of datasets. In this work, we introduce e-ViL and e-SNLI-VE. e-ViL is a benchmark for explainable vision-language tasks that establishes a unified evaluation framework and provides the first comprehensive comparison of existing approaches that generate NLEs for VL tasks. It spans four models and three datasets and both automatic metrics and human evaluation are used to assess model-generated explanations. e-SNLI-VE is currently the largest existing VL dataset with NLEs (over 430k instances). We also propose a new model that combines UNITER, which learns joint embeddings of images and text, and GPT-2, a pre-trained language model that is well-suited for text generation. It surpasses the previous state of the art by a large margin across all datasets. Code and data are available here: https://github.com/maximek3/e-ViL. | Maxime Kayser, Oana-Maria Camburu, Leonard Salewski, Cornelius Emde, Virginie Do, Zeynep Akata, Thomas Lukasiewicz; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1244-1254 | null | null | 2,021 | iccv |
Learning Unsupervised Metaformer for Anomaly Detection | null | Anomaly detection (AD) aims to address the task of classification or localization of image anomalies. This paper addresses two pivotal issues of reconstruction-based approaches to AD in images, namely, model adaptation and reconstruction gap. The former generalizes an AD model to tackling a broad range of object categories, while the latter provides useful clues for localizing abnormal regions. At the core of our method is an unsupervised universal model, termed as Metaformer, which leverages both meta-learned model parameters to achieve high model adaptation capability and instance-aware attention to emphasize the focal regions for localizing abnormal regions, i.e., to explore the reconstruction gap at those regions of interest. We justify the effectiveness of our method with SOTA results on the MVTec AD dataset of industrial images and highlight the adaptation flexibility of the universal Metaformer with multi-class and few-shot scenarios. | Jhih-Ciang Wu, Ding-Jie Chen, Chiou-Shann Fuh, Tyng-Luh Liu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4369-4378 | null | null | 2,021 | iccv |
Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs | null | Controllable scene synthesis consists of generating 3D information that satisfy underlying specifications. Thereby, these specifications should be abstract, i.e. allowing easy user interaction, whilst providing enough interface for detailed control. Scene graphs are representations of a scene, composed of objects (nodes) and inter-object relationships (edges), proven to be particularly suited for this task, as they allow for semantic control on the generated content. Previous works tackling this task often rely on synthetic data, and retrieve object meshes, which naturally limits the generation capabilities. To circumvent this issue, we instead propose the first work that directly generates shapes from a scene graph in an end-to-end manner. In addition, we show that the same model supports scene modification, using the respective scene graph as interface. Leveraging Graph Convolutional Networks (GCN) we train a variational Auto-Encoder on top of the object and edge categories, as well as 3D shapes and scene layouts, allowing latter sampling of new scenes and shapes. | Helisa Dhamo, Fabian Manhardt, Nassir Navab, Federico Tombari; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 16352-16361 | null | null | 2,021 | iccv |
Self-Motivated Communication Agent for Real-World Vision-Dialog Navigation | null | Vision-Dialog Navigation (VDN) requires an agent to ask questions and navigate following the human responses to find target objects. Conventional approaches are only allowed to ask questions at predefined locations, which are built upon expensive dialogue annotations, and inconvenience the real-word human-robot communication and cooperation. In this paper, we propose a Self-Motivated Communication Agent (SCoA) that learns whether and what to communicate with human adaptively to acquire instructive information for realizing dialogue annotation-free navigation and enhancing the transferability in real-world unseen environment. Specifically, we introduce a whether-to-ask (WeTA) policy, together with uncertainty of which action to choose, to indicate whether the agent should ask a question. Then, a what-to-ask (WaTA) policy is proposed, in which, along with the oracle's answers, the agent learns to score question candidates so as to pick up the most informative one for navigation, and meanwhile mimic oracle's answering. Thus, the agent can navigate in a self-Q&A manner even in real-world environment where the human assistance is often unavailable. Through joint optimization of communication and navigation in a unified imitation learning and reinforcement learning framework, SCoA asks a question if necessary and obtains a hint for guiding the agent to move towards the target with less communication cost. Experiments on seen and unseen environments demonstrate that SCoA shows not only superior performance over existing baselines without dialog annotations, but also competing results compared with rich dialog annotations based counterparts. | Yi Zhu, Yue Weng, Fengda Zhu, Xiaodan Liang, Qixiang Ye, Yutong Lu, Jianbin Jiao; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1594-1603 | null | null | 2,021 | iccv |
Universal Cross-Domain Retrieval: Generalizing Across Classes and Domains | null | In this work, for the first time, we address the problem of universal cross-domain retrieval, where the test data can belong to classes or domains which are unseen during training. Due to dynamically increasing number of categories and practical constraint of training on every possible domain, which requires large amounts of data, generalizing to both unseen classes and domains is important. Towards that goal, we propose SnMpNet (Semantic Neighbourhood and Mixture Prediction Network), which incorporates two novel losses to account for the unseen classes and domains encountered during testing. Specifically, we introduce a novel Semantic Neighborhood loss to bridge the knowledge gap between seen and unseen classes and ensure that the latent space embedding of the unseen classes is semantically meaningful with respect to its neighboring classes. We also introduce a mix-up based supervision at image-level as well as semantic-level of the data for training with the Mixture Prediction loss, which helps in efficient retrieval when the query belongs to an unseen domain. These losses are incorporated on the SE-ResNet50 backbone to obtain SnMpNet. Extensive experiments on two large-scale datasets, Sketchy Extended and DomainNet, and thorough comparisons with state-of-the-art justify the effectiveness of the proposed model. | Soumava Paul, Titir Dutta, Soma Biswas; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12056-12064 | null | null | 2,021 | iccv |
Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution | null | High quality imaging usually requires bulky and expensive lenses to compensate geometric and chromatic aberrations. This poses high constraints on the optical hash or low cost applications. Although one can utilize algorithmic reconstruction to remove the artifacts of low-end lenses, the degeneration from optical aberrations is spatially varying and the computation has to trade off efficiency for performance. For example, we need to conduct patch-wise optimization or train a large set of local deep neural networks to achieve high reconstruction performance across the whole image. In this paper, we propose a PSF aware plug-and-play deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors, thus leading to a universal and flexible optical aberration correction method. Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters, which largely alleviates the time and memory consumption of model learning. The approach is of high efficiency in both training and testing stages. Extensive results verify the promising applications of our proposed approach for compact low-end cameras. | Xiu Li, Jinli Suo, Weihang Zhang, Xin Yuan, Qionghai Dai; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2613-2621 | null | null | 2,021 | iccv |
Rethinking Coarse-To-Fine Approach in Single Image Deblurring | null | Coarse-to-fine strategies have been extensively used for the architecture design of single image deblurring networks. Conventional methods typically stack sub-networks with multi-scale input images and gradually improve sharpness of images from the bottom sub-network to the top sub-network, yielding inevitably high computational costs. Toward a fast and accurate deblurring network design, we revisit the coarse-to-fine strategy and present a multi-input multi-output U-net (MIMO-UNet). The MIMO-UNet has three distinct features. First, the single encoder of the MIMO-UNet takes multi-scale input images to ease the difficulty of training. Second, the single decoder of the MIMO-UNet outputs multiple deblurred images with different scales to mimic multi-cascaded U-nets using a single U-shaped network. Last, asymmetric feature fusion is introduced to merge multi-scale features in an efficient manner. Extensive experiments on the GoPro and RealBlur datasets demonstrate that the proposed network outperforms the state-of-the-art methods in terms of both accuracy and computational complexity. Source code is available for research purposes at https://github.com/chosj95/MIMO-UNet. | Sung-Jin Cho, Seo-Won Ji, Jun-Pyo Hong, Seung-Won Jung, Sung-Jea Ko; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4641-4650 | null | null | 2,021 | iccv |
DeePSD: Automatic Deep Skinning and Pose Space Deformation for 3D Garment Animation | null | We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. | Hugo Bertiche, Meysam Madadi, Emilio Tylson, Sergio Escalera; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5471-5480 | null | null | 2,021 | iccv |
Variable-Rate Deep Image Compression Through Spatially-Adaptive Feature Transform | null | We propose a versatile deep image compression network based on Spatial Feature Transform (SFT), which takes a source image and a corresponding quality map as inputs and produce a compressed image with variable rates. Our model covers a wide range of compression rates using a single model, which is controlled by arbitrary pixel-wise quality maps. In addition, the proposed framework allows us to perform task-aware image compressions for various tasks, e.g., classification, by efficiently estimating optimized quality maps specific to target tasks for our encoding network. This is even possible with a pretrained network without learning separate models for individual tasks. Our algorithm achieves outstanding rate-distortion trade-off compared to the approaches based on multiple models that are optimized separately for several different target rates. At the same level of compression, the proposed approach successfully improves performance on image classification and text region quality preservation via task-aware quality map estimation without additional model training. The code is available at the project website https://github.com/micmic123/QmapCompression. | Myungseo Song, Jinyoung Choi, Bohyung Han; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2380-2389 | null | null | 2,021 | iccv |
Structured Outdoor Architecture Reconstruction by Exploration and Classification | null | This paper presents an explore-and-classify framework for structured architectural reconstruction from aerial image. Starting from a potentially imperfect building reconstruction by an existing algorithm, our approach 1) explores the space of building models by modifying the reconstruction via heuristic actions; 2) learns to classify the correctness of building models while generating classification labels based on the ground-truth; and 3) repeat. At test time, we iterate exploration and classification, seeking for a result with the best classification score. We evaluate the approach using initial reconstructions by two baselines and two state-of-the-art reconstruction algorithms. Qualitative and quantitative evaluations demonstrate that our approach consistently improves the reconstruction quality from every initial reconstruction. | Fuyang Zhang, Xiang Xu, Nelson Nauata, Yasutaka Furukawa; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12427-12435 | null | null | 2,021 | iccv |
MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction | null | Pedestrian trajectory prediction is challenging due to its uncertain and multimodal nature. While generative adversarial networks can learn a distribution over future trajectories, they tend to predict out-of-distribution samples when the distribution of future trajectories is a mixture of multiple, possibly disconnected modes. To address this issue, we propose a multi-generator model for pedestrian trajectory prediction. Each generator specializes in learning a distribution over trajectories routing towards one of the primary modes in the scene, while a second network learns a categorical distribution over these generators, conditioned on the dynamics and scene input. This architecture allows us to effectively sample from specialized generators and to significantly reduce the out-of-distribution samples compared to single generator methods. | Patrick Dendorfer, Sven Elflein, Laura Leal-Taixé; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13158-13167 | null | null | 2,021 | iccv |
Robust Object Detection via Instance-Level Temporal Cycle Confusion | null | Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications. In this work, we study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object detectors. Inspired by the principle of maximum entropy, we introduce a novel self-supervised task, instance-level temporal cycle confusion (CycConf), which operates on the region features of the object detectors. For each object, the task is to find the most different object proposals in the adjacent frame in a video and then cycle back to itself for self-supervision. CycConf encourages the object detector to explore invariant structures across instances under various motions, which leads to improved model robustness in unseen domains at test time. We observe consistent out-of-domain performance improvements when training object detectors in tandem with self-supervised tasks on various domain adaptation benchmarks with static images (Cityscapes, Foggy Cityscapes, Sim10K) and large-scale video datasets (BDD100K and Waymo open data). The code and models are released at https://xinw.ai/cyc-conf. | Xin Wang, Thomas E. Huang, Benlin Liu, Fisher Yu, Xiaolong Wang, Joseph E. Gonzalez, Trevor Darrell; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9143-9152 | null | null | 2,021 | iccv |
Procedure Planning in Instructional Videos via Contextual Modeling and Model-Based Policy Learning | null | Learning new skills by observing humans' behaviors is an essential capability of AI. In this work, we leverage instructional videos to study humans' decision-making processes, focusing on learning a model to plan goal-directed actions in real-life videos. In contrast to conventional action recognition, goal-directed actions are based on expectations of their outcomes requiring causal knowledge of potential consequences of actions. Thus, integrating the environment structure with goals is critical for solving this task. Previous works learn a single world model will fail to distinguish various tasks, resulting in an ambiguous latent space; planning through it will gradually neglect the desired outcomes since the global information of the future goal degrades quickly as the procedure evolves. We address these limitations with a new formulation of procedure planning and propose novel algorithms to model human behaviors through Bayesian Inference and model-based Imitation Learning. Experiments conducted on real-world instructional videos show that our method can achieve state-of-the-art performance in reaching the indicated goals. Furthermore, the learned contextual information presents interesting features for planning in a latent space. | Jing Bi, Jiebo Luo, Chenliang Xu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15611-15620 | null | null | 2,021 | iccv |
Learning To Know Where To See: A Visibility-Aware Approach for Occluded Person Re-Identification | null | Person re-identification (ReID) has gained an impressive progress in recent years. However, the occlusion is still a common and challenging problem for recent ReID methods. Several mainstream methods utilize extra cues (e.g., human pose information) to distinguish human parts from obstacles to alleviate the occlusion problem. Although achieving inspiring progress, these methods severely rely on the fine-grained extra cues, and are sensitive to the estimation error in the extra cues. In this paper, we show that existing methods may degrade if the extra information is sparse or noisy. Thus we propose a simple yet effective method that is robust to sparse and noisy pose information. This is achieved by discretizing pose information to the visibility label of body parts, so as to suppress the influence of occluded regions. We show in our experiments that leveraging pose information in this way is more effective and robust. Besides, our method can be embedded into most person ReID models easily. Extensive experiments validate the effectiveness of our model on common occluded person ReID datasets. | Jinrui Yang, Jiawei Zhang, Fufu Yu, Xinyang Jiang, Mengdan Zhang, Xing Sun, Ying-Cong Chen, Wei-Shi Zheng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11885-11894 | null | null | 2,021 | iccv |
Point-Set Distances for Learning Representations of 3D Point Clouds | null | Learning an effective representation of 3D point clouds requires a good metric to measure the discrepancy between two 3D point sets, which is non-trivial due to their irregularity. Most of the previous works resort to using the Chamfer discrepancy or Earth Mover's distance, but those metrics are either ineffective in measuring the differences between point clouds or computationally expensive. In this paper, we conduct a systematic study with extensive experiments on distance metrics for 3D point clouds. From this study, we propose to use sliced Wasserstein distance and its variants for learning representations of 3D point clouds. In addition, we introduce a new algorithm to estimate sliced Wasserstein distance that guarantees that the estimated value is close enough to the true one. Experiments show that the sliced Wasserstein distance and its variants allow the neural network to learn a more efficient representation compared to the Chamfer discrepancy. We demonstrate the efficiency of the sliced Wasserstein metric and its variants on several tasks in 3D computer vision including training a point cloud autoencoder, generative modeling, transfer learning, and point cloud registration. | Trung Nguyen, Quang-Hieu Pham, Tam Le, Tung Pham, Nhat Ho, Binh-Son Hua; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10478-10487 | null | null | 2,021 | iccv |
RetrievalFuse: Neural 3D Scene Reconstruction With a Database | null | 3D reconstruction of large scenes is a challenging problem due to the high-complexity nature of the solution space, in particular for generative neural networks. In contrast to traditional generative learned models which encode the full generative process into a neural network and can struggle with maintaining local details at the scene level, we introduce a new method that directly leverages scene geometry from the training database. First, we learn to synthesize an initial estimate for a 3D scene, constructed by retrieving a top-k set of volumetric chunks from the scene database. These candidates are then refined to a final scene generation with an attention-based refinement that can effectively select the most consistent set of geometry from the candidates and combine them together to create an output scene, facilitating transfer of coherent structures and local detail from train scene geometry. We demonstrate our neural scene reconstruction with a database for the tasks of 3D super-resolution and surface reconstruction from sparse point clouds, showing that our approach enables generation of more coherent, accurate 3D scenes, improving on average by over 8% in IoU over state-of-the-art scene reconstruction. | Yawar Siddiqui, Justus Thies, Fangchang Ma, Qi Shan, Matthias Nießner, Angela Dai; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12568-12577 | null | null | 2,021 | iccv |
Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation | null | A key assumption of top-down human pose estimation approaches is their expectation of having a single person/instance present in the input bounding box. This often leads to failures in crowded scenes with occlusions. We propose a novel solution to overcome the limitations of this fundamental assumption. Our Multi-Instance Pose Network (MIPNet) allows for predicting multiple 2D pose instances within a given bounding box. We introduce a Multi-Instance Modulation Block (MIMB) that can adaptively modulate channel-wise feature responses for each instance and is parameter efficient. We demonstrate the efficacy of our approach by evaluating on COCO, CrowdPose, and OCHuman datasets. Specifically, we achieve 70.0 AP on CrowdPose and 42.5 AP on OCHuman test sets, a significant improvement of 2.4 AP and 6.5 AP over the prior art, respectively. When using ground truth bounding boxes for inference, MIPNet achieves an improvement of 0.7 AP on COCO, 0.9 AP on CrowdPose, and 9.1 AP on OCHuman validation sets compared to HRNet. Interestingly, when fewer, high confidence bounding boxes are used, HRNet's performance degrades (by 5 AP) on OCHuman, whereas MIPNet maintains a relatively stable performance (drop of 1 AP) for the same inputs. | Rawal Khirodkar, Visesh Chari, Amit Agrawal, Ambrish Tyagi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3122-3131 | null | null | 2,021 | iccv |
Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matching | null | Satellite multi-view stereo (MVS) imagery is particularly suited for large-scale Earth surface reconstruction. Differing from the perspective camera model (pin-hole model) that is commonly used for close-range and aerial cameras, the cubic rational polynomial camera (RPC) model is the mainstream model for push-broom linear-array satellite cameras. However, the homography warping used in the prevailing learning based MVS methods is only applicable to pin-hole cameras. In order to apply the SOTA learning based MVS technology to the satellite MVS taskfor large-scale Earth surface reconstruction, RPC warping should be considered. In this work, we propose, for the first time, a rigorous RPC warping module. The rational polynomial coefficients are recorded as a tensor, and the RPC warping is formulated as a series of tensor transformations. Based on the RPC warping, we propose the deep learning based satellite MVS (SatMVS) framework for large-scale and wide depth range Earth surface reconstruction. We also introduce a large-scale satellite image dataset consisting of 519 5120x5120 images, which we call the TLC SatMVS dataset. The satellite images were acquired from a three-line camera (TLC) that catches triple-view images simultaneously, forming a valuable supplement to the existing open-source WorldView-3 datasets with single-scanline images. Experiments show that the proposed RPC warping module and the SatMVS framework can achieve a superior reconstruction accuracy compared to the pin-hole fitting method and conventional MVS methods. Code and data are available at https://github.com/WHU-GPCV/SatMVS. | Jian Gao, Jin Liu, Shunping Ji; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6148-6157 | null | null | 2,021 | iccv |
Spatio-Temporal Self-Supervised Representation Learning for 3D Point Clouds | null | To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immerse variations due to camera views, lighting, occlusions, etc. In this paper, we tackle this immanent challenge by introducing a spatio-temporal representation learning (STRL) framework, capable of learning from unlabeled 3D point clouds in a self-supervised fashion. Inspired by how infants learn from visual data in-the-wild, we explore the rich spatio-temporal cues derived from the 3D data. Specifically, STRL takes two temporal-correlated frames from a 3D point cloud sequence as the input, transforms it with spatial data augmentation, and learns the invariant representation self-supervisedly. To corroborate the efficacy of STRL, we conduct extensive experiments on synthetic, indoor, and outdoor datasets. Experimental results demonstrate that, compared with supervised learning methods, the learned self-supervised representation facilitates various models to attain comparable or even better performances while capable of generalizing pre-trained models to downstream tasks, including 3D shape classification, 3D object detection, and 3D semantic segmentation. Moreover, spatio-temporal contextual cues embedded in 3D point clouds significantly improve the learned representations. | Siyuan Huang, Yichen Xie, Song-Chun Zhu, Yixin Zhu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6535-6545 | null | null | 2,021 | iccv |
ARCH++: Animation-Ready Clothed Human Reconstruction Revisited | null | We present ARCH++, an image-based method to reconstruct 3D avatars with arbitrary clothing styles. Our reconstructed avatars are animation-ready and highly realistic, in both the visible regions from input views and the unseen regions. While prior work shows great promise of reconstructing animatable clothed humans with various topologies, we observe that there exist fundamental limitations resulting in sub-optimal reconstruction quality. In this paper, we revisit the major steps of image-based avatar reconstruction and address the limitations with ARCH++. First, we introduce an end-to-end point based geometry encoder to better describe the semantics of the underlying 3D human body, in replacement of previous hand-crafted features. Second, in order to address the occupancy ambiguity caused by topological changes of clothed humans in the canonical pose, we propose a co-supervising framework with cross-space consistency to jointly estimate the occupancy in both the posed and canonical spaces. Last, we use image-to-image translation networks to further refine detailed geometry and texture on the reconstructed surface, which improves the fidelity and consistency across arbitrary viewpoints. In the experiments, we demonstrate improvements over the state of the art on both public benchmarks and user studies in reconstruction quality and realism. | Tong He, Yuanlu Xu, Shunsuke Saito, Stefano Soatto, Tony Tung; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11046-11056 | null | null | 2,021 | iccv |
HighlightMe: Detecting Highlights From Human-Centric Videos | null | We present a domain- and user-preference-agnostic approach to detect highlightable excerpts from human-centric videos. Our method works on the graph-based representation of multiple observable human-centric modalities in the videos, such as poses and faces. We use an autoencoder network equipped with spatial-temporal graph convolutions to detect human activities and interactions based on these modalities. We train our network to map the activity- and interaction-based latent structural representations of the different modalities to per-frame highlight scores based on the representativeness of the frames. We use these scores to compute which frames to highlight and stitch contiguous frames to produce the excerpts. We train our network on the large-scale AVA-Kinetics action dataset and evaluate it on four benchmark video highlight datasets: DSH, TVSum, PHD^2, and SumMe. We observe a 4-12% improvement in the mean average precision of matching the human-annotated highlights over state-of-the-art methods in these datasets, without requiring any user-provided preferences or dataset-specific fine-tuning. | Uttaran Bhattacharya, Gang Wu, Stefano Petrangeli, Viswanathan Swaminathan, Dinesh Manocha; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8157-8167 | null | null | 2,021 | iccv |
Semantically Coherent Out-of-Distribution Detection | null | Current out-of-distribution (OOD) detection benchmarks are commonly built by defining one dataset as in-distribution (ID) and all others as OOD. However, these benchmarks unfortunately introduce some unwanted and impractical goals, e.g., to perfectly distinguish CIFAR dogs from ImageNet dogs, even though they have the same semantics and negligible covariate shifts. These unrealistic goals will result in an extremely narrow range of model capabilities, greatly limiting their use in real applications. To overcome these drawbacks, we re-design the benchmarks and propose the semantically coherent out-of-distribution detection (SC-OOD). On the SC-OOD benchmarks, existing methods suffer from large performance degradation, suggesting that they are extremely sensitive to low-level discrepancy between data sources while ignoring their inherent semantics. To develop an effective SC-OOD detection approach, we leverage an external un- labeled set and design a concise framework featured by unsupervised dual grouping (UDG) for the joint modeling of ID and OOD data. The proposed UDG can not only enrich the semantic knowledge of the model by exploiting unlabeled data in an unsupervised manner but also distinguish ID/OOD samples to enhance ID classification and OOD detection tasks simultaneously. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on SC-OOD benchmarks. Code and benchmarks are provided on our project page: https://jingkang50.github.io/projects/scood. | Jingkang Yang, Haoqi Wang, Litong Feng, Xiaopeng Yan, Huabin Zheng, Wayne Zhang, Ziwei Liu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8301-8309 | null | null | 2,021 | iccv |
A New Journey From SDRTV to HDRTV | null | Nowadays modern displays are capable to render video content with high dynamic range (HDR) and wide color gamut (WCG). However, most available resources are still in standard dynamic range (SDR). Therefore, there is an urgent demand to transform existing SDR-TV contents into their HDR-TV versions. In this paper, we conduct an analysis of SDRTV-to-HDRTV task by modeling the formation of SDRTV/HDRTV content. Base on the analysis, we propose a three-step solution pipeline including adaptive global color mapping, local enhancement and highlight generation. Moreover, the above analysis inspires us to present a lightweight network that utilizes global statistics as guidance to conduct image-adaptive color mapping. In addition, we construct a dataset using HDR videos in HDR10 standard, named HDRTV1K, and select five metrics to evaluate the results of SDRTV-to-HDRTV algorithms. Furthermore, our final results achieve state-of-the-art performance in quantitative comparisons and visual quality. The code and dataset are available at https://github.com/chxy95/HDRTVNet. | Xiangyu Chen, Zhengwen Zhang, Jimmy S. Ren, Lynhoo Tian, Yu Qiao, Chao Dong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4500-4509 | null | null | 2,021 | iccv |
Vision-Language Transformer and Query Generation for Referring Segmentation | null | In this work, we address the challenging task of referring segmentation. The query expression in referring segmentation typically indicates the target object by describing its relationship with others. Therefore, to find the target one among all instances in the image, the model must have a holistic understanding of the whole image. To achieve this, we reformulate referring segmentation as a direct attention problem: finding the region in the image where the query language expression is most attended to. We introduce transformer and multi-head attention to build a network with an encoder-decoder attention mechanism architecture that "queries" the given image with the language expression. Furthermore, we propose a Query Generation Module, which produces multiple sets of queries with different attention weights that represent the diversified comprehensions of the language expression from different aspects. At the same time, to find the best way from these diversified comprehensions based on visual clues, we further propose a Query Balance Module to adaptively select the output features of these queries for a better mask generation. Without bells and whistles, our approach is light-weight and achieves new state-of-the-art performance consistently on three referring segmentation datasets, RefCOCO, RefCOCO+, and G-Ref. Our code is available at https://github.com/henghuiding/Vision-Language-Transformer. | Henghui Ding, Chang Liu, Suchen Wang, Xudong Jiang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 16321-16330 | null | null | 2,021 | iccv |
Global Pooling, More Than Meets the Eye: Position Information Is Encoded Channel-Wise in CNNs | null | In this paper, we challenge the common assumption that collapsing the spatial dimensions of a 3D (spatial-channel) tensor in a convolutional neural network (CNN) into a vector via global pooling removes all spatial information. Specifically, we demonstrate that positional information is encoded based on the ordering of the channel dimensions, while semantic information is largely not. Following this demonstration, we show the real world impact of these findings by applying them to two applications. First, we propose a simple yet effective data augmentation strategy and loss function which improves the translation invariance of a CNN's output. Second, we propose a method to efficiently determine which channels in the latent representation are responsible for (i) encoding overall position information or (ii) region-specific positions. We first show that semantic segmentation has a significant reliance on the overall position channels to make predictions. We then show for the first time that it is possible to perform a `region-specific' attack, and degrade a network's performance in a particular part of the input. We believe our findings and demonstrated applications will benefit research areas concerned with understanding the characteristics of CNNs. | Md Amirul Islam, Matthew Kowal, Sen Jia, Konstantinos G. Derpanis, Neil D. B. Bruce; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 793-801 | null | null | 2,021 | iccv |
Vision-Language Navigation With Random Environmental Mixup | null | Vision-language Navigation (VLN) task requires an agent to perceive both the visual scene and natural language and navigate step-by-step. Large data bias makes the VLN task challenging, which is caused by the disparity ratio between small data scale and large navigation space. Previous works have proposed many data augmentation methods to reduce data bias. However, these works do not explicitly reduce the data bias across different house scenes. Therefore, the agent would be overfitting to the seen scenes and perform navigation poorly in the unseen scenes. To tackle this problem, we propose the random environmental mixup (REM) method, which generates augmentation data in cross-connected house scenes. This method consists of three steps: 1) we select the key viewpoints according to the room connection graph for each scene in the training split; 2) we cross-connect the key views of different scenes to construct augmented scenes; 3) we generate augmentation data triplets (environment, path, instruction) in the cross-connected scenes. Our experiments prove that the augmentation data helps the agent reduce its performance gap between the seen and unseen environment and improve its performance, making our model be the best existing approach on the standard benchmark. | Chong Liu, Fengda Zhu, Xiaojun Chang, Xiaodan Liang, Zongyuan Ge, Yi-Dong Shen; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1644-1654 | null | null | 2,021 | iccv |
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images | null | While neural symbolic methods demonstrate impressive performance in visual question answering on synthetic images, their performance suffers on real images. We identify that the long-tail distribution of visual concepts and unequal importance of reasoning steps in real data are the two key obstacles that limit the models' real-world potentials. To address these challenges, we propose a new paradigm, Calibrating Concepts and Operations (CCO), which enables neural symbolic models to capture underlying data characteristics and to reason with hierarchical importance. Specifically, we introduce an executor with learnable concept embedding magnitudes for handling distribution imbalance, and an operation calibrator for highlighting important operations and suppressing redundant ones. Our experiments show CCO substantially boosts the performance of neural symbolic methods on real images. By evaluating models on the real world dataset GQA, CCO helps the neural symbolic method NSCL outperforms its vanilla counterpart by 9.1% (from 47.0% to 56.1%); this result also largely reduces the performance gap between symbolic and non-symbolic methods. Additionally, we create a perturbed test set for better understanding and analyzing model performance on real images. Code is available at https://lizw14.github.io/project/ccosr. | Zhuowan Li, Elias Stengel-Eskin, Yixiao Zhang, Cihang Xie, Quan Hung Tran, Benjamin Van Durme, Alan Yuille; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14910-14919 | null | null | 2,021 | iccv |
SCOUTER: Slot Attention-Based Classifier for Explainable Image Recognition | null | Explainable artificial intelligence has been gaining attention in the past few years. However, most existing methods are based on gradients or intermediate features, which are not directly involved in the decision-making process of the classifier. In this paper, we propose a slot attention-based classifier called SCOUTER for transparent yet accurate classification. Two major differences from other attention-based methods include: (a) SCOUTER's explanation is involved in the final confidence for each category, offering more intuitive interpretation, and (b) all the categories have their corresponding positive or negative explanation, which tells "why the image is of a certain category" or "why the image is not of a certain category." We design a new loss tailored for SCOUTER that controls the model's behavior to switch between positive and negative explanations, as well as the size of explanatory regions. Experimental results show that SCOUTER can give better visual explanations in terms of various metrics while keeping good accuracy on small and medium-sized datasets. | Liangzhi Li, Bowen Wang, Manisha Verma, Yuta Nakashima, Ryo Kawasaki, Hajime Nagahara; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1046-1055 | null | null | 2,021 | iccv |
COTR: Correspondence Transformer for Matching Across Images | null | We propose a novel framework for finding correspondences in images based on a deep neural network that, given two images and a query point in one of them, finds its correspondence in the other. By doing so, one has the option to query only the points of interest and retrieve sparse correspondences, or to query all points in an image and obtain dense mappings. Importantly, in order to capture both local and global priors, and to let our model relate between image regions using the most relevant among said priors, we realize our network using a transformer. At inference time, we apply our correspondence network by recursively zooming in around the estimates, yielding a multi-scale pipeline able to provide highly-accurate correspondences. Our method significantly outperforms the state-of-the-art on both sparse and dense correspondence problems on multiple datasets and tasks, ranging from wide-baseline stereo to optical flow, without any retraining for a specific dataset. | Wei Jiang, Eduard Trulls, Jan Hosang, Andrea Tagliasacchi, Kwang Moo Yi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6207-6217 | null | null | 2,021 | iccv |
ELLIPSDF: Joint Object Pose and Shape Optimization With a Bi-Level Ellipsoid and Signed Distance Function Description | null | Autonomous systems need to understand the semantics and geometry of their surroundings in order to comprehend and safely execute object-level task specifications. This paper proposes an expressive yet compact model for joint object pose and shape optimization, and an associated optimization algorithm to infer an object-level map from multi-view RGB-D camera observations. The model is expressive because it captures the identities, positions, orientations, and shapes of objects in the environment. It is compact because it relies on a low-dimensional latent representation of implicit object shape, allowing onboard storage of large multi-category object maps. Different from other works that rely on a single object representation format, our approach has a bi-level object model that captures both the coarse level scale as well as the fine level shape details. Our approach is evaluated on the large-scale real-world ScanNet dataset and compared against state-of-the-art methods. | Mo Shan, Qiaojun Feng, You-Yi Jau, Nikolay Atanasov; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5946-5955 | null | null | 2,021 | iccv |
CvT: Introducing Convolutions to Vision Transformers | null | We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs. This is accomplished through two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Trasnsformer block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) to the ViT architecture (i.e. shift, scale, and distortion invariance) while maintaining the merits of Transformers (i.e. dynamic attention, global context, and better generalization). We validate CvT by conducting extensive experiments, showing that this approach achieves state-of-the-art performance over other Vision Transformers and ResNets on ImageNet-1k, with less parameters and lower FLOPs. In addition, performance gains are maintained when pretrained on larger datasets (e.g. ImageNet-22k) and fine-tuned to downstream tasks. Finally, our results show that the positional encoding, a crucial component in existing Vision Transformers, can be safely removed in our model, simplifying the design for higher resolution vision tasks. Code will be released at https://github.com/microsoft/CvT. | Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 22-31 | null | null | 2,021 | iccv |
Object Tracking by Jointly Exploiting Frame and Event Domain | null | Inspired by the complementarity between conventional frame-based and bio-inspired event-based cameras, we propose a multi-modal based approach to fuse visual cues from the frame- and event-domain to enhance the single object tracking performance, especially in degraded conditions (e.g., scenes with high dynamic range, low light, and fast-motion objects). The proposed approach can effectively and adaptively combine meaningful information from both domains. Our approach's effectiveness is enforced by a novel designed cross-domain attention schemes, which can effectively enhance features based on self- and cross-domain attention schemes; The adaptiveness is guarded by a specially designed weighting scheme, which can adaptively balance the contribution of the two domains. To exploit event-based visual cues in single-object tracking, we construct a large-scale frame-event-based dataset, which we subsequently employ to train a novel frame-event fusion based model. Extensive experiments show that the proposed approach outperforms state-of-the-art frame-based tracking methods by at least 10.4% and 11.9% in terms of representative success rate and precision rate, respectively. Besides, the effectiveness of each key component of our approach is evidenced by our thorough ablation study. | Jiqing Zhang, Xin Yang, Yingkai Fu, Xiaopeng Wei, Baocai Yin, Bo Dong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13043-13052 | null | null | 2,021 | iccv |
Testing Using Privileged Information by Adapting Features With Statistical Dependence | null | Given an imperfect predictor, we exploit additional features at test time to improve the predictions made, without retraining and without knowledge of the prediction function. This scenario arises if training labels or data are proprietary, restricted, or no longer available, or if training itself is prohibitively expensive. We assume that the additional features are useful if they exhibit strong statistical dependence to the underlying perfect predictor. Then, we empirically estimate and strengthen the statistical dependence between the initial noisy predictor and the additional features via manifold denoising. As an example, we show that this approach leads to improvement in real-world visual attribute ranking. | Kwang In Kim, James Tompkin; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9405-9413 | null | null | 2,021 | iccv |
Learning To Generate Scene Graph From Natural Language Supervision | null | Learning from image-text data has demonstrated recent success for many recognition tasks, yet is currently limited to visual features or individual visual concepts such as objects. In this paper, we propose one of the first methods that learn from image-sentence pairs to extract a graphical representation of localized objects and their relationships within an image, known as scene graph. To bridge the gap between images and texts, we leverage an off-the-shelf object detector to identify and localize object instances, match labels of detected regions to concepts parsed from captions, and thus create "pseudo" labels for learning scene graph. Further, we design a Transformer-based model to predict these "pseudo" labels via a masked token prediction task. Learning from only image-sentence pairs, our model achieves 30% relative gain over a latest method trained with human-annotated unlocalized scene graphs. Our model also shows strong results for weakly and fully supervised scene graph generation. In addition, we explore an open-vocabulary setting for detecting scene graphs, and present the first result for open-set scene graph generation. | Yiwu Zhong, Jing Shi, Jianwei Yang, Chenliang Xu, Yin Li; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1823-1834 | null | null | 2,021 | iccv |
DecentLaM: Decentralized Momentum SGD for Large-Batch Deep Training | null | The scale of deep learning nowadays calls for efficient distributed training algorithms. Decentralized momentum SGD (DmSGD), in which each node averages only with its neighbors, is more communication efficient than vanilla Parallel momentum SGD that incurs global average across all computing nodes. On the other hand, the large-batch training has been demonstrated critical to achieve runtime speedup. This motivates us to investigate how DmSGD performs in the large-batch scenario. In this work, we find the momentum term can amplify the inconsistency bias in DmSGD. Such bias becomes more evident as batch-size grows large and hence results in severe performance degradation. We next propose DecentLaM, a novel decentralized large-batch momentum SGD to remove the momentum-incurred bias. The convergence rate for both strongly convex and non-convex scenarios is established. Our theoretical results justify the superiority of DecentLaM to DmSGD especially in the large-batch scenario. Experimental results on a a variety of computer vision tasks and models show that DecentLaM promises both efficient and high-quality training. | Kun Yuan, Yiming Chen, Xinmeng Huang, Yingya Zhang, Pan Pan, Yinghui Xu, Wotao Yin; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3029-3039 | null | null | 2,021 | iccv |
CoMatch: Semi-Supervised Learning With Contrastive Graph Regularization | null | Semi-supervised learning has been an effective paradigm for leveraging unlabeled data to reduce the reliance on labeled data. We propose CoMatch, a new semi-supervised learning method that unifies dominant approaches and addresses their limitations. CoMatch jointly learns two representations of the training data, their class probabilities and low-dimensional embeddings. The two representations interact with each other to jointly evolve. The embeddings impose a smoothness constraint on the class probabilities to improve the pseudo-labels, whereas the pseudo-labels regularize the structure of the embeddings through graph-based contrastive learning. CoMatch achieves state-of-the-art performance on multiple datasets. It achieves substantial accuracy improvements on the label-scarce CIFAR-10 and STL-10. On ImageNet with 1% labels, CoMatch achieves a top-1 accuracy of 66.0%, outperforming FixMatch by 12.6%. Furthermore, CoMatch achieves better representation learning performance on downstream tasks, outperforming both supervised learning and self-supervised learning. Code and pre-trained models are available at https://github.com/salesforce/CoMatch/. | Junnan Li, Caiming Xiong, Steven C.H. Hoi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9475-9484 | null | null | 2,021 | iccv |
Context-Sensitive Temporal Feature Learning for Gait Recognition | null | Although gait recognition has drawn increasing research attention recently, it remains challenging to learn discriminative temporal representation since the silhouette differences are quite subtle in spatial domain. Inspired by the observation that humans can distinguish gaits of different subjects by adaptively focusing on temporal sequences with different time scales, we propose a context-sensitive temporal feature learning (CSTL) network in this paper, which aggregates temporal features in three scales to obtain motion representation according to the temporal contextual information. Specifically, CSTL introduces relation modeling among multi-scale features to evaluate feature importances, based on which network adaptively enhances more important scale and suppresses less important scale. Besides that, we propose a salient spatial feature learning (SSFL) module to tackle the misalignment problem caused by temporal operation, e.g., temporal convolution. SSFL recombines a frame of salient spatial features by extracting the most discriminative parts across the whole sequence. In this way, we achieve adaptive temporal learning and salient spatial mining simultaneously. Extensive experiments conducted on two datasets demonstrate the state-of-the-art performance. On CASIA-B dataset, we achieve rank-1 accuracies of 98.0%, 95.4% and 87.0% under normal walking, bag-carrying and coat-wearing conditions. On OU-MVLP dataset, we achieve rank-1 accuracy of 90.2%. The source code will be published at https://github.com/OliverHxh/CSTL. | Xiaohu Huang, Duowang Zhu, Hao Wang, Xinggang Wang, Bo Yang, Botao He, Wenyu Liu, Bin Feng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12909-12918 | null | null | 2,021 | iccv |
Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition | null | Accuracy predictor is a key component in Neural Architecture Search (NAS) for ranking architectures. Building a high-quality accuracy predictor usually costs enormous computation. To address this issue, instead of using an accuracy predictor, we propose a novel zero-shot index dubbed Zen-Score to rank the architectures. The Zen-Score represents the network expressivity and positively correlates with the model accuracy. The calculation of Zen-Score only takes a few forward inferences through a randomly initialized network, without training network parameters. Built upon the Zen-Score, we further propose a new NAS algorithm, termed as Zen-NAS, by maximizing the Zen-Score of the target network under given inference budgets. Within less than half GPU day, Zen-NAS is able to directly search high performance architectures in a data-free style. Comparing with previous NAS methods, the proposed Zen-NAS is magnitude times faster on multiple server-side and mobile-side GPU platforms with state-of-the-art accuracy on ImageNet. Searching and training code as well as pre-trained models are available from https://github.com/idstcv/ZenNAS. | Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 347-356 | null | null | 2,021 | iccv |
Virtual Light Transport Matrices for Non-Line-of-Sight Imaging | null | The light transport matrix (LTM) is an instrumental tool in line-of-sight (LOS) imaging, describing how light interacts with the scene and enabling applications such as relighting or separation of illumination components. We introduce a framework to estimate the LTM of non-line-of-sight (NLOS) scenarios, coupling recent virtual forward light propagation models for NLOS imaging with the LOS light transport equation. We design computational projector-camera setups, and use these virtual imaging systems to estimate the transport matrix of hidden scenes. We introduce the specific illumination functions to compute the different elements of the matrix, overcoming the challenging wide-aperture conditions of NLOS setups. Our NLOS light transport matrix allows us to (re)illuminate specific locations of a hidden scene, and separate direct, first-order indirect, and higher-order indirect illumination of complex cluttered hidden scenes, similar to existing LOS techniques. | Julio Marco, Adrian Jarabo, Ji Hyun Nam, Xiaochun Liu, Miguel Ángel Cosculluela, Andreas Velten, Diego Gutierrez; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2440-2449 | null | null | 2,021 | iccv |
End-to-End Semi-Supervised Object Detection With Soft Teacher | null | Previous pseudo-label approaches for semi-supervised object detection typically follow a multi-stage schema, with the first stage to train an initial detector on a few labeled data, followed by the pseudo labeling and re-training stage on unlabeled data. These multi-stage methods complicate the training, and also hinder the use of improved detectors for more accurate pseudo-labeling. In this paper, we propose an end-to-end approach to simultaneously improve the detector and pseudo labels gradually for semi-supervised object detection. The pseudo labels are generated on the fly by a teacher model which is an aggregated version of the student detector at different steps. As the detector becomes stronger during the training, the teacher detector's performance improves and the pseudo labels tend to be more accurate, which further benefits the detector training. Within the end-to-end training, we present two simple yet effective techniques: weigh the classification loss of unlabeled images through soft teacher and select reliable pseudo boxes for regression through box jittering. Experimentally, the proposed approach outperforms the state-of-the-art methods by a large margin on MS-COCO benchmark by using Faster R-CNN with ResNet-50 and FPN, reaching 20.5 mAP, 30.7 mAP and 34.0 mAP with 1%, 5%, 10% labeled data, respectively. Moreover, the proposed approach also proves to improve this detector trained on the COCO full set by +1.8 mAP by leveraging additional unlabelled data of COCO, achieving 42.7 mAP. | Mengde Xu, Zheng Zhang, Han Hu, Jianfeng Wang, Lijuan Wang, Fangyun Wei, Xiang Bai, Zicheng Liu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3060-3069 | null | null | 2,021 | iccv |
Pseudo-Mask Matters in Weakly-Supervised Semantic Segmentation | null | Most weakly supervised semantic segmentation (WSSS) methods follow the pipeline that generates pseudo-masks initially and trains the segmentation model with the pseudo-masks in fully supervised manner after. However, we find some matters related to the pseudo-masks, including high quality pseudo-masks generation from class activation maps (CAMs), and training with noisy pseudo-mask supervision. For these matters, we propose the following designs to push the performance to new state-of-art: (i) Coefficient of Variation Smoothing to smooth the CAMs adaptively; (ii) Proportional Pseudo-mask Generation to project the expanded CAMs to pseudo-mask based on a new metric indicating the importance of each class on each location, instead of the scores trained from binary classifiers. (iii) Pretended Under-Fitting strategy to suppress the influence of noise in pseudo-mask; (iv) Cyclic Pseudo-mask to boost the pseudo-masks during training of fully supervised semantic segmentation (FSSS). Experiments based on our methods achieve new state-of-art results on two changeling weakly supervised semantic segmentation datasets, pushing the mIoU to 70.0% and 40.2% on PAS-CAL VOC 2012 and MS COCO 2014 respectively. Codes including segmentation framework are released at https://github.com/Eli-YiLi/PMM | Yi Li, Zhanghui Kuang, Liyang Liu, Yimin Chen, Wayne Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6964-6973 | null | null | 2,021 | iccv |
Deep Reparametrization of Multi-Frame Super-Resolution and Denoising | null | We propose a deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks. Our approach is derived by introducing a learned error metric and a latent representation of the target image, which transforms the MAP objective to a deep feature space. The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image priors into the prediction. Our approach thereby leverages the advantages of deep learning, while also benefiting from the principled multi-frame fusion provided by the classical MAP formulation. We validate our approach through comprehensive experiments on burst denoising and burst super-resolution datasets. Our approach sets a new state-of-the-art for both tasks, demonstrating the generality and effectiveness of the proposed formulation. | Goutam Bhat, Martin Danelljan, Fisher Yu, Luc Van Gool, Radu Timofte; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2460-2470 | null | null | 2,021 | iccv |
Video Object Segmentation With Dynamic Memory Networks and Adaptive Object Alignment | null | In this paper, we propose a novel solution for object-matching based semi-supervised video object segmentation, where the target object masks in the first frame are provided. Existing object-matching based methods focus on the matching between the raw object features of the current frame and the first/previous frames. However, two issues are still not solved by these object-matching based methods. As the appearance of the video object changes drastically over time, 1) unseen parts/details of the object present in the current frame, resulting in incomplete annotation in the first annotated frame (e.g., view/scale changes). 2) even for the seen parts/details of the object in the current frame, their positions change relatively (e.g., pose changes/camera motion), leading to a misalignment for the object matching. To obtain the complete information of the target object, we propose a novel object-based dynamic memory network that exploits visual contents of all the past frames. To solve the misalignment problem caused by position changes of visual contents, we propose an adaptive object alignment module by incorporating a region translation function that aligns object proposals towards templates in the feature space. Our method achieves state-of-the-art results on latest benchmark datasets DAVIS 2017 (J of 81.4% and F of 87.5% on the validation set) and YouTube-VOS (the overall score of 82.7% on the validation set) with a very efficient inference time (0.16 second/frame on DAVIS 2017 validation set). Code is available at: https://github.com/liang4sx/DMN-AOA. | Shuxian Liang, Xu Shen, Jianqiang Huang, Xian-Sheng Hua; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8065-8074 | null | null | 2,021 | iccv |
Lucas-Kanade Reloaded: End-to-End Super-Resolution From Raw Image Bursts | null | This presentation addresses the problem of reconstructing a high-resolution image from multiple lower-resolution snapshots captured from slightly different viewpoints in space and time. Key challenges for solving this super-resolution problem include (i) aligning the input pictures with sub-pixel accuracy, (ii) handling raw (noisy) images for maximal faithfulness to native camera data, and (iii) designing/learning an image prior (regularizer) well suited to the task. We address these three challenges with a hybrid algorithm building on the insight from Wronski et al. that aliasing is an ally in this setting, with parameters that can be learned end to end, while retaining the interpretability of classical approaches to inverse problems. The effectiveness of our approach is demonstrated on synthetic and real image bursts, setting a new state of the art on several benchmarks and delivering excellent qualitative results on real raw bursts captured by smartphones and prosumer cameras. | Bruno Lecouat, Jean Ponce, Julien Mairal; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2370-2379 | null | null | 2,021 | iccv |
Product Quantizer Aware Inverted Index for Scalable Nearest Neighbor Search | null | The inverted index is one of the most commonly used structures for non-exhaustive nearest neighbor search on large-scale datasets. It allows a significant factor of acceleration by a reduced number of distance computations with only a small fraction of the database. In particular, the inverted index enables the product quantization (PQ) to learn their codewords in the residual vector space. The quantization error of the PQ can be substantially improved in such combination since the residual vector space is much more quantization-friendly thanks to their compact distribution compared to the original data. In this paper, we first raise an unremarked but crucial question; why the inverted index and the product quantizer are optimized separately even though they are closely related? For instance, changes on the inverted index distort the whole residual vector space. To address the raised question, we suggest a joint optimization of the coarse and fine quantizers by substituting the original objective of the coarse quantizer to end-to-end quantization distortion. Moreover, our method is generic and applicable to different combinations of coarse and fine quantizers such as inverted multi-index and optimized PQ. | Haechan Noh, Taeho Kim, Jae-Pil Heo; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12210-12218 | null | null | 2,021 | iccv |
ViViT: A Video Vision Transformer | null | We present pure-transformer based models for video classification, drawing upon the recent success of such models in image classification. Our model extracts spatio-temporal tokens from the input video, which are then encoded by a series of transformer layers. In order to handle the long sequences of tokens encountered in video, we propose several, efficient variants of our model which factorise the spatial- and temporal-dimensions of the input. Although transformer-based models are known to only be effective when large training datasets are available, we show how we can effectively regularise the model during training and leverage pretrained image models to be able to train on comparatively small datasets. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple video classification benchmarks including Kinetics 400 and 600, Epic Kitchens, Something-Something v2 and Moments in Time, outperforming prior methods based on deep 3D convolutional networks. To facilitate further research, we will release code and models. | Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6836-6846 | null | null | 2,021 | iccv |
3D Local Convolutional Neural Networks for Gait Recognition | null | The goal of gait recognition is to learn the unique spatio-temporal pattern about the human body shape from its temporal changing characteristics. As different body parts behave differently during walking, it is intuitive to model the spatio-temporal patterns of each part separately. However, existing part-based methods equally divide the feature maps of each frame into fixed horizontal stripes to get local parts. It is obvious that these stripe partition-based methods cannot accurately locate the body parts. First, different body parts can appear at the same stripe (e.g., arms and the torso), and one part can appear at different stripes in different frames (e.g., hands). Second, different body parts possess different scales, and even the same part in different frames can appear at different locations and scales. Third, different parts also exhibit distinct movement patterns (e.g., at which frame the movement starts, the position change frequency, how long it lasts). To overcome these issues, we propose novel 3D local operations as a generic family of building blocks for 3D gait recognition backbones. The proposed 3D local operations support the extraction of local 3D volumes of body parts in a sequence with adaptive spatial and temporal scales, locations and lengths. In this way, the spatio-temporal patterns of the body parts are well learned from the 3D local neighborhood in part-specific scales, locations, frequencies and lengths. Experiments demonstrate that our 3D local convolutional neural networks achieve state-of-the-art performance on popular gait datasets. Code is available at: https://github.com/yellowtownhz/3DLocalCNN. | Zhen Huang, Dixiu Xue, Xu Shen, Xinmei Tian, Houqiang Li, Jianqiang Huang, Xian-Sheng Hua; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14920-14929 | null | null | 2,021 | iccv |
An Elastica Geodesic Approach With Convexity Shape Prior | null | The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios. Existing geodesic-based segmentation approaches usually exploit the image features in conjunction with geometric regularization terms (such as curve length or elastica length) for computing geodesic paths. In this paper, we consider a more complicated problem: finding simple and closed geodesic curves which are imposed a convexity shape prior. The proposed approach relies on an orientation-lifting strategy, by which a planar curve can be mapped to an high-dimensional orientation space. The convexity shape prior serves as a constraint for the construction of local metrics. The geodesic curves in the lifted space then can be efficiently computed through the fast marching method. In addition, we introduce a way to incorporate region-based homogeneity features into the proposed geodesic model so as to solve the region-based segmentation issues with shape prior constraints. | Da Chen, Laurent D. Cohen, Jean-Marie Mirebeau, Xue-Cheng Tai; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6900-6909 | null | null | 2,021 | iccv |
Contrastive Multimodal Fusion With TupleInfoNCE | null | This paper proposes a method for representation learning of multimodal data using contrastive losses. A traditional approach is to contrast different modalities to learn the information shared between them. However, that approach could fail to learn the complementary synergies between modalities that might be useful for downstream tasks. Another approach is to concatenate all the modalities into a tuple and then contrast positive and negative tuple correspondences. However, that approach could consider only the stronger modalities while ignoring the weaker ones. To address these issues, we propose a novel contrastive learning objective, TupleInfoNCE. It contrasts tuples based not only on positive and negative correspondences, but also by composing new negative tuples using modalities describing different scenes. Training with these additional negatives encourages the learning model to examine the correspondences among modalities in the same tuple, ensuring that weak modalities are not ignored. We provide a theoretical justification based on mutual-information for why this approach works, and we propose a sample optimization algorithm to generate positive and negative samples to maximize training efficacy. We find that TupleInfoNCE significantly outperforms previous state of the arts on three different downstream tasks. | Yunze Liu, Qingnan Fan, Shanghang Zhang, Hao Dong, Thomas Funkhouser, Li Yi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 754-763 | null | null | 2,021 | iccv |
PR-GCN: A Deep Graph Convolutional Network With Point Refinement for 6D Pose Estimation | null | RGB-D based 6D pose estimation has recently achieved remarkable progress, but still suffers from two major limitations: (1) ineffective representation of depth data and (2) insufficient integration of different modalities. This paper proposes a novel deep learning approach, namely Graph Convolutional Network with Point Refinement (PR-GCN), to simultaneously address the issues above in a unified way. It first introduces the Point Refinement Network (PRN) to polish 3D point clouds, recovering missing parts with noise removed. Subsequently, the Multi-Modal Fusion Graph Convolutional Network (MMF-GCN) is presented to strengthen RGB-D combination, which captures geometry-aware inter-modality correlation through local information propagation in the graph convolutional network. Extensive experiments are conducted on three widely used benchmarks, and state-of-the-art performance is reached. Besides, it is also shown that the proposed PRN and MMF-GCN modules are well generalized to other frameworks. | Guangyuan Zhou, Huiqun Wang, Jiaxin Chen, Di Huang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2793-2802 | null | null | 2,021 | iccv |
Are We Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection? | null | Pseudo-LiDAR-based methods for monocular 3D object detection have received considerable attention in the community due to the performance gains exhibited on the KITTI3D benchmark, in particular on the commonly reported validation split. This generated a distorted impression about the superiority of Pseudo-LiDAR-based (PL-based) approaches over methods working with RGB images only. Our first contribution consists in rectifying this view by pointing out and showing experimentally that the validation results published by PL-based methods are substantially biased. The source of the bias resides in an overlap between the KITTI3D object detection validation set and the training/validation sets used to train depth predictors feeding PL-based methods. Surprisingly, the bias remains also after geographically removing the overlap. This leaves the test set as the only reliable set for comparison, where published PL-based methods do not excel. Our second contribution brings PL-based methods back up in the ranking with the design of a novel deep architecture which introduces a 3D confidence prediction module. We show that 3D confidence estimation techniques derived from RGB-only 3D detection approaches can be successfully integrated into our framework and, more importantly, that improved performance can be obtained with a newly designed 3D confidence measure, leading to state-of-the-art performance on the KITTI3D benchmark. | Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder, Elisa Ricci; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3225-3233 | null | null | 2,021 | iccv |
Learning High-Fidelity Face Texture Completion Without Complete Face Texture | null | For face texture completion, previous methods typically use some complete textures captured by multiview imaging systems or 3D scanners for supervised learning. This paper deals with a new challenging problem -- learning to complete invisible texture in a single face image without using any complete texture. We simply leverage a large corpus of face images of different subjects (e.\,g., FFHQ) to train a texture completion model in an unsupervised manner. To achieve this, we propose DSD-GAN, a novel deep neural network based method that applies two discriminators in UV map space and image space. These two discriminators work in a complementary manner to learn both facial structures and texture details. We show that their combination is essential to obtain high-fidelity results. Despite the network never sees any complete facial appearance, it is able to generate compelling full textures from single images. | Jongyoo Kim, Jiaolong Yang, Xin Tong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13990-13999 | null | null | 2,021 | iccv |
Track Without Appearance: Learn Box and Tracklet Embedding With Local and Global Motion Patterns for Vehicle Tracking | null | Vehicle tracking is an essential task in the multi-object tracking (MOT) field. A distinct characteristic in vehicle tracking is that the trajectories of vehicles are fairly smooth in both the world coordinate and the image coordinate. Hence, models that capture motion consistencies are of high necessity. However, tracking with the standalone motion-based trackers is quite challenging because targets could get lost easily due to limited information, detection error and occlusion. Leveraging appearance information to assist object re-identification could resolve this challenge to some extent. However, doing so requires extra computation while appearance information is sensitive to occlusion as well. In this paper, we try to explore the significance of motion patterns for vehicle tracking without appearance information. We propose a novel approach that tackles the association issue for long-term tracking with the exclusive fully-exploited motion information. We address the tracklet embedding issue with the proposed reconstruct-to-embed strategy based on deep graph convolutional neural networks (GCN). Comprehensive experiments on the KITTI-car tracking dataset and UA-Detrac dataset show that the proposed method, though without appearance information, could achieve competitive performance with the state-of-the-art (SOTA) trackers. The source code will be available at https://github.com/GaoangW/LGMTracker. | Gaoang Wang, Renshu Gu, Zuozhu Liu, Weijie Hu, Mingli Song, Jenq-Neng Hwang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9876-9886 | null | null | 2,021 | iccv |
Learning Better Visual Data Similarities via New Grouplet Non-Euclidean Embedding | null | In many computer vision problems, it is desired to learn the effective visual data similarity such that the prediction accuracy can be enhanced. Deep Metric Learning (DML) methods have been actively studied to measure the data similarity. Pair-based and proxy-based losses are the two major paradigms in DML. However, pair-wise methods involve expensive training costs, while proxy-based methods are less accurate in characterizing the relationships between data points. In this paper, we provide a hybrid grouplet paradigm, which inherits the accurate pair-wise relationship in pair-based methods and the efficient training in proxy-based methods. Our method also equips a non-Euclidean space to DML, which employs a hierarchical representation manifold. More specifically, we propose a unified graph perspective --- different DML methods learn different local connecting patterns between data points. Based on the graph interpretation, we construct a flexible subset of data points, dubbed grouplet. Our grouplet doesn't require explicit pair-wise relationships, instead, we encode the data relationships in an optimal transport problem regarding the proxies, and solve this problem via a differentiable implicit layer to automatically determine the relationships. Extensive experimental results show that our method significantly outperforms state-of-the-art baselines on several benchmarks. The ablation studies also verify the effectiveness of our method. | Yanfu Zhang, Lei Luo, Wenhan Xian, Heng Huang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9918-9927 | null | null | 2,021 | iccv |
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack | null | Modern deep neural networks are often vulnerable to adversarial examples. Most exist attack methods focus on crafting adversarial examples in the digital domain, while only limited works study physical adversarial attack. However, it is more challenging to generate effective adversarial examples in the physical world due to many uncontrollable physical dynamics. Most current physical attack methods aim to generate robust physical adversarial examples by simulating all possible physical dynamics. When attacking new images or new DNN models, they require expensive manually efforts for simulating physical dynamics and considerable time for iteratively optimizing for each image. To tackle these issues, we propose a class-agnostic and model-agnostic physical adversarial attack model (Meta-Attack), which is able to not only generate robust physical adversarial examples by simulating color and shape distortions, but also generalize to attacking novel images and novel DNN models by accessing a few digital and physical images. To the best of our knowledge, this is the first work to formulate the physical attack as a few-shot learning problem. Here, the training task is redefined as the composition of a support set, a query set, and a target DNN model. Under the few- shot setting, we design a novel class-agnostic and model-agnostic meta-learning algorithm to enhance the generalization ability of our method. Extensive experimental results on two benchmark datasets with four challenging experimental settings verify the superior robustness and generalization of our method by comparing to state-of-the-art physical attack methods. | Weiwei Feng, Baoyuan Wu, Tianzhu Zhang, Yong Zhang, Yongdong Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 7787-7796 | null | null | 2,021 | iccv |
Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning | null | Modern computer vision applications suffer from catastrophic forgetting when incrementally learning new concepts over time. The most successful approaches to alleviate this forgetting require extensive replay of previously seen data, which is problematic when memory constraints or data legality concerns exist. In this work, we consider the high-impact problem of Data-Free Class-Incremental Learning (DFCIL), where an incremental learning agent must learn new concepts over time without storing generators or training data from past tasks. One approach for DFCIL is to replay synthetic images produced by inverting a frozen copy of the learner's classification model, but we show this approach fails for common class-incremental benchmarks when using standard distillation strategies. We diagnose the cause of this failure and propose a novel incremental distillation strategy for DFCIL, contributing a modified cross-entropy training and importance-weighted feature distillation, and show that our method results in up to a 25.1% increase in final task accuracy (absolute difference) compared to SOTA DFCIL methods for common class-incremental benchmarks. Our method even outperforms several standard replay based methods which store a coreset of images. | James Smith, Yen-Chang Hsu, Jonathan Balloch, Yilin Shen, Hongxia Jin, Zsolt Kira; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9374-9384 | null | null | 2,021 | iccv |
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth | null | As a fundamental building block in computer vision, edges can be categorised into four types according to the discontinuity in surface-Reflectance, Illumination, surface-Normal or Depth. While great progress has been made in detecting generic or individual types of edges, it remains under-explored to comprehensively study all four edge types together. In this paper, we propose a novel neural network solution, RINDNet, to jointly detect all four types of edges. Taking into consideration the distinct attributes of each type of edges and the relationship between them, RINDNet learns effective representations for each of them and works in three stages. In stage I, RINDNet uses a common backbone to extract features shared by all edges. Then in stage II it branches to prepare discriminative features for each edge type by the corresponding decoder. In stage III, an independent decision head for each type aggregates the features from previous stages to predict the initial results. Additionally, an attention module learns attention maps for all types to capture the underlying relations between them, and these maps are combined with initial results to generate the final edge detection results. For training and evaluation, we construct the first public benchmark, BSDS-RIND, with all four types of edges carefully annotated. In our experiments, RINDNet yields promising results in comparison with state-of-the-art methods. Additional analysis is presented in supplementary material. | Mengyang Pu, Yaping Huang, Qingji Guan, Haibin Ling; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6879-6888 | null | null | 2,021 | iccv |
Probabilistic Monocular 3D Human Pose Estimation With Normalizing Flows | null | 3D human pose estimation from monocular images is a highly ill-posed problem due to depth ambiguities and occlusions. Nonetheless, most existing works ignore these ambiguities and only estimate a single solution. In contrast, we generate a diverse set of hypotheses that represents the full posterior distribution of feasible 3D poses. To this end, we propose a normalizing flow based method that exploits the deterministic 3D-to-2D mapping to solve the ambiguous inverse 2D-to-3D problem. Additionally, uncertain detections and occlusions are effectively modeled by incorporating uncertainty information of the 2D detector as condition. Further keys to success are a learned 3D pose prior and a generalization of the best-of-M loss. We evaluate our approach on the two benchmark datasets Human3.6M and MPI-INF-3DHP, outperforming all comparable methods in most metrics. The implementation is available on GitHub. | Tom Wehrbein, Marco Rudolph, Bodo Rosenhahn, Bastian Wandt; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11199-11208 | null | null | 2,021 | iccv |
Video Matting via Consistency-Regularized Graph Neural Networks | null | Learning temporally consistent foreground opacity from videos, i.e., video matting, has drawn great attention due to the blossoming of video conferencing. Previous approaches are built on top of image matting models, which fail in maintaining the temporal coherence when being adapted to videos. They either utilize the optical flow to smooth frame-wise prediction, where the performance is dependent on the selected optical flow model; or naively combine feature maps from multiple frames, which does not model well the correspondence of pixels in adjacent frames. In this paper, we propose to enhance the temporal coherence by Consistency-Regularized Graph Neural Networks (CRGNN) with the aid of a synthesized video matting dataset. CRGNN utilizes Graph Neural Networks (GNN) to relate adjacent frames such that pixels or regions that are incorrectly predicted in one frame can be corrected by leveraging information from its neighboring frames. To generalize our model from synthesized videos to real-world videos, we propose a consistency regularization technique to enforce the consistency on the alpha and foreground when blending them with different backgrounds. To evaluate the efficacy of CRGNN, we further collect a real-world dataset with annotated alpha mattes. Compared with state-of-the-art methods that require hand-crafted trimaps or backgrounds for modeling training, CRGNN generates favorably results with the help of unlabeled real training dataset. | Tiantian Wang, Sifei Liu, Yapeng Tian, Kai Li, Ming-Hsuan Yang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4902-4911 | null | null | 2,021 | iccv |
Benchmarking Ultra-High-Definition Image Super-Resolution | null | Increasingly, modern mobile devices allow capturing images at Ultra-High-Definition (UHD) resolution, which includes 4K and 8K images. However, current single image super-resolution (SISR) methods focus on super-resolving images to ones with resolution up to high definition (HD) and ignore higher-resolution UHD images. To explore their performance on UHD images, in this paper, we first introduce two large-scale image datasets, UHDSR4K and UHDSR8K, to benchmark existing SISR methods. With 70,000 V100 GPU hours of training, we benchmark these methods on 4K and 8K resolution images under seven different settings to provide a set of baseline models. Moreover, we propose a baseline model, called Mesh Attention Network (MANet) for SISR. The MANet applies the attention mechanism in both different depths (horizontal) and different levels of receptive field (vertical). In this way, correlations among feature maps are learned, enabling the network to focus on more important features. | Kaihao Zhang, Dongxu Li, Wenhan Luo, Wenqi Ren, Björn Stenger, Wei Liu, Hongdong Li, Ming-Hsuan Yang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14769-14778 | null | null | 2,021 | iccv |
On the Limits of Pseudo Ground Truth in Visual Camera Re-Localisation | null | Benchmark datasets that measure camera pose accuracy have driven progress in visual re-localisation research. To obtain poses for thousands of images, it is common to use a reference algorithm to generate pseudo ground truth. Popular choices include Structure-from-Motion (SfM) and Simultaneous-Localisation-and-Mapping (SLAM) using additional sensors like depth cameras if available. Re-localisation benchmarks thus measure how well each method replicates the results of the reference algorithm. This begs the question whether the choice of the reference algorithm favours a certain family of re-localisation methods. This paper analyzes two widely used re-localisation datasets and shows that evaluation outcomes indeed vary with the choice of the reference algorithm. We thus question common beliefs in the re-localisation literature, namely that learning-based scene coordinate regression outperforms classical feature-based methods, and that RGB-D- based methods outperform RGB-based methods. We argue that any claims on ranking re-localisation methods should take the type of the reference algorithm, and the similarity of the methods to the reference algorithm, into account. | Eric Brachmann, Martin Humenberger, Carsten Rother, Torsten Sattler; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6218-6228 | null | null | 2,021 | iccv |
Inverting a Rolling Shutter Camera: Bring Rolling Shutter Images to High Framerate Global Shutter Video | null | Rolling shutter (RS) images can be viewed as the result of the row-wise combination of global shutter (GS) images captured by a virtual moving GS camera over the period of camera readout time. The RS effect brings tremendous difficulties for the downstream applications. In this paper, we propose to invert the above RS imaging mechanism, i.e., recovering a high framerate GS video from consecutive RS images to achieve RS temporal super-resolution (RSSR). This extremely challenging problem, e.g., recovering 1440 GS images from two 720-height RS images, is far from being solved end-to-end. To address this challenge, we exploit the geometric constraint in the RS camera model, thus achieving geometry-aware inversion. Specifically, we make three contributions in resolving the above difficulties: (i) formulating the bidirectional RS undistortion flows under the constant velocity motion model, (ii) building the connection between the RS undistortion flow and optical flow via a scaling operation, and (iii) developing a mutual conversion scheme between varying RS undistortion flows that correspond to different scanlines. Building upon these formulations, we propose the first RS temporal super-resolution network in a cascaded structure to extract high framerate global shutter video. Our method explores the underlying spatio-temporal geometric relationships within a deep learning framework, where no extra supervision besides the middle-scanline ground truth GS image is needed. Essentially, our method can be very efficient for explicit propagation to generate GS images under any scanline. Experimental results on both synthetic and real data show that our method can produce high-quality GS image sequences with rich details, outperforming state-of-the-art methods. | Bin Fan, Yuchao Dai; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4228-4237 | null | null | 2,021 | iccv |
PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition | null | Face recognition networks encode information about sensitive attributes while being trained for identity classification. Such encoding has two major issues: (a) it makes the face representations susceptible to privacy leakage (b) it appears to contribute to bias in face recognition. However, existing bias mitigation approaches generally require end-to-end training and are unable to achieve high verification accuracy. Therefore, we present a descriptor-based adversarial de-biasing approach called `Protected Attribute Suppression System (PASS)'. PASS can be trained on top of descriptors obtained from any previously trained high-performing network to classify identities and simultaneously reduce encoding of sensitive attributes. This eliminates the need for end-to-end training. As a component of PASS, we present a novel discriminator training strategy that discourages a network from encoding protected attribute information. We show the efficacy of PASS to reduce gender and skintone information in descriptors from SOTA face recognition networks like Arcface. As a result, PASS descriptors outperform existing baselines in reducing gender and skintone bias on the IJB-C dataset, while maintaining a high verification accuracy. | Prithviraj Dhar, Joshua Gleason, Aniket Roy, Carlos D. Castillo, Rama Chellappa; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15087-15096 | null | null | 2,021 | iccv |
AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer | null | Fast arbitrary neural style transfer has attracted widespread attention from academic, industrial and art communities due to its flexibility in enabling various applications. Existing solutions either attentively fuse deep style feature into deep content feature without considering feature distributions, or adaptively normalize deep content feature according to the style such that their global statistic information is matched. Although effective, leaving shallow feature unexplored or without locally considering feature statistics, they are prone to suffer from unnatural output with unpleasing local distortions. To alleviate this problem, in this paper, we propose a novel Adaptive Attention Normalization (AdaAttN) module to adaptively perform attentive normalization on per-point basis. Specifically, spatial attention score is learnt from both shallow and deep features of content and style images. Then per-point weighted statistics are calculated by regarding a style feature point as a distribution of attention-weighted output of all style feature points. Finally, the content feature is normalized so that they demonstrate the same local feature statistics as the calculated per-point weighted style feature statistics. Besides, a novel local feature loss is derived based on AdaAttN to enhance local visual quality. We also extend AdaAttN to be ready for video style transfer with slight modifications. Extensive experiments demonstrate that our method achieves state-of-the-art arbitrary image/video style transfer. Codes and models will be available. | Songhua Liu, Tianwei Lin, Dongliang He, Fu Li, Meiling Wang, Xin Li, Zhengxing Sun, Qian Li, Errui Ding; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6649-6658 | null | null | 2,021 | iccv |
From Two to One: A New Scene Text Recognizer With Visual Language Modeling Network | null | In this paper, we abandon the dominant complex language model and rethink the linguistic learning process in the scene text recognition. Different from previous methods considering the visual and linguistic information in two separate structures, we propose a Visual Language Modeling Network (VisionLAN), which views the visual and linguistic information as a union by directly enduing the vision model with language capability. Specially, we introduce the text recognition of character-wise occluded feature maps in the training stage. Such operation guides the vision model to use not only the visual texture of characters, but also the linguistic information in visual context for recognition when the visual cues are confused (e.g. occlusion, noise, etc.). As the linguistic information is acquired along with visual features without the need of extra language model, VisionLAN significantly improves the speed by 39% and adaptively considers the linguistic information to enhance the visual features for accurate recognition. Furthermore, an Occlusion Scene Text (OST) dataset is proposed to evaluate the performance on the case of missing character-wise visual cues. The state of-the-art results on several benchmarks prove our effectiveness. Code and dataset are available at https://github.com/wangyuxin87/VisionLAN. | Yuxin Wang, Hongtao Xie, Shancheng Fang, Jing Wang, Shenggao Zhu, Yongdong Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14194-14203 | null | null | 2,021 | iccv |
GDP: Stabilized Neural Network Pruning via Gates With Differentiable Polarization | null | Model compression techniques are recently gaining explosive attention for obtaining efficient AI models for various real time applications. Channel pruning is one important compression strategy, and widely used in slimming various DNNs. Previous gate-based or importance-based pruning methods aim to remove channels whose "importance" are smallest. However, it remains unclear what criteria the channel importance should be measured on, leading to various channel selection heuristics. Some other sampling-based pruning methods deploy sampling strategy to train sub-nets, which often causes the training instability and the compressed model's degraded performance. In view of the research gaps, we present a new module named Gates with Differentiable Polarization (GDP), inspired by principled optimization ideas. GDP can be plugged before convolutional layers without bells and whistles, to control the on-and-off of each channel or whole layer block. During the training process, the polarization effect will drive a subset of gates to smoothly decrease to exactly zero, while other gates gradually stay away from zero by a large margin. When training terminates, those zero-gated channels can be painlessly removed, while other non-zero gates can be absorbed into the succeeding convolution kernel, causing completely no interruption to training nor damage to the trained model. Experiments conducted over CIFAR-10 and ImageNet datasets show that the proposed GDP algorithm achieves the state-of-the-art performance on various benchmark DNNs at a broad range of pruning ratios. We also apply GDP to DeepLabV3Plus-ResNet50 on the challenging Pascal VOC segmentation task, whose test performance sees no drop (even slightly improved) with over 60% FLOPs saving. | Yi Guo, Huan Yuan, Jianchao Tan, Zhangyang Wang, Sen Yang, Ji Liu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5239-5250 | null | null | 2,021 | iccv |
Learning To Bundle-Adjust: A Graph Network Approach to Faster Optimization of Bundle Adjustment for Vehicular SLAM | null | Bundle adjustment (BA) occupies a large portion of SfM and visual SLAM's total execution time. Local BA over the latest several keyframes plays a crucial role in visual SLAM. Its execution time should be sufficiently short for robust tracking; this is especially critical for embedded systems with a limited computational resource. This study proposes a learning-based method using a graph network that can replace conventional optimization-based BA and works faster. The graph network operates on a graph consisting of the nodes of keyframes and landmarks and the edges of the latter's visibility from the former. The graph network receives the parameters' initial values as inputs and predicts the updates to their optimal values. We design an intermediate representation of inputs inspired by the normal equation of the Levenberg-Marquardt method. We use the sum of reprojection errors as a loss function to train the graph network. The experiments show that the proposed method outputs parameter estimates with slightly inferior accuracy in 1/60-1/10 of time compared with the conventional BA. | Tetsuya Tanaka, Yukihiro Sasagawa, Takayuki Okatani; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6250-6259 | null | null | 2,021 | iccv |
GRF: Learning a General Radiance Field for 3D Representation and Rendering | null | We present a simple yet powerful neural network that implicitly represents and renders 3D objects and scenes only from 2D observations. The network models 3D geometries as a general radiance field, which takes a set of 2D images with camera poses and intrinsics as input, constructs an internal representation for each point of the 3D space, and then renders the corresponding appearance and geometry of that point viewed from an arbitrary position. The key to our approach is to learn local features for each pixel in 2D images and to then project these features to 3D points, thus yielding general and rich point representations. We additionally integrate an attention mechanism to aggregate pixel features from multiple 2D views, such that visual occlusions are implicitly taken into account. Extensive experiments demonstrate that our method can generate high-quality and realistic novel views for novel objects, unseen categories and challenging real-world scenes. | Alex Trevithick, Bo Yang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15182-15192 | null | null | 2,021 | iccv |
Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection | null | Arbitrary shape text detection is a challenging task due to the high complexity and variety of scene texts. In this work, we propose a novel adaptive boundary proposal network for arbitrary shape text detection, which can learn to directly produce accurate boundary for arbitrary shape text without any post-processing. Our method mainly consists of a boundary proposal model and an innovative adaptive boundary deformation model. The boundary proposal model constructed by multi-layer dilated convolutions is adopted to produce prior information (including classification map, distance field, and direction field) and coarse boundary proposals. The adaptive boundary deformation model is an encoder-decoder network, in which the encoder mainly consists of a Graph Convolutional Network (GCN) and a Recurrent Neural Network (RNN). It aims to perform boundary deformation in an iterative way for obtaining text instance shape guided by prior information from the boundary proposal model. In this way, our method can directly and efficiently generate accurate text boundaries without complex post-processing. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method. | Shi-Xue Zhang, Xiaobin Zhu, Chun Yang, Hongfa Wang, Xu-Cheng Yin; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1305-1314 | null | null | 2,021 | iccv |
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