title
stringlengths 5
246
| categories
stringlengths 5
94
⌀ | abstract
stringlengths 54
5.03k
| authors
stringlengths 0
6.72k
| doi
stringlengths 12
54
⌀ | id
stringlengths 6
10
⌀ | year
float64 2.02k
2.02k
⌀ | venue
stringclasses 13
values |
|---|---|---|---|---|---|---|---|
Learning To Resize Images for Computer Vision Tasks | null | For all the ways convolutional neural nets have revolutionized computer vision in recent years, one important aspect has received surprisingly little attention: the effect of image size on the accuracy of tasks being trained for. Typically, to be efficient, the input images are resized to a relatively small spatial resolution (e.g. 224x224), and both training and inference are carried out at this resolution. The actual mechanism for this re-scaling has been an afterthought: Namely, off-the-shelf image resizers such as bilinear and bicubic are commonly used in most machine learning software frameworks. But do these resizers limit the on task performance of the trained networks? The answer is yes. Indeed, we show that the typical linear resizer can be replaced with learned resizers that can substantially improve performance. Importantly, while the classical resizers typically result in better perceptual quality of the downscaled images, our proposed learned resizers do not necessarily give better visual quality, but instead improve task performance. Our learned image resizer is jointly trained with a baseline vision model. This learned CNN-based resizer creates machine friendly visual manipulations that lead to a consistent improvement of the end task metric over the baseline model. Specifically, here we focus on the classification task with the ImageNet dataset, and experiment with four different models to learn resizers adapted to each model. Moreover, we show that the proposed resizer can also be useful for fine-tuning the classification baselines for other vision tasks. To this end, we experiment with three different baselines to develop image quality assessment (IQA) models on the AVA dataset. | Hossein Talebi, Peyman Milanfar; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 497-506 | null | null | 2,021 | iccv |
Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery | null | People often use physical intuition when manipulating articulated objects, irrespective of object semantics. Motivated by this observation, we identify an important embodied task where an agent must play with objects to recover their parts. To this end, we introduce Act the Part (AtP) to learn how to interact with articulated objects to discover and segment their pieces. By coupling action selection and motion segmentation, AtP is able to isolate structures to make perceptual part recovery possible without semantic labels. Our experiments show AtP learns efficient strategies for part discovery, can generalize to unseen categories, and is capable of conditional reasoning for the task. Although trained in simulation, we show convincing transfer to real world data with no fine-tuning. A summery video, interactive demo, and code will be available at atp.cs.columbia.edu. | Samir Yitzhak Gadre, Kiana Ehsani, Shuran Song; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15752-15761 | null | null | 2,021 | iccv |
PnP-DETR: Towards Efficient Visual Analysis With Transformers | null | Recently, DETR pioneered the solution of vision tasks with transformers, it directly translates the image feature map into the object detection result. Though effective, translating the full feature map can be costly due to redundant computation on some area like the background. In this work, we encapsulate the idea of reducing spatial redundancy into a novel poll and pool (PnP) sampling module, with which we build an end-to-end PnP-DETR architecture that adaptively allocates its computation spatially to be more efficient. Concretely, the PnP module abstracts the image feature map into fine foreground object feature vectors and a small number of coarse background contextual feature vectors. The transformer models information interaction within the fine-coarse feature space and translates the features into the detection result. Moreover, the PnP-augmented model can instantly achieve various desired trade-offs between performance and computation with a single model by varying the sampled feature length, without requiring to train multiple models as existing methods. Thus it offers greater flexibility for deployment in diverse scenarios with varying computation constraint. We further validate the generalizability of the PnP module on panoptic segmentation and the recent transformer-based image recognition model ViT and show consistent efficiency gain. We believe our method makes a step for efficient visual analysis with transformers, wherein spatial redundancy is commonly observed. Code and models will be available. | Tao Wang, Li Yuan, Yunpeng Chen, Jiashi Feng, Shuicheng Yan; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4661-4670 | null | null | 2,021 | iccv |
PlaneTR: Structure-Guided Transformers for 3D Plane Recovery | null | This paper presents a neural network built upon Transformers, namely PlaneTR, to simultaneously detect and reconstruct planes from a single image. Different from previous methods, PlaneTR jointly leverages the context information and the geometric structures in a sequence-to-sequence way to holistically detect plane instances in one forward pass. Specifically, we represent the geometric structures as line segments and conduct the network with three main components: (i) context and line segments encoders, (ii) a structure-guided plane decoder, (iii) a pixel-wise plane embedding decoder. Given an image and its detected line segments, PlaneTR generates the context and line segment sequences via two specially designed encoders and then feeds them into a Transformers-based decoder to directly predict a sequence of plane instances by simultaneously considering the context and global structure cues. Finally, the pixel-wise embeddings are computed to assign each pixel to one predicted plane instance which is nearest to it in embedding space. Comprehensive experiments demonstrate that PlaneTR achieves state-of-the-art performance on the ScanNet and NYUv2 datasets. | Bin Tan, Nan Xue, Song Bai, Tianfu Wu, Gui-Song Xia; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4186-4195 | null | null | 2,021 | iccv |
Multi-Echo LiDAR for 3D Object Detection | null | LiDAR sensors can be used to obtain a wide range of measurement signals other than a simple 3D point cloud, and those signals can be leveraged to improve perception tasks like 3D object detection. A single laser pulse can be partially reflected by multiple objects along its path, resulting in multiple measurements called echoes. Multi-echo measurement can provide information about object contours and semi-transparent surfaces which can be used to better identify and locate objects. LiDAR can also measure surface reflectance (intensity of laser pulse return), as well as ambient light of the scene (sunlight reflected by objects). These signals are already available in commercial LiDAR devices but have not been used in most LiDAR-based detection models. We present a 3D object detection model which leverages the full spectrum of measurement signals provided by LiDAR. First, we propose a multi-signal fusion (MSF) module to combine (1) the reflectance and ambient features extracted with a 2D CNN, and (2) point cloud features extracted using a 3D graph neural network (GNN). Second, we propose a multi-echo aggregation (MEA) module to combine the information encoded in different set of echo points. Compared with traditional single echo point cloud methods, our proposed multi-signal LiDAR Detector (MSLiD) extracts richer context information from a wider range of sensing measurements and achieves more accurate 3D object detection. Experiments show that by incorporating the multi-modality of LiDAR, our method outperforms the state-of-the-art by up to relatively 9.1%. | Yunze Man, Xinshuo Weng, Prasanna Kumar Sivakumar, Matthew O'Toole, Kris M. Kitani; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3763-3772 | null | null | 2,021 | iccv |
DCT-SNN: Using DCT To Distribute Spatial Information Over Time for Low-Latency Spiking Neural Networks | null | Spiking Neural Networks (SNNs) offer a promising alternative to traditional deep learning frameworks, since they provide higher computational efficiency due to event-driven information processing. SNNs distribute the analog values of pixel intensities into binary spikes over time. However, the most widely used input coding schemes, such as Poisson based rate-coding, do not leverage the additional temporal learning capability of SNNs effectively. Moreover, these SNNs suffer from high inference latency which is a major bottleneck to their deployment. To overcome this, we propose a time-based encoding scheme that utilizes the Discrete Cosine Transform (DCT) to reduce the number of timesteps required for inference. DCT decomposes an image into a weighted sum of sinusoidal basis images. At each time step, a single frequency base, taken in order and modulated by its corresponding DCT coefficient, is input to an accumulator that generates spikes upon crossing a threshold. We use the proposed scheme to learn DCT-SNN, a low-latency deep SNN with leaky-integrate-and-fire neurons, trained using surrogate gradient descent based backpropagation. We achieve top-1 accuracy of 89.94%, 68.3% and 52.43% on CIFAR-10, CIFAR-100 and TinyImageNet, respectively using VGG architectures. Notably, DCT-SNN performs inference with 2-14X reduced latency compared to other state-of-the-art SNNs, while achieving comparable accuracy to their standard deep learning counterparts. The dimension of the transform allows us to control the number of timesteps required for inference. Additionally, we can trade-off accuracy with latency in a principled manner by dropping the highest frequency components during inference. The code is publicly available*. | Isha Garg, Sayeed Shafayet Chowdhury, Kaushik Roy; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4671-4680 | null | null | 2,021 | iccv |
Weakly Supervised Temporal Anomaly Segmentation With Dynamic Time Warping | null | Most recent studies on detecting and localizing temporal anomalies have mainly employed deep neural networks to learn the normal patterns of temporal data in an unsupervised manner. Unlike them, the goal of our work is to fully utilize instance-level (or weak) anomaly labels, which only indicate whether any anomalous events occurred or not in each instance of temporal data. In this paper, we present WETAS, a novel framework that effectively identifies anomalous temporal segments (i.e., consecutive time points) in an input instance. WETAS learns discriminative features from the instance-level labels so that it infers the sequential order of normal and anomalous segments within each instance, which can be used as a rough segmentation mask. Based on the dynamic time warping (DTW) alignment between the input instance and its segmentation mask, WETAS obtains the result of temporal segmentation, and simultaneously, it further enhances itself by using the mask as additional supervision. Our experiments show that WETAS considerably outperforms other baselines in terms of the localization of temporal anomalies, and also it provides more informative results than point-level detection methods. | Dongha Lee, Sehun Yu, Hyunjun Ju, Hwanjo Yu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 7355-7364 | null | null | 2,021 | iccv |
LSD-StructureNet: Modeling Levels of Structural Detail in 3D Part Hierarchies | null | Generative models for 3D shapes represented by hierarchies of parts can generate realistic and diverse sets of outputs. However, existing models suffer from the key practical limitation of modelling shapes holistically and thus cannot perform conditional sampling, i.e. they are not able to generate variants on individual parts of generated shapes without modifying the rest of the shape. This is limiting for applications such as 3D CAD design that involve adjusting created shapes at multiple levels of detail. To address this, we introduce LSD-StructureNet, an augmentation to the StructureNet architecture that enables re-generation of parts situated at arbitrary positions in the hierarchies of its outputs. We achieve this by learning individual, probabilistic conditional decoders for each hierarchy depth. We evaluate LSD-StructureNet on the PartNet dataset, the largest dataset of 3D shapes represented by hierarchies of parts. Our results show that contrarily to existing methods, LSD-StructureNet can perform conditional sampling without impacting inference speed or the realism and diversity of its outputs. | Dominic Roberts, Ara Danielyan, Hang Chu, Mani Golparvar-Fard, David Forsyth; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5836-5845 | null | null | 2,021 | iccv |
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization | null | We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. With our new datasets, we take stock of previously proposed methods for improving out-of-distribution robustness and put them to the test. We find that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work. We find improvements in artificial robustness benchmarks can transfer to real-world distribution shifts, contrary to claims in prior work. Motivated by our observation that data augmentations can help with real-world distribution shifts, we also introduce a new data augmentation method which advances the state-of-the-art and outperforms models pretrained with 1000x more labeled data. Overall we find that some methods consistently help with distribution shifts in texture and local image statistics, but these methods do not help with some other distribution shifts like geographic changes. Our results show that future research must study multiple distribution shifts simultaneously, as we demonstrate that no evaluated method consistently improves robustness. | Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8340-8349 | null | null | 2,021 | iccv |
Crowd Counting With Partial Annotations in an Image | null | To fully leverage the data captured from different scenes with different view angles while reducing the annotation cost, this paper studies a novel crowd counting setting, i.e. only using partial annotations in each image as training data. Inspired by the repetitive patterns in the annotated and unannotated regions as well as the ones between them, we design a network with three components to tackle those unannotated regions: i) in an Unannotated Regions Characterization (URC) module, we employ a memory bank to only store the annotated features, which could help the visual features extracted from these annotated regions flow to these unannotated regions; ii) For each image, Feature Distribution Consistency (FDC) regularizes the feature distributions of annotated head and unannotated head regions to be consistent; iii) a Cross-regressor Consistency Regularization (CCR) module is designed to learn the visual features of unannotated regions in a self-supervised style. The experimental results validate the effectiveness of our proposed model under the partial annotation setting for several datasets, such as ShanghaiTech, UCF-CC-50, UCF-QNRF, NWPU-Crowd, and JHU-CROWD++. With only 10% annotated regions in each image, our proposed model achieves better performance than the recent methods and baselines under semi-supervised or active learning settings on all datasets. The code is https://github.com/svip-lab/CrwodCountingPAL. | Yanyu Xu, Ziming Zhong, Dongze Lian, Jing Li, Zhengxin Li, Xinxing Xu, Shenghua Gao; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15570-15579 | null | null | 2,021 | iccv |
Semantic-Embedded Unsupervised Spectral Reconstruction From Single RGB Images in the Wild | null | This paper investigates the problem of reconstructing hyperspectral (HS) images from single RGB images captured by commercial cameras, without using paired HS and RGB images during training. To tackle this challenge, we propose a new lightweight and end-to-end learning-based framework. Specifically, on the basis of the intrinsic imaging degradation model of RGB images from HS images, we progressively spread the differences between input RGB images and re-projected RGB images from recovered HS images via effective unsupervised camera spectral response function estimation. To enable the learning without paired ground-truth HS images as supervision, we adopt the adversarial learning manner and boost it with a simple yet effective L1 gradient clipping scheme. Besides, we embed the semantic information of input RGB images to locally regularize the unsupervised learning, which is expected to promote pixels with identical semantics to have consistent spectral signatures. In addition to conducting quantitative experiments over two widely-used datasets for HS image reconstruction from synthetic RGB images, we also evaluate our method by applying recovered HS images from real RGB images to HS-based visual tracking. Extensive results show that our method significantly outperforms state-of-the-art unsupervised methods and even exceeds the latest supervised method under some settings. The source code is public available at https://github.com/zbzhzhy/Unsupervised-Spectral-Reconstruction. | Zhiyu Zhu, Hui Liu, Junhui Hou, Huanqiang Zeng, Qingfu Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2279-2288 | null | null | 2,021 | iccv |
Membership Inference Attacks Are Easier on Difficult Problems | null | Membership inference attacks (MIA) try to detect if data samples were used to train a neural network model, e.g. to detect copyright abuses. We show that models with higher dimensional input and output are more vulnerable to MIA, and address in more detail models for image translation and semantic segmentation, including medical image segmentation. We show that reconstruction-errors can lead to very effective MIA attacks as they are indicative of memorization. Unfortunately, reconstruction error alone is less effective at discriminating between non-predictable images used in training and easy to predict images that were never seen before. To overcome this, we propose using a novel predictability error that can be computed for each sample, and its computation does not require a training set. Our membership error, obtained by subtracting the predictability error from the reconstruction error, is shown to achieve high MIA accuracy on an extensive number of benchmarks. | Avital Shafran, Shmuel Peleg, Yedid Hoshen; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14820-14829 | null | null | 2,021 | iccv |
Self-Supervised Cryo-Electron Tomography Volumetric Image Restoration From Single Noisy Volume With Sparsity Constraint | null | Cryo-Electron Tomography (cryo-ET) is a powerful tool for 3D cellular visualization. Due to instrumental limitations, cryo-ET images and their volumetric reconstruction suffer from extremely low signal-to-noise ratio. In this paper, we propose a novel end-to-end self-supervised learning model, the Sparsity Constrained Network (SC-Net), to restore volumetric image from single noisy data in cryo-ET. The proposed method only requires a single noisy data as training input and no ground-truth is needed in the whole training procedure. A new target function is proposed to preserve both local smoothness and detailed structure. Additionally, a novel procedure for the simulation of electron tomographic photographing is designed to help the evaluation of methods. Experiments are done on three simulated data and four real-world data. The results show that our method could produce a strong enhancement for a single very noisy cryo-ET volumetric data, which is much better than the state-of-the-art Noise2Void, and with a competitive performance comparing with Noise2Noise. Code is available at https://github.com/icthrm/SC-Net. | Zhidong Yang, Fa Zhang, Renmin Han; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4056-4065 | null | null | 2,021 | iccv |
Unsupervised Segmentation Incorporating Shape Prior via Generative Adversarial Networks | null | We present an image segmentation algorithm that is developed in an unsupervised deep learning framework. The delineation of object boundaries often fails due to the nuisance factors such as illumination changes and occlusions. Thus, we initially propose an unsupervised image decomposition algorithm to obtain an intrinsic representation that is robust with respect to undesirable bias fields based on a multiplicative image model. The obtained intrinsic image is subsequently provided to an unsupervised segmentation procedure that is developed based on a piecewise smooth model. The segmentation model is further designed to incorporate a geometric constraint imposed in the generative adversarial network framework where the discrepancy between the distribution of partitioning functions and the distribution of prior shapes is minimized. We demonstrate the effectiveness and robustness of the proposed algorithm in particular with bias fields and occlusions using simple yet illustrative synthetic examples and a benchmark dataset for image segmentation. | Dahye Kim, Byung-Woo Hong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 7324-7334 | null | null | 2,021 | iccv |
Learning Action Completeness From Points for Weakly-Supervised Temporal Action Localization | null | We tackle the problem of localizing temporal intervals of actions with only a single frame label for each action instance for training. Owing to label sparsity, existing work fails to learn action completeness, resulting in fragmentary action predictions. In this paper, we propose a novel framework, where dense pseudo-labels are generated to provide completeness guidance for the model. Concretely, we first select pseudo background points to supplement point-level action labels. Then, by taking the points as seeds, we search for the optimal sequence that is likely to contain complete action instances while agreeing with the seeds. To learn completeness from the obtained sequence, we introduce two novel losses that contrast action instances with background ones in terms of action score and feature similarity, respectively. Experimental results demonstrate that our completeness guidance indeed helps the model to locate complete action instances, leading to large performance gains especially under high IoU thresholds. Moreover, we demonstrate the superiority of our method over existing state-of-the-art methods on four benchmarks: THUMOS'14, GTEA, BEOID, and ActivityNet. Notably, our method even performs comparably to recent fully-supervised methods, at the 6 times cheaper annotation cost. Our code is available at https://github.com/Pilhyeon. | Pilhyeon Lee, Hyeran Byun; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13648-13657 | null | null | 2,021 | iccv |
Re-Distributing Biased Pseudo Labels for Semi-Supervised Semantic Segmentation: A Baseline Investigation | null | While self-training has advanced semi-supervised semantic segmentation, it severely suffers from the long-tailed class distribution on real-world semantic segmentation datasets that make the pseudo-labeled data bias toward majority classes. In this paper, we present a simple and yet effective Distribution Alignment and Random Sampling (DARS) method to produce unbiased pseudo labels that match the true class distribution estimated from the labeled data. Besides, we also contribute a progressive data augmentation and labeling strategy to facilitate model training with pseudo-labeled data. Experiments on both Cityscapes and PASCAL VOC 2012 datasets demonstrate the effectiveness of our approach. Albeit simple, our method performs favorably in comparison with state-of-the-art approaches. Code will be available at https://github.com/CVMI-Lab/DARS. | Ruifei He, Jihan Yang, Xiaojuan Qi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6930-6940 | null | null | 2,021 | iccv |
Mining Contextual Information Beyond Image for Semantic Segmentation | null | This paper studies the context aggregation problem in semantic image segmentation. The existing researches focus on improving the pixel representations by aggregating the contextual information within individual images. Though impressive, these methods neglect the significance of the representations of the pixels of the corresponding class beyond the input image. To address this, this paper proposes to mine the contextual information beyond individual images to further augment the pixel representations. We first set up a feature memory module, which is updated dynamically during training, to store the dataset-level representations of various categories. Then, we learn class probability distribution of each pixel representation under the supervision of the ground-truth segmentation. At last, the representation of each pixel is augmented by aggregating the dataset-level representations based on the corresponding class probability distribution. Furthermore, by utilizing the stored dataset-level representations, we also propose a representation consistent learning strategy to make the classification head better address intra-class compactness and inter-class dispersion. The proposed method could be effortlessly incorporated into existing segmentation frameworks (e.g., FCN, PSPNet, OCRNet and DeepLabV3) and brings consistent performance improvements. Mining contextual information beyond image allows us to report state-of-the-art performance on various benchmarks: ADE20K, LIP, Cityscapes and COCO-Stuff. | Zhenchao Jin, Tao Gong, Dongdong Yu, Qi Chu, Jian Wang, Changhu Wang, Jie Shao; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 7231-7241 | null | null | 2,021 | iccv |
Learning To Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data | null | Recent work has shown impressive results on data-driven defocus deblurring using the two-image views available on modern dual-pixel (DP) sensors. One significant challenge in this line of research is access to DP data. Despite many cameras having DP sensors, only a limited number provide access to the low-level DP sensor images. In addition, capturing training data for defocus deblurring involves a time-consuming and tedious setup requiring the camera's aperture to be adjusted. Some cameras with DP sensors (e.g., smartphones) do not have adjustable apertures, further limiting the ability to produce the necessary training data. We address the data capture bottleneck by proposing a procedure to generate realistic DP data synthetically. Our synthesis approach mimics the optical image formation found on DP sensors and can be applied to virtual scenes rendered with standard computer software. Leveraging these realistic synthetic DP images, we introduce a recurrent convolutional network (RCN) architecture that improves deblurring results and is suitable for use with single-frame and multi-frame data (e.g., video) captured by DP sensors. Finally, we show that our synthetic DP data is useful for training DNN models targeting video deblurring applications where access to DP data remains challenging. | Abdullah Abuolaim, Mauricio Delbracio, Damien Kelly, Michael S. Brown, Peyman Milanfar; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2289-2298 | null | null | 2,021 | iccv |
Visformer: The Vision-Friendly Transformer | null | The past year has witnessed the rapid development of applying the Transformer module to vision problems. While some researchers have demonstrated that Transformer-based models enjoy a favorable ability of fitting data, there are still growing number of evidences showing that these models suffer over-fitting especially when the training data is limited. This paper offers an empirical study by performing step-by-step operations to gradually transit a Transformer-based model to a convolution-based model. The results we obtain during the transition process deliver useful messages for improving visual recognition. Based on these observations, we propose a new architecture named Visformer, which is abbreviated from the 'Vision-friendly Transformer'. With the same computational complexity, Visformer outperforms both the Transformer-based and convolution-based models in terms of ImageNet classification accuracy, and the advantage becomes more significant when the model complexity is lower or the training set is smaller. The code is available at https://github.com/danczs/Visformer. | Zhengsu Chen, Lingxi Xie, Jianwei Niu, Xuefeng Liu, Longhui Wei, Qi Tian; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 589-598 | null | null | 2,021 | iccv |
Uncertainty-Aware Pseudo Label Refinery for Domain Adaptive Semantic Segmentation | null | Unsupervised domain adaptation for semantic segmentation aims to assign the pixel-level labels for unlabeled target domain by transferring knowledge from the labeled source domain. A typical self-supervised learning approach generates pseudo labels from the source model and then re-trains the model to fit the target distribution. However, it suffers from noisy pseudo labels due to the existence of domain shift. Related works alleviate this problem by selecting high-confidence predictions, but uncertain classes with low confidence scores have rarely been considered. This informative uncertainty is essential to enhance feature representation and align source and target domains. In this paper, we propose a novel uncertainty-aware pseudo label refinery framework considering two crucial factors simultaneously. First, we progressively enhance the feature alignment model via the target-guided uncertainty rectifying framework. Second, we provide an uncertainty-aware pseudo label assignment strategy without any manually designed threshold to reduce the noisy labels. Extensive experiments demonstrate the effectiveness of our proposed approach and achieve state-of-the-art performance on two standard synthetic-2-real tasks. | Yuxi Wang, Junran Peng, ZhaoXiang Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9092-9101 | null | null | 2,021 | iccv |
Field of Junctions: Extracting Boundary Structure at Low SNR | null | We introduce a bottom-up model for simultaneously finding many boundary elements in an image, including contours, corners and junctions. The model explains boundary shape in each small patch using a 'generalized M-junction' comprising M angles and a freely-moving vertex. Images are analyzed using non-convex optimization to cooperatively find M+2 junction values at every location, with spatial consistency being enforced by a novel regularizer that reduces curvature while preserving corners and junctions. The resulting 'field of junctions' is simultaneously a contour detector, corner/junction detector, and boundary-aware smoothing of regional appearance. Notably, its unified analysis of contours, corners, junctions and uniform regions allows it to succeed at high noise levels, where other methods for segmentation and boundary detection fail. | Dor Verbin, Todd Zickler; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6869-6878 | null | null | 2,021 | iccv |
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition | null | The training of a deep face recognition system usually faces the interference of label noise in the training data. However, it is difficult to obtain a high-precision cleaning model to remove these noises. In this paper, we propose an adaptive label noise cleaning algorithm based on meta-learning for face recognition datasets, which can learn the distribution of the data to be cleaned and make automatic adjustments based on class differences. It first learns reliable cleaning knowledge from well-labeled noisy data, then gradually transfers it to the target data with meta-supervision to improve performance. A threshold adapter module is also proposed to address the drift problem in transfer learning methods. Extensive experiments clean two noisy in-the-wild face recognition datasets and show the effectiveness of the proposed method to reach state-of-the-art performance on the IJB-C face recognition benchmark. | Yaobin Zhang, Weihong Deng, Yaoyao Zhong, Jiani Hu, Xian Li, Dongyue Zhao, Dongchao Wen; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15065-15075 | null | null | 2,021 | iccv |
OpenGAN: Open-Set Recognition via Open Data Generation | null | Real-world machine learning systems need to analyze novel testing data that differs from the training data. In K-way classification, this is crisply formulated as open-set recognition, core to which is the ability to discriminate open-set data outside the K closed-set classes. Two conceptually elegant ideas for open-set discrimination are: 1) discriminatively learning an open-vs-closed binary discriminator by exploiting some outlier data as the open-set, and 2) unsupervised learning the closed-set data distribution with a GAN and using its discriminator as the open-set likelihood function. However, the former generalizes poorly to diverse open test data due to overfitting to the training outliers, which unlikely exhaustively span the open-world. The latter does not work well, presumably due to the instable training of GANs. Motivated by the above, we propose OpenGAN, which addresses the limitation of each approach by combining them with several technical insights. First, we show that a carefully selected GAN-discriminator on some real outlier data already achieves the state-of-the-art. Second, we augment the available set of real open training examples with adversarially synthesized ""fake"" data. Third and most importantly, we build the discriminator over the features computed by the closed-world K-way networks. Extensive experiments show that OpenGAN significantly outperforms prior open-set methods. | Shu Kong, Deva Ramanan; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 813-822 | null | null | 2,021 | iccv |
Learning Indoor Inverse Rendering With 3D Spatially-Varying Lighting | null | In this work, we address the problem of jointly estimating albedo, normals, depth and 3D spatially-varying lighting from a single image. Most existing methods formulate the task as image-to-image translation, ignoring the 3D properties of the scene. However, indoor scenes contain complex 3D light transport where a 2D representation is insufficient. In this paper, we propose a unified, learning-based inverse rendering framework that formulates 3D spatially-varying lighting. Inspired by classic volume rendering techniques, we propose a novel Volumetric Spherical Gaussian representation for lighting, which parameterizes the exitant radiance of the 3D scene surfaces on a voxel grid. We design a physicsbased differentiable renderer that utilizes our 3D lighting representation, and formulates the energy-conserving image formation process that enables joint training of all intrinsic properties with the re-rendering constraint. Our model ensures physically correct predictions and avoids the need for ground-truth HDR lighting which is not easily accessible. Experiments show that our method outperforms prior works both quantitatively and qualitatively, and is capable of producing photorealistic results for AR applications such as virtual object insertion even for highly specular objects. | Zian Wang, Jonah Philion, Sanja Fidler, Jan Kautz; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12538-12547 | null | null | 2,021 | iccv |
Cloud Transformers: A Universal Approach to Point Cloud Processing Tasks | null | We present a new versatile building block for deep point cloud processing architectures that is equally suited for diverse tasks. This building block combines the ideas of spatial transformers and multi-view convolutional networks with the efficiency of standard convolutional layers in two and three-dimensional dense grids. The new block operates via multiple parallel heads, whereas each head differentiably rasterizes feature representations of individual points into a low-dimensional space, and then uses dense convolution to propagate information across points. The results of the processing of individual heads are then combined together resulting in the update of point features. Using the new block, we build architectures for both discriminative (point cloud segmentation, point cloud classification) and generative (point cloud inpainting and image-based point cloud reconstruction) tasks. The resulting architectures achieve state-of-the-art performance for these tasks, demonstrating the versatility of the new block for point cloud processing. | Kirill Mazur, Victor Lempitsky; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10715-10724 | null | null | 2,021 | iccv |
DeepGaze IIE: Calibrated Prediction in and Out-of-Domain for State-of-the-Art Saliency Modeling | null | Since 2014 transfer learning has become the key driver for the improvement of spatial saliency prediction - however, with stagnant progress in the last 3-5 years. We conduct a large-scale transfer learning study which tests different ImageNet backbones, always using the same read out architecture and learning protocol adopted from DeepGaze II. By replacing the VGG19 backbone of DeepGaze II with ResNet50 features we improve the performance on saliency prediction from 78% to 85%. However, as we continue to test better ImageNet models as backbones - such as EfficientNetB5 - we observe no additional improvement on saliency prediction. By analyzing the backbones further, we find that generalization to other datasets differs substantially, with models being consistently overconfident in their fixation predictions. We show that by combining multiple backbones in a principled manner a good confidence calibration on unseen datasets can be achieved. This new model "DeepGaze IIE" yields a significant leap in benchmark performance in and out-of-domain with a 15 percent point improvement over DeepGaze II to 93% on MIT1003, marking a new state of the art on the MIT/Tuebingen Saliency Benchmark in all available metrics (AUC: 88.3%, sAUC: 79.4%, CC: 82.4%). | Akis Linardos, Matthias Kümmerer, Ori Press, Matthias Bethge; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12919-12928 | null | null | 2,021 | iccv |
Unsupervised Dense Deformation Embedding Network for Template-Free Shape Correspondence | null | Shape correspondence from 3D deformation learning has attracted appealing academy interests recently. Nevertheless, current deep learning based methods require the supervision of dense annotations to learn per-point translations, which severely over-parameterize the deformation process. Moreover, they fail to capture local geometric details of original shape via global feature embedding. To address these challenges, we develop a new Unsupervised Dense Deformation Embedding Network (i.e., UD2E-Net), which learns to predict deformations between non-rigid shapes from dense local features. Since it is non-trivial to match deformation-variant local features for deformation prediction, we develop an Extrinsic-Intrinsic Autoencoder to frst encode extrinsic geometric features from source into intrinsic coordinates in a shared canonical shape, with which the decoder then synthesizes corresponding target features. Moreover, a bounded maximum mean discrepancy loss is developed to mitigate the distribution divergence between the synthesized and original features. To learn natural deformation without dense supervision, we introduce a coarse parameterized deformation graph, for which a novel trace and propagation algorithm is proposed to improve both the quality and effciency of the deformation. Our UD2E-Net outperforms state-of-the-art unsupervised methods by 24% on Faust Inter challenge and even supervised methods by 13% on Faust Intra challenge. | Ronghan Chen, Yang Cong, Jiahua Dong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8361-8370 | null | null | 2,021 | iccv |
Learning To Drive From a World on Rails | null | We learn an interactive vision-based driving policy from pre-recorded driving logs via a model-based approach. A forward model of the world supervises a driving policy that predicts the outcome of any potential driving trajectory. To support learning from pre-recorded logs, we assume that the world is on rails, meaning neither the agent nor its actions influence the environment. This assumption greatly simplifies the learning problem, factorizing the dynamics into a nonreactive world model and a low-dimensional and compact forward model of the ego-vehicle. Our approach computes action-values for each training trajectory using a tabular dynamic-programming evaluation of the Bellman equations; these action-values in turn supervise the final vision-based driving policy. Despite the world-on-rails assumption, the final driving policy acts well in a dynamic and reactive world. It outperforms imitation learning as well as model-based and model-free reinforcement learning on the challenging CARLA NoCrash benchmark. It is also an order of magnitude more sample-efficient than state-of-the-art model-free reinforcement learning techniques on navigational tasks in the ProcGen benchmark. | Dian Chen, Vladlen Koltun, Philipp Krähenbühl; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15590-15599 | null | null | 2,021 | iccv |
Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies | null | This paper addresses the challenge of reconstructing an animatable human model from a multi-view video. Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images. However, they represent the deformation field as translational vector field or SE(3) field, which makes the optimization highly under-constrained. Moreover, these representations cannot be explicitly controlled by input motions. Instead, we introduce neural blend weight fields to produce the deformation fields. Based on the skeleton-driven deformation, blend weight fields are used with 3D human skeletons to generate observation-to-canonical and canonical-to-observation correspondences. Since 3D human skeletons are more observable, they can regularize the learning of deformation fields. Moreover, the learned blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model. Experiments show that our approach significantly outperforms recent human synthesis methods. The code and supplementary materials are available at \href https://zju3dv.github.io/animatable_nerf/ https://zju3dv.github.io/animatable_nerf/ . | Sida Peng, Junting Dong, Qianqian Wang, Shangzhan Zhang, Qing Shuai, Xiaowei Zhou, Hujun Bao; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14314-14323 | null | null | 2,021 | iccv |
Continual Neural Mapping: Learning an Implicit Scene Representation From Sequential Observations | null | Recent advances have enabled a single neural network to serve as an implicit scene representation, establishing the mapping function between spatial coordinates and scene properties. In this paper, we make a further step towards continual learning of the implicit scene representation directly from sequential observations, namely Continual Neural Mapping. The proposed problem setting bridges the gap between batch-trained implicit neural representations and commonly used streaming data in robotics and vision communities. We introduce an experience replay approach to tackle an exemplary task of continual neural mapping: approximating a continuous signed distance function (SDF) from sequential depth images as a scene geometry representation. We show for the first time that a single network can represent scene geometry over time continually without catastrophic forgetting, while achieving promising trade-offs between accuracy and efficiency. | Zike Yan, Yuxin Tian, Xuesong Shi, Ping Guo, Peng Wang, Hongbin Zha; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15782-15792 | null | null | 2,021 | iccv |
Going Deeper With Image Transformers | null | Transformers have been recently adapted for large scale image classification, achieving high scores shaking up the long supremacy of convolutional neural networks. However the optimization of vision transformers has been little studied so far. In this work, we build and optimize deeper transformer networks for image classification. In particular, we investigate the interplay of architecture and optimization of such dedicated transformers. We make two architecture changes that significantly improve the accuracy of deep transformers. This leads us to produce models whose performance does not saturate early with more depth, for instance we obtain 86.5% top-1 accuracy on Imagenet when training with no external data, we thus attain the current sate of the art with less floating-point operations and parameters. Our best model establishes the new state of the art on Imagenet with Reassessed labels and Imagenet-V2 / match frequency, in the setting with no additional training data. We share our code and models | Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, Hervé Jégou; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 32-42 | null | null | 2,021 | iccv |
Exploring Relational Context for Multi-Task Dense Prediction | null | The timeline of computer vision research is marked with advances in learning and utilizing efficient contextual representations. Most of them, however, are targeted at improving model performance on a single downstream task. We consider a multi-task environment for dense prediction tasks, represented by a common backbone and independent task-specific heads. Our goal is to find the most efficient way to refine each task prediction by capturing cross-task contexts dependent on tasks' relations. We explore various attention-based contexts, such as global and local, in the multi-task setting and analyze their behavior when applied to refine each task independently. Empirical findings confirm that different source-target task pairs benefit from different context types. To automate the selection process, we propose an Adaptive Task-Relational Context (ATRC) module, which samples the pool of all available contexts for each task pair using neural architecture search and outputs the optimal configuration for deployment. Our method achieves state-of-the-art performance on two important multi-task benchmarks, namely NYUD-v2 and PASCAL-Context. The proposed ATRC has a low computational toll and can be used as a drop-in refinement module for any supervised multi-task architecture. | David Brüggemann, Menelaos Kanakis, Anton Obukhov, Stamatios Georgoulis, Luc Van Gool; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15869-15878 | null | null | 2,021 | iccv |
TravelNet: Self-Supervised Physically Plausible Hand Motion Learning From Monocular Color Images | null | This paper aims to reconstruct physically plausible hand motion from monocular color images. Existing frame-by-frame estimating approaches can not guarantee the physical plausibility (e.g. penetration, jittering) directly. In this paper, we embed physical constraints on the per-frame estimated motions in both spatial and temporal space. Our key idea is to adopt a self-supervised learning strategy to train a novel encoder-decoder, named TravelNet, whose training motion data is prepared by the physics engine using discrete pose states. TravelNet captures key pose states from hand motion sequences as compact motion descriptors, inspired by the concept of keyframes in animation. Finally, it manages to extract those key states out of perturbations without manual annotations, and reconstruct the motions preserving details and physical plausibility. In the experiments, we show that the outputs of the TravelNet contain both finger synergism and time consistency. Through the proposed framework, hand motions can be accurately reconstructed and flexibly re-edited, which is superior to the state-of-the-art methods. | Zimeng Zhao, Xi Zhao, Yangang Wang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11666-11676 | null | null | 2,021 | iccv |
Knowledge Mining and Transferring for Domain Adaptive Object Detection | null | With the thriving of deep learning, CNN-based object detectors have made great progress in the past decade. However, the domain gap between training and testing data leads to a prominent performance degradation and thus hinders their application in the real world. To alleviate this problem, Knowledge Transfer Network (KTNet) is proposed as a new paradigm for domain adaption. Specifically, KTNet is constructed on a base detector with intrinsic knowledge mining and relational knowledge constraints. First, we design a foreground/background classifier shared by source domain and target domain to extract the common attribute knowledge of objects in different scenarios. Second, we model the relational knowledge graph and explicitly constrain the consistency of category correlation under source domain, target domain, as well as cross-domain conditions. As a result, the detector is guided to learn object-related and domain-independent representation. Extensive experiments and visualizations confirm that transferring object-specific knowledge can yield notable performance gains. The proposed KTNet achieves state-of-the-art results on three cross-domain detection benchmarks. | Kun Tian, Chenghao Zhang, Ying Wang, Shiming Xiang, Chunhong Pan; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9133-9142 | null | null | 2,021 | iccv |
Joint Visual Semantic Reasoning: Multi-Stage Decoder for Text Recognition | null | Although text recognition has significantly evolved over the years, state-of the-art (SOTA) models still struggle in the wild scenarios due to complex backgrounds, varying fonts, uncontrolled illuminations, distortions and other artifacts. This is because such models solely depend on visual information for text recognition, thus lacking semantic reasoning capabilities. In this paper, we argue that semantic information offers a complementary role in addition to visual only. More specifically, we additionally utilize semantic information by proposing a multi-stage multi-scale attentional decoder that performs joint visual-semantic reasoning. Our novelty lies in the intuition that for text recognition, prediction should be refined in a stage-wise manner. Therefore our key contribution is in designing a stage-wise unrolling attentional decoder where non-differentiability, invoked by discretely predicted character labels, needs to be bypassed for end-to-end training. While the first stage predicts using visual features, subsequent stages refine on-top of it using joint visual-semantic information. Additionally, we introduce multi-scale 2D attention along with dense and residual connections between different stages to deal with varying scales of character sizes, for better performance and faster convergence during training. Experimental results show our approach to outperform existing SOTA methods by a considerable margin. | Ayan Kumar Bhunia, Aneeshan Sain, Amandeep Kumar, Shuvozit Ghose, Pinaki Nath Chowdhury, Yi-Zhe Song; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14940-14949 | null | null | 2,021 | iccv |
A General Recurrent Tracking Framework Without Real Data | null | Recent progress in multi-object tracking (MOT) has shown great significance of a robust scoring mechanism for potential tracks. However, the lack of available data in MOT makes it difficult to learn a general scoring mechanism. Multiple cues including appearance, motion and etc., are limitedly utilized in current manual scoring functions. In this paper, we propose a Multiple Nodes Tracking (MNT) framework that adapts to most trackers. Based on this framework, a Recurrent Tracking Unit (RTU) is designed to score potential tracks through long-term information. In addition, we present a method of generating simulated tracking data without real data to overcome the defect of limited available data in MOT. The experiments demonstrate that our simulated tracking data is effective for training RTU and achieves state-of-the-art performance on both MOT17 and MOT16 benchmarks. Meanwhile, RTU can be flexibly plugged into classic trackers such as DeepSORT and MHT, and makes remarkable improvements as well. | Shuai Wang, Hao Sheng, Yang Zhang, Yubin Wu, Zhang Xiong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13219-13228 | null | null | 2,021 | iccv |
Dynamic Dual Gating Neural Networks | null | In dynamic neural networks that adapt computations to different inputs, gating-based methods have demonstrated notable generality and applicability in trading-off the model complexity and accuracy. However, existing works only explore the redundancy from a single point of the network, limiting the performance. In this paper, we propose dual gating, a new dynamic computing method, to reduce the model complexity at run-time. For each convolutional block, dual gating identifies the informative features along two separate dimensions, spatial and channel. Specifically, the spatial gating module estimates which areas are essential, and the channel gating module predicts the salient channels that contribute more to the results. Then the computation of both unimportant regions and irrelevant channels can be skipped dynamically during inference. Extensive experiments on a variety of datasets demonstrate that our method can achieve higher accuracy under similar computing budgets compared with other dynamic execution methods. In particular, dynamic dual gating can provide 59.7% saving in computing of ResNet50 with 76.41% top-1 accuracy on ImageNet, which has advanced the state-of-the-art. | Fanrong Li, Gang Li, Xiangyu He, Jian Cheng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5330-5339 | null | null | 2,021 | iccv |
FFT-OT: A Fast Algorithm for Optimal Transportation | null | An optimal transportation map finds the most economical way to transport one probability measure to the other. It has been applied in a broad range of applications in vision, deep learning and medical images. By Brenier theory, computing the optimal transport map is equivalent to solving a Monge-Ampere equation. Due to the highly non-linear nature, the computation of optimal transportation maps in large scale is very challenging. This work proposes a simple but powerful method, the FFT-OT algorithm, to tackle this difficulty based on three key ideas. First, solving Monge-Ampere equation is converted to a fixed point problem; Second, the obliqueness property of optimal transportation maps are reformulated as Neumann boundary conditions on rectangular domains; Third, FFT is applied in each iteration to solve a Poisson equation in order to improve the efficiency. Experiments on surfaces captured from 3D scanning and reconstructed from medical imaging are conducted, and compared with other existing methods. Our experimental results show that the proposed FFT-OT algorithm is simple, general and scalable with high efficiency and accuracy. | Na Lei, Xianfeng Gu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6280-6289 | null | null | 2,021 | iccv |
Video Self-Stitching Graph Network for Temporal Action Localization | null | Temporal action localization (TAL) in videos is a challenging task, especially due to the large variation in action temporal scales. Short actions usually occupy a major proportion in the datasets, but tend to have the lowest performance. In this paper, we confront the challenge of short actions and propose a multi-level cross-scale solution dubbed as video self-stitching graph network (VSGN). We have two key components in VSGN: video self-stitching (VSS) and cross-scale graph pyramid network (xGPN). In VSS, we focus on a short period of a video and magnify it along the temporal dimension to obtain a larger scale. We stitch the original clip and its magnified counterpart in one input sequence to take advantage of the complementary properties of both scales. The xGPN component further exploits the cross-scale correlations by a pyramid of cross-scale graph networks, each containing a hybrid module to aggregate features from across scales as well as within the same scale. Our VSGN not only enhances the feature representations, but also generates more positive anchors for short actions and more short training samples. Experiments demonstrate that VSGN obviously improves the localization performance of short actions as well as achieving the state-of-the-art overall performance on THUMOS-14 and ActivityNet-v1.3. | Chen Zhao, Ali K. Thabet, Bernard Ghanem; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13658-13667 | null | null | 2,021 | iccv |
Gravity-Aware Monocular 3D Human-Object Reconstruction | null | This paper proposes GraviCap, i.e., a new approach for joint markerless 3D human motion capture and object trajectory estimation from monocular RGB videos. We focus on scenes with objects partially observed during a free flight. In contrast to existing monocular methods, we can recover scale, object trajectories as well as human bone lengths in meters and the ground plane's orientation, thanks to the awareness of the gravity constraining object motions. Our objective function is parametrised by the object's initial velocity and position, gravity direction and focal length, and jointly optimised for one or several free flight episodes. The proposed human-object interaction constraints ensure geometric consistency of the 3D reconstructions and improved physical plausibility of human poses compared to the unconstrained case. We evaluate GraviCap on a new dataset with ground-truth annotations for persons and different objects undergoing free flights. In the experiments, our approach achieves state-of-the-art accuracy in 3D human motion capture on various metrics. We urge the reader to watch our supplementary video. Both the source code and the dataset are released; see http://4dqv.mpi-inf.mpg.de/GraviCap/. | Rishabh Dabral, Soshi Shimada, Arjun Jain, Christian Theobalt, Vladislav Golyanik; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12365-12374 | null | null | 2,021 | iccv |
Zero-Shot Natural Language Video Localization | null | Understanding videos to localize moments with natural language often requires large expensive annotated video regions paired with language queries. To eliminate the annotation costs, we make a first attempt to train a natural language video localization model in zero-shot manner. Inspired by unsupervised image captioning setup, we merely require random text corpora, unlabeled video collections, and an off-the-shelf object detector to train a model. With the unrelated and unpaired data, we propose to generate pseudo-supervision of candidate temporal regions and corresponding query sentences, and develop a simple NLVL model to train with the pseudo-supervision. Our empirical validations show that the proposed pseudo-supervised method outperforms several baseline approaches and a number of methods using stronger supervision on Charades-STA and ActivityNet-Captions. | Jinwoo Nam, Daechul Ahn, Dongyeop Kang, Seong Jong Ha, Jonghyun Choi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1470-1479 | null | null | 2,021 | iccv |
MFNet: Multi-Filter Directive Network for Weakly Supervised Salient Object Detection | null | Weakly supervised salient object detection (WSOD) targets to train a CNNs-based saliency network using only low-cost annotations. Existing WSOD methods take various techniques to pursue single "high-quality" pseudo label from low-cost annotations and then develop their saliency networks. Though these methods have achieved good performance, the generated single label is inevitably affected by adopted refinement algorithms and shows prejudiced characteristics which further influence the saliency networks. In this work, we introduce a new multiple-pseudo label framework to integrate more comprehensive and accurate saliency cues from multiple labels, avoiding the aforementioned problem. Specifically, we propose a multi-filer directive network (MFNet) including a saliency network as well as multiple directive filters. The directive filter (DF) is designed to extract and filter more accurate saliency cues from the noisy pseudo labels. The multiple accurate cues from multiple DFs are then simultaneously propagated to the saliency network with a multi-guidance loss. Extensive experiments on five datasets over four metrics demonstrate that our method outperforms all the existing congeneric methods. Moreover, it is also worth noting that our framework is flexible enough to apply to existing methods and improve their performance. | Yongri Piao, Jian Wang, Miao Zhang, Huchuan Lu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4136-4145 | null | null | 2,021 | iccv |
On the Importance of Distractors for Few-Shot Classification | null | Few-shot classification aims at classifying categories of a novel task by learning from just a few (typically, 1 to 5) labeled examples. An effective approach to few-shot classification involves a prior model trained on a large-sample base domain, which is then finetuned over the novel few-shot task to yield generalizable representations. However, task-specific finetuning is prone to overfitting due to the lack of enough training examples. To alleviate this issue, we propose a new finetuning approach based on contrastive learning that reuses unlabelled examples from the base domain in the form of distractors. Unlike the nature of unlabelled data used in prior works, distractors belong to classes that do not overlap with the novel categories. We demonstrate for the first time that the inclusion of such distractors can significantly boost few-shot generalization. Our technical novelty includes a stochastic pairing of examples sharing the same category in the few-shot task and a weighting term that controls the relative influence of task-specific negatives and distractors. An important aspect of our finetuning objective is that it is agnostic to distractor labels and hence applicable to various base domain settings. More precisely, compared to state-of-the-art approaches, our method shows accuracy gains of up to 12% in cross-domain and up to 5% in unsupervised prior-learning settings. Our code is available at https://github.com/quantacode/Contrastive-Finetuning.git | Rajshekhar Das, Yu-Xiong Wang, José M. F. Moura; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9030-9040 | null | null | 2,021 | iccv |
Deep Halftoning With Reversible Binary Pattern | null | Existing halftoning algorithms usually drop colors and fine details when dithering color images with binary dot patterns, which makes it extremely difficult to recover the original information. To dispense the recovery trouble in future, we propose a novel halftoning technique that converts a color image into binary halftone with full restorability to the original version. The key idea is to implicitly embed those previously dropped information into the halftone patterns. So, the halftone pattern not only serves to reproduce the image tone, maintain the blue-noise randomness, but also represents the color information and fine details. To this end, we exploit two collaborative convolutional neural networks (CNNs) to learn the dithering scheme, under a non-trivial self-supervision formulation. To tackle the flatness degradation issue of CNNs, we propose a novel noise incentive block (NIB) that can serve as a generic CNN plug-in for performance promotion. At last, we tailor a guiding-aware training scheme that secures the convergence direction as regulated. We evaluate the invertible halftones in multiple aspects, which evidences the effectiveness of our method. | Menghan Xia, Wenbo Hu, Xueting Liu, Tien-Tsin Wong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14000-14009 | null | null | 2,021 | iccv |
UltraPose: Synthesizing Dense Pose With 1 Billion Points by Human-Body Decoupling 3D Model | null | Recovering dense human poses from images plays a critical role in establishing an image-to-surface correspondence between RGB images and the 3D surface of the human body, serving the foundation of rich real-world applications, such as virtual humans, monocular-to-3d reconstruction. However, the popular DensePose-COCO dataset relies on a sophisticated manual annotation system, leading to severe limitations in acquiring the denser and more accurate annotated pose resources. In this work, we introduce a new 3D human-body model with a series of decoupled parameters that could freely control the generation of the body. Furthermore, we build a data generation system based on this decoupling 3D model, and construct an ultra dense synthetic benchmark UltraPose, containing around 1.3 billion corresponding points. Compared to the existing manually annotated DensePose-COCO dataset, the synthetic UltraPose has ultra dense image-to-surface correspondences without annotation cost and error. Our proposed UltraPose provides the largest benchmark and data resources for lifting the model capability in predicting more accurate dense poses. To promote future researches in this field, we also propose a transformer-based method to model the dense correspondence between 2D and 3D worlds. The proposed model trained on synthetic UltraPose can be applied to real-world scenarios, indicating the effectiveness of our benchmark and model. | Haonan Yan, Jiaqi Chen, Xujie Zhang, Shengkai Zhang, Nianhong Jiao, Xiaodan Liang, Tianxiang Zheng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10891-10900 | null | null | 2,021 | iccv |
Hand Image Understanding via Deep Multi-Task Learning | null | Analyzing and understanding hand information from multimedia materials like images or videos is important for many real world applications and remains to be very active in research community. There are various works focusing on recovering hand information from single image, however, they usually solve a single task, for example, hand mask segmentation, 2D/3D hand pose estimation, or hand mesh reconstruction and perform not well in challenging scenarios. To further improve the performance of these tasks, we propose a novel Hand Image Understanding (HIU) framework (HIU-DMTL) to extract comprehensive information of the hand object from a single RGB image, by jointly considering the relationships between these tasks. To achieve this goal, a cascaded multi-task learning (MTL) backbone is designed to estimate the 2D heat maps, to learn the segmentation mask, and to generate the intermediate 3D information encoding, followed by a coarse-to-fine learning paradigm and a self-supervised learning strategy. Qualitative experiments demonstrate that our approach is capable of recovering reasonable mesh representations even in challenging situations. Quantitatively, our method significantly outperforms the state-of-the-art approaches on various widely-used datasets, in terms of diverse evaluation metrics. | Xiong Zhang, Hongsheng Huang, Jianchao Tan, Hongmin Xu, Cheng Yang, Guozhu Peng, Lei Wang, Ji Liu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11281-11292 | null | null | 2,021 | iccv |
GroupFormer: Group Activity Recognition With Clustered Spatial-Temporal Transformer | null | Group activity recognition is a crucial yet challenging problem, whose core lies in fully exploring spatial-temporal interactions among individuals and generating reasonable group representations. However, previous methods either model spatial and temporal information separately, or directly aggregate individual features to form group features. To address these issues, we propose a novel group activity recognition network termed GroupFormer. It captures spatial-temporal contextual information jointly to augment the individual and group representations effectively with a clustered spatial-temporal transformer. Specifically, our GroupFormer has three appealing advantages: (1) A tailor-modified Transformer, Clustered Spatial-Temporal Transformer, is proposed to enhance the individual and group representation. (2) It models the spatial and temporal dependencies integrally and utilizes decoders to build the bridge between the spatial and temporal information. (3) A clustered attention mechanism is utilized to dynamically divide individuals into multiple clusters for better learning activity-aware semantic representations. Moreover, experimental results show that the proposed framework outperforms state-of-the-art methods on the Volleyball dataset and Collective Activity dataset. | Shuaicheng Li, Qianggang Cao, Lingbo Liu, Kunlin Yang, Shinan Liu, Jun Hou, Shuai Yi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13668-13677 | null | null | 2,021 | iccv |
DRIVE: Deep Reinforced Accident Anticipation With Visual Explanation | null | Traffic accident anticipation aims to accurately and promptly predict the occurrence of a future accident from dashcam videos, which is vital for a safety-guaranteed self-driving system. To encourage an early and accurate decision, existing approaches typically focus on capturing the cues of spatial and temporal context before a future accident occurs. However, their decision-making lacks visual explanation and ignores the dynamic interaction with the environment. In this paper, we propose Deep ReInforced accident anticipation with Visual Explanation, named DRIVE. The method simulates both the bottom-up and top-down visual attention mechanism in a dashcam observation environment so that the decision from the proposed stochastic multi-task agent can be visually explained by attentive regions. Moreover, the proposed dense anticipation reward and sparse fixation reward are effective in training the DRIVE model with our improved reinforcement learning algorithm. Experimental results show that the DRIVE model achieves state-of-the-art performance on multiple real-world traffic accident datasets. Code and pre-trained models are available at https://www.rit.edu/actionlab/drive. | Wentao Bao, Qi Yu, Yu Kong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 7619-7628 | null | null | 2,021 | iccv |
BAPA-Net: Boundary Adaptation and Prototype Alignment for Cross-Domain Semantic Segmentation | null | Existing cross-domain semantic segmentation methods usually focus on the overall segmentation results of whole objects but neglect the importance of object boundaries. In this work, we find that the segmentation performance can be considerably boosted if we treat object boundaries properly. For that, we propose a novel method called BAPA-Net, which is based on a convolutional neural network via Boundary Adaptation and Prototype Alignment, under the unsupervised domain adaptation setting. Specifically, we first construct additional images by pasting objects from source images to target images, and we develop a so-called boundary adaptation module to weigh each pixel based on its distance to the nearest boundary pixel of those pasted source objects. Moreover, we pro- pose another prototype alignment module to reduce the domain mismatch by minimizing distances between the class prototypes of the source and target domains, where boundaries are removed to avoid domain confusion during prototype calculation. By integrating the boundary adaptation and prototype alignment, we are able to train a discriminative and domain-invariant model for cross-domain semantic segmentation. We conduct extensive experiments on the benchmark datasets of urban scenes (i.e., GTA5->Cityscapes and SYNTHIA->Cityscapes). And the promising results clearly show the effectiveness of our BAPA-Net method over existing state-of-the-art for cross-domain semantic segmentation. Our implementation is available at https://github.com/manmanjun/BAPA-Net. | Yahao Liu, Jinhong Deng, Xinchen Gao, Wen Li, Lixin Duan; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8801-8811 | null | null | 2,021 | iccv |
Multispectral Illumination Estimation Using Deep Unrolling Network | null | This paper examines the problem of illumination spectra estimation in multispectral images. We cast the problem into a constrained matrix factorization problem and present a method for both single-global and multiple illumination estimation in which a deep unrolling network is constructed from the alternating direction method of multipliers(ADMM) optimization for solving the matrix factorization problem. To alleviate the lack of multispectral training data, we build a large multispectral reflectance image dataset for generating synthesized data and use them for training and evaluating our model. The results of simulations and real experiments demonstrate that the proposed method is able to outperform state-of-the-art spectral illumination estimation methods, and that it generalizes well to a wide variety of scenes and spectra. | Yuqi Li, Qiang Fu, Wolfgang Heidrich; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2672-2681 | null | null | 2,021 | iccv |
BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation | null | Semantic segmentation aims to predict pixel-level labels. It has become a popular task in various computer vision applications. While fully supervised segmentation methods have achieved high accuracy on large-scale vision datasets, they are unable to generalize on a new test environment or a new domain well. In this work, we first introduce a new Un- aligned Domain Score to measure the efficiency of a learned model on a new target domain in unsupervised manner. Then, we present the new Bijective Maximum Likelihood (BiMaL) loss that is a generalized form of the Adversarial Entropy Minimization without any assumption about pixel independence. We have evaluated the proposed BiMaL on two domains. The proposed BiMaL approach consistently outperforms the SOTA methods on empirical experiments on "SYNTHIA to Cityscapes", "GTA5 to Cityscapes", and "SYNTHIA to Vistas". | Thanh-Dat Truong, Chi Nhan Duong, Ngan Le, Son Lam Phung, Chase Rainwater, Khoa Luu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8548-8557 | null | null | 2,021 | iccv |
Topic Scene Graph Generation by Attention Distillation From Caption | null | If an image tells a story, the image caption is the briefest narrator. Generally, a scene graph prefers to be an omniscient "generalist", while the image caption is more willing to be a "specialist", which outlines the gist. Lots of previous studies have found that a scene graph is not as practical as expected unless it can reduce the trivial contents and noises. In this respect, the image caption is a good tutor. To this end, we let the scene graph borrow the ability from the image caption so that it can be a specialist on the basis of remaining all-around, resulting in the so-called Topic Scene Graph. What an image caption pays attention to is distilled and passed to the scene graph for estimating the importance of partial objects, relationships, and events. Specifically, during the caption generation, the attention about individual objects in each time step is collected, pooled, and assembled to obtain the attention about relationships, which serves as weak supervision for regularizing the estimated importance scores of relationships. In addition, as this attention distillation process provides an opportunity for combining the generation of image caption and scene graph together, we further transform the scene graph into linguistic form with rich and free-form expressions by sharing a single generation model with image caption. Experiments show that attention distillation brings significant improvements in mining important relationships without strong supervision, and the topic scene graph shows great potential in subsequent applications. | Wenbin Wang, Ruiping Wang, Xilin Chen; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15900-15910 | null | null | 2,021 | iccv |
Contrastive Learning for Label Efficient Semantic Segmentation | null | Collecting labeled data for the task of semantic segmentation is expensive and time-consuming, as it requires dense pixel-level annotations. While recent Convolutional Neural Network (CNN) based semantic segmentation approaches have achieved impressive results by using large amounts of labeled training data, their performance drops significantly as the amount of labeled data decreases. This happens because deep CNNs trained with the de facto cross-entropy loss can easily overfit to small amounts of labeled data. To address this issue, we propose a simple and effective contrastive learning-based training strategy in which we first pretrain the network using a pixel-wise, label-based contrastive loss, and then fine-tune it using the cross-entropy loss. This approach increases intra-class compactness and inter-class separability, thereby resulting in a better pixel classifier. We demonstrate the effectiveness of the proposed training strategy using the Cityscapes and PASCAL VOC 2012 segmentation datasets. Our results show that pretraining with the proposed contrastive loss results in large performance gains (more than 20% absolute improvement in some settings) when the amount of labeled data is limited. In many settings, the proposed contrastive pretraining strategy, which does not use any additional data, is able to match or outperform the widely-used ImageNet pretraining strategy that uses more than a million additional labeled images. | Xiangyun Zhao, Raviteja Vemulapalli, Philip Andrew Mansfield, Boqing Gong, Bradley Green, Lior Shapira, Ying Wu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10623-10633 | null | null | 2,021 | iccv |
Progressive Correspondence Pruning by Consensus Learning | null | Correspondence pruning aims to correctly remove false matches (outliers) from an initial set of putative correspondences. The selection is challenging since putative matches are typically extremely unbalanced, largely dominated by outliers, and the random distribution of such outliers further complicates the learning process for learning-based methods. To address this issue, we propose to progressively prune the correspondences via a local-to-global consensus learning procedure. We introduce a "pruning" block that lets us identify reliable candidates among the initial matches according to consensus scores estimated using local-to-global dynamic graphs. We then achieve progressive pruning by stacking multiple pruning blocks sequentially. Our method outperforms state-of-the-arts on robust line fitting, camera pose estimation and retrieval-based image localization benchmarks by significant margins and shows promising generalization ability to different datasets and detector/descriptor combinations. | Chen Zhao, Yixiao Ge, Feng Zhu, Rui Zhao, Hongsheng Li, Mathieu Salzmann; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6464-6473 | null | null | 2,021 | iccv |
Learning Specialized Activation Functions With the Piecewise Linear Unit | null | The choice of activation functions is crucial for modern deep neural networks. Popular hand-designed activation functions like Rectified Linear Unit(ReLU) and its variants show promising performance in various tasks and models. Swish, the automatically discovered activation function, outperforms ReLU on many challenging datasets. However, it has two main drawbacks. First, the tree-based search space is highly discrete and restricted, making it difficult to searching. Second, the sample-based searching method is inefficient, making it infeasible to find specialized activation functions for each dataset or neural architecture. To tackle these drawbacks, we propose a new activation function called Piecewise Linear Unit(PWLU), which incorporates a carefully designed formulation and learning method. It can learn specialized activation functions and achieves SOTA performance on large-scale datasets like ImageNet and COCO. For example, on ImageNet classification dataset, PWLU improves 0.9%/0.53%/1.0%/1.7%/1.0% top-1 accuracy over Swish for ResNet-18/ResNet-50/MobileNet-V2/MobileNet-V3/EfficientNet-B0. PWLU is also easy to implement and efficient at inference, which can be widely applied in real-world applications. | Yucong Zhou, Zezhou Zhu, Zhao Zhong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12095-12104 | null | null | 2,021 | iccv |
TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild | null | Joint forecasting of human trajectory and pose dynamics is a fundamental building block of various applications ranging from robotics and autonomous driving to surveillance systems. Predicting body dynamics requires capturing subtle information embedded in the humans' interactions with each other and with the objects present in the scene. In this paper, we propose a novel TRajectory and POse Dynamics (nicknamed TRiPOD) method based on graph attentional networks to model the human-human and human-object interactions both in the input space and the output space (decoded future output). The model is supplemented by a message passing interface over the graphs to fuse these different levels of interactions efficiently. Furthermore, to incorporate a real-world challenge, we propound to learn an indicator representing whether an estimated body joint is visible/invisible at each frame, e.g. due to occlusion or being outside the sensor field of view. Finally, we introduce a new benchmark for this joint task based on two challenging datasets (PoseTrack and 3DPW) and propose evaluation metrics to measure the effectiveness of predictions in the global space, even when there are invisible cases of joints. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks. | Vida Adeli, Mahsa Ehsanpour, Ian Reid, Juan Carlos Niebles, Silvio Savarese, Ehsan Adeli, Hamid Rezatofighi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13390-13400 | null | null | 2,021 | iccv |
Just a Few Points Are All You Need for Multi-View Stereo: A Novel Semi-Supervised Learning Method for Multi-View Stereo | null | While learning-based multi-view stereo (MVS) methods have recently shown successful performances in quality and efficiency, limited MVS data hampers generalization to unseen environments. A simple solution is to generate various large-scale MVS datasets, but generating dense ground truth for 3D structure requires a huge amount of time and resources. On the other hand, if the reliance on dense ground truth is relaxed, MVS systems will generalize more smoothly to new environments. To this end, we first introduce a novel semi-supervised multi-view stereo framework called a Sparse Ground truth-based MVS Network (SGT-MVSNet) that can reliably reconstruct the 3D structures even with a few ground truth 3D points. Our strategy is to divide the accurate and erroneous regions and individually conquer them based on our observation that a probability map can separate these regions. We propose a self-supervision loss called the 3D Point Consistency Loss to enhance the 3D reconstruction performance, which forces the 3D points back-projected from the corresponding pixels by the predicted depth values to meet at the same 3D coordinates. Finally, we propagate these improved depth predictions toward edges and occlusions by the Coarse-to-fine Reliable Depth Propagation module. We generate the spare ground truth of the DTU dataset for evaluation and extensive experiments verify that our SGT-MVSNet outperforms the state-of-the-art MVS methods on the sparse ground truth setting. Moreover, our method shows comparable reconstruction results to the supervised MVS methods though we only used tens and hundreds of ground truth 3D points. | Taekyung Kim, Jaehoon Choi, Seokeon Choi, Dongki Jung, Changick Kim; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6178-6186 | null | null | 2,021 | iccv |
Robust Small Object Detection on the Water Surface Through Fusion of Camera and Millimeter Wave Radar | null | In recent years, unmanned surface vehicles (USVs) have been experiencing growth in various applications. With the expansion of USVs' application scenes from the typical marine areas to inland waters, new challenges arise for the object detection task, which is an essential part of the perception system of USVs. In our work, we focus on a relatively unexplored task for USVs in inland waters: small object detection on water surfaces, which is of vital importance for safe autonomous navigation and USVs' certain missions such as floating waste cleaning. Considering the limitations of vision-based object detection, we propose a novel vision-radar fusion based method for robust small object detection on water surfaces. By using a novel representation format of millimeter wave radar point clouds and applying a deep-level multi-scale fusion of RGB images and radar data, the proposed method can efficiently utilize the characteristics of radar data and improve the accuracy and robustness for small object detection on water surfaces. We test the method on the real-world floating bottle dataset that we collected and released. The result shows that, our method improves the average detection accuracy significantly compared to the vision-based methods and achieves state-of-the-art performance. Besides, the proposed method performs robustly when single sensor degrades. | Yuwei Cheng, Hu Xu, Yimin Liu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15263-15272 | null | null | 2,021 | iccv |
Video Autoencoder: Self-Supervised Disentanglement of Static 3D Structure and Motion | null | We present Video Autoencoder for learning disentangled representations of 3D structure and camera pose from videos in a self-supervised manner. Relying on temporal continuity in videos, our work assumes that the 3D scene structure in nearby video frames remains static. Given a sequence of video frames as input, the Video Autoencoder extracts a disentangled representation of the scene including: (i) a temporally-consistent deep voxel feature to represent the 3D structure and (ii) a 3D trajectory of camera poses for each frame. These two representations will then be re-entangled for rendering the input video frames. Video Autoencoder can be trained directly using a pixel reconstruction loss, without any ground truth 3D or camera pose annotations. The disentangled representation can be applied to a range of tasks, including novel view synthesis, camera pose estimation, and video generation by motion following. We evaluate our method on several large-scale natural video datasets, and show generalization results on out-of-domain images. | Zihang Lai, Sifei Liu, Alexei A. Efros, Xiaolong Wang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9730-9740 | null | null | 2,021 | iccv |
Distilling Global and Local Logits With Densely Connected Relations | null | In prevalent knowledge distillation, logits in most image recognition models are computed by global average pooling, then used to learn to encode the high-level and task-relevant knowledge. In this work, we solve the limitation of this global logit transfer in this distillation context. We point out that it prevents the transfer of informative spatial information, which provides localized knowledge as well as rich relational information across contexts of an input scene. To exploit the rich spatial information, we propose a simple yet effective logit distillation approach. We add a local spatial pooling layer branch to the penultimate layer, thereby our method extends the standard logit distillation and enables learning of both finely-localized knowledge and holistic representation. Our proposed method shows favorable accuracy improvement against the state-of-the-art methods on several image classification datasets. We show that our distilled students trained on the image classification task can be successfully leveraged for object detection and semantic segmentation tasks; this result demonstrates our method's high transferability. | Youmin Kim, Jinbae Park, YounHo Jang, Muhammad Ali, Tae-Hyun Oh, Sung-Ho Bae; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6290-6300 | null | null | 2,021 | iccv |
Viewpoint Invariant Dense Matching for Visual Geolocalization | null | In this paper we propose a novel method for image matching based on dense local features and tailored for visual geolocalization. Dense local features matching is robust against changes in illumination and occlusions, but not against viewpoint shifts which are a fundamental aspect of geolocalization. Our method, called GeoWarp, directly embeds invariance to viewpoint shifts in the process of extracting dense features. This is achieved via a trainable module which learns from the data an invariance that is meaningful for the task of recognizing places. We also devise a new self-supervised loss and two new weakly supervised losses to train this module using only unlabeled data and weak labels. GeoWarp is implemented efficiently as a re-ranking method that can be easily embedded into pre-existing visual geolocalization pipelines. Experimental validation on standard geolocalization benchmarks demonstrates that GeoWarp boosts the accuracy of state-of-the-art retrieval architectures. The code and trained models will be released upon acceptance of this paper. | Gabriele Berton, Carlo Masone, Valerio Paolicelli, Barbara Caputo; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12169-12178 | null | null | 2,021 | iccv |
Conditional DETR for Fast Training Convergence | null | The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7x faster for the backbones R50 and R101 and 10x faster for stronger backbones DC5-R50 and DC5-R101. Code is available at https://github.com/Atten4Vis/ConditionalDETR. | Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3651-3660 | null | null | 2,021 | iccv |
Deep Matching Prior: Test-Time Optimization for Dense Correspondence | null | Conventional techniques to establish dense correspondences across visually or semantically similar images focused on designing a task-specific matching prior, which is difficult to model in general. To overcome this, recent learning-based methods have attempted to learn a good matching prior within a model itself on large training data. The performance improvement was apparent, but the need for sufficient training data and intensive learning hinders their applicability. Moreover, using the fixed model at test time does not account for the fact that a pair of images may require their own prior, thus providing limited performance and poor generalization to unseen images. In this paper, we show that an image pair-specific prior can be captured by solely optimizing the untrained matching networks on an input pair of images. Tailored for such test-time optimization for dense correspondence, we present a residual matching network and a confidence-aware contrastive loss to guarantee a meaningful convergence. Experiments demonstrate that our framework, dubbed Deep Matching Prior (DMP), is competitive, or even outperforms, against the latest learning-based methods on several benchmarks for geometric matching and semantic matching, even though it requires neither large training data nor intensive learning. With the networks pre-trained, DMP attains state-of-the-art performance on all benchmarks. | Sunghwan Hong, Seungryong Kim; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9907-9917 | null | null | 2,021 | iccv |
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection | null | Anomaly detection, the task of identifying unusual samples in data, often relies on a large set of training samples. In this work, we consider the setting of few-shot anomaly detection in images, where only a few images are given at training. We devise a hierarchical generative model that captures the multi-scale patch distribution of each training image. We further enhance the representation of our model by using image transformations and optimize scale-specific patch-discriminators to distinguish between real and fake patches of the image, as well as between different transformations applied to those patches. The anomaly score is obtained by aggregating the patch-based votes of the correct transformation across scales and image regions. We demonstrate the superiority of our method on both the one-shot and few-shot settings, on the datasets of Paris, CIFAR10, MNIST and FashionMNIST as well as in the setting of defect detection on MVTec. In all cases, our method outperforms the recent baseline methods. | Shelly Sheynin, Sagie Benaim, Lior Wolf; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8495-8504 | null | null | 2,021 | iccv |
ODAM: Object Detection, Association, and Mapping Using Posed RGB Video | null | Localizing objects and estimating their extent in 3D is an important step towards high-level 3D scene understanding, which has many applications in Augmented Reality and Robotics. We present ODAM, a system for 3D Object Detection, Association, and Mapping using posed RGB videos. The proposed system relies on a deep-learning-based front-end to detect 3D objects from a given RGB frame and associate them to a global object-based map using a graph neural network (GNN). Based on these frame-to-model associations, our back-end optimizes object bounding volumes, represented as super-quadrics, under multi-view geometry constraints and the object scale prior. We validate the proposed system on ScanNet where we show a significant improvement over existing RGB-only methods. | Kejie Li, Daniel DeTone, Yu Fan (Steven) Chen, Minh Vo, Ian Reid, Hamid Rezatofighi, Chris Sweeney, Julian Straub, Richard Newcombe; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5998-6008 | null | null | 2,021 | iccv |
DualPoseNet: Category-Level 6D Object Pose and Size Estimation Using Dual Pose Network With Refined Learning of Pose Consistency | null | Category-level 6D object pose and size estimation is to predict full pose configurations of rotation, translation, and size for object instances observed in single, arbitrary views of cluttered scenes. In this paper, we propose a new method of Dual Pose Network with refined learning of pose consistency for this task, shortened as DualPoseNet. DualPoseNet stacks two parallel pose decoders on top of a shared pose encoder, where the implicit decoder predicts object poses with a working mechanism different from that of the explicit one; they thus impose complementary supervision on the training of pose encoder. We construct the encoder based on spherical convolutions, and design a module of Spherical Fusion wherein for a better embedding of pose-sensitive features from the appearance and shape observations. Given no testing CAD models, it is the novel introduction of the implicit decoder that enables the refined pose prediction during testing, by enforcing the predicted pose consistency between the two decoders using a self-adaptive loss term. Thorough experiments on benchmarks of both category- and instance-level object pose datasets confirm efficacy of our designs. DualPoseNet outperforms existing methods with a large margin in the regime of high precision. Our code is released publicly at https://github.com/Gorilla-Lab-SCUT/DualPoseNet. | Jiehong Lin, Zewei Wei, Zhihao Li, Songcen Xu, Kui Jia, Yuanqing Li; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3560-3569 | null | null | 2,021 | iccv |
Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition | null | Human pose is a useful feature for fine-grained sports action understanding. However, pose estimators are often unreliable when run on sports video due to domain shift and factors such as motion blur and occlusions. This leads to poor accuracy when downstream tasks, such as action recognition, depend on pose. End-to-end learning circumvents pose, but requires more labels to generalize. We introduce Video Pose Distillation (VPD), a weakly-supervised technique to learn features for new video domains, such as individual sports that challenge pose estimation. Under VPD, a student network learns to extract robust pose features from RGB frames in the sports video, such that, whenever pose is considered reliable, the features match the output of a pretrained teacher pose detector. Our strategy retains the best of both pose and end-to-end worlds, exploiting the rich visual patterns in raw video frames, while learning features that agree with the athletes' pose and motion in the target video domain to avoid over-fitting to patterns unrelated to athletes' motion. VPD features improve performance on few-shot, fine-grained action recognition, retrieval, and detection tasks in four real-world sports video datasets, without requiring additional ground-truth pose annotations. | James Hong, Matthew Fisher, Michaël Gharbi, Kayvon Fatahalian; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9254-9263 | null | null | 2,021 | iccv |
Detecting Persuasive Atypicality by Modeling Contextual Compatibility | null | We propose a new approach to detect atypicality in persuasive imagery. Unlike atypicality which has been studied in prior work, persuasive atypicality has a particular purpose to convey meaning, and relies on understanding the common-sense spatial relations of objects. We propose a self-supervised attention-based technique which captures contextual compatibility, and models spatial relations in a precise manner. We further experiment with capturing common sense through the semantics of co-occurring object classes. We verify our approach on a dataset of atypicality in visual advertisements, as well as a second dataset capturing atypicality that has no persuasive intent. | Meiqi Guo, Rebecca Hwa, Adriana Kovashka; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 972-982 | null | null | 2,021 | iccv |
MDETR - Modulated Detection for End-to-End Multi-Modal Understanding | null | Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. However, this crucial module is typically used as a black box, trained independently of the downstream task and on a fixed vocabulary of objects and attributes. This makes it challenging for such systems to capture the long tail of visual concepts expressed in free form text. In this paper we propose MDETR, an end-to-end modulated detector that detects objects in an image conditioned on a raw text query, like a caption or a question. We use a transformer-based architecture to reason jointly over text and image by fusing the two modalities at an early stage of the model. We pre-train the network on 1.3M text-image pairs, mined from pre-existing multi-modal datasets having explicit alignment between phrases in text and objects in the image. We then fine-tune on several downstream tasks such as phrase grounding, referring expression comprehension and segmentation, achieving state-of-the-art results on popular benchmarks. We also investigate the utility of our model as an object detector on a given label set when fine-tuned in a few-shot setting. We show that our pre-training approach provides a way to handle the long tail of object categories which have very few labelled instances. Our approach can be easily extended for visual question answering, achieving competitive performance on GQA and CLEVR. The code and models are available at https://github.com/ashkamath/mdetr. | Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, Nicolas Carion; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1780-1790 | null | null | 2,021 | iccv |
The Surprising Impact of Mask-Head Architecture on Novel Class Segmentation | null | Instance segmentation models today are very accurate when trained on large annotated datasets, but collecting mask annotations at scale is prohibitively expensive. We address the partially supervised instance segmentation problem in which one can train on (significantly cheaper) bounding boxes for all categories but use masks only for a subset of categories. In this work, we focus on a popular family of models which apply differentiable cropping to a feature map and predict a mask based on the resulting crop. Under this family, we study Mask R-CNN and discover that instead of its default strategy of training the mask-head with a combination of proposals and groundtruth boxes, training the mask-head with only groundtruth boxes dramatically improves its performance on novel classes. This training strategy also allows us to take advantage of alternative mask-head architectures, which we exploit by replacing the typical mask-head of 2-4 layers with significantly deeper off-the-shelf architectures (e.g. ResNet, Hourglass models). While many of these architectures perform similarly when trained in fully supervised mode, our main finding is that they can generalize to novel classes in dramatically different ways. We call this ability of mask-heads to generalize to unseen classes the strong mask generalization effect and show that without any specialty modules or losses, we can achieve state-of-the-art results in the partially supervised COCO instance segmentation benchmark. Finally, we demonstrate that our effect is general, holding across underlying detection methodologies (including anchor-based, anchor-free or no detector at all) and across different backbone networks. Code and pre-trained models are available at https://git.io/deepmac. | Vighnesh Birodkar, Zhichao Lu, Siyang Li, Vivek Rathod, Jonathan Huang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 7015-7025 | null | null | 2,021 | iccv |
Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories | null | For machine learning models trained with limited labeled training data, validation stands to become the main bottleneck to reducing overall annotation costs. We propose a statistical validation algorithm that accurately estimates the F-score of binary classifiers for rare categories, where finding relevant examples to evaluate on is particularly challenging. Our key insight is that simultaneous calibration and importance sampling enables accurate estimates even in the low-sample regime (<300 samples). Critically, we also derive an accurate single-trial estimator of the variance of our method and demonstrate that this estimator is empirically accurate at low sample counts, enabling a practitioner to know how well they can trust a given low-sample estimate. When validating state-of-the-art semi-supervised models on ImageNet and iNaturalist2017, our method achieves the same estimates of model performance with up to 10x fewer labels than competing approaches. In particular, we can estimate model F1 scores with a variance of 0.005 using as few as 100 labels. | Fait Poms, Vishnu Sarukkai, Ravi Teja Mullapudi, Nimit S. Sohoni, William R. Mark, Deva Ramanan, Kayvon Fatahalian; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10705-10714 | null | null | 2,021 | iccv |
Else-Net: Elastic Semantic Network for Continual Action Recognition From Skeleton Data | null | We address continual action recognition from skeleton sequence, which aims to learn a recognition model over time from a continuous stream of skeleton data. This task is very important in changing environment. Due to catastrophic forgetting problems of deep neural networks and large discrepancies between the previously learned and current new human actions from different categories, the neural networks may "forget" old actions, when learning new actions. This makes online continual action recognition a challenging task. We observe that although different human actions may vary to a large extent as a whole, their local body parts could share similar features. Therefore, we propose an Elastic Semantic Network (Else-Net) to learn new actions by decomposing human bodies into several semantic body parts. For each body part, the proposed Else-Net constructs a semantic pathway using several elastic cells learned with old actions, or explores new cells to store new knowledge. | Tianjiao Li, Qiuhong Ke, Hossein Rahmani, Rui En Ho, Henghui Ding, Jun Liu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13434-13443 | null | null | 2,021 | iccv |
GNeRF: GAN-Based Neural Radiance Field Without Posed Camera | null | We introduce GNeRF, a framework to marry Generative Adversarial Networks (GAN) with Neural Radiance Field (NeRF) reconstruction for the complex scenarios with unknown and even randomly initialized camera poses. Recent NeRF-based advances have gained popularity for remarkable realistic novel view synthesis. However, most of them heavily rely on accurate camera poses estimation, while few recent methods can only optimize the unknown camera poses in roughly forward-facing scenes with relatively short camera trajectories and require rough camera poses initialization. Differently, our GNeRF only utilizes randomly initialized poses for complex outside-in scenarios. We propose a novel two-phases end-to-end framework. The first phase takes the use of GANs into the new realm for optimizing coarse camera poses and radiance fields jointly, while the second phase refines them with additional photometric loss. We overcome local minima using a hybrid and iterative optimization scheme. Extensive experiments on a variety of synthetic and natural scenes demonstrate the effectiveness of GNeRF. More impressively, our approach outperforms the baselines favorably in those scenes with repeated patterns or even low textures that are regarded as extremely challenging before. | Quan Meng, Anpei Chen, Haimin Luo, Minye Wu, Hao Su, Lan Xu, Xuming He, Jingyi Yu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6351-6361 | null | null | 2,021 | iccv |
Adaptive Convolutions With Per-Pixel Dynamic Filter Atom | null | Applying feature dependent network weights have been proved to be effective in many fields. However, in practice, restricted by the enormous size of model parameters and memory footprints, scalable and versatile dynamic convolutions with per-pixel adapted filters are yet to be fully explored. In this paper, we address this challenge by decomposing filters, adapted to each spatial position, over dynamic filter atoms generated by a light-weight network from local features. Adaptive receptive fields can be supported by further representing each filter atom over sets of pre-fixed multi-scale bases. As plug-and-play replacements to convolutional layers, the introduced adaptive convolutions with per-pixel dynamic atoms enable explicit modeling of intra-image variance, while avoiding heavy computation, parameters, and memory cost. Our method preserves the appealing properties of conventional convolutions as being translation-equivariant and parametrically efficient. We present experiments to show that, the proposed method delivers comparable or even better performance across tasks, and are particularly effective on handling tasks with significant intra-image variance. | Ze Wang, Zichen Miao, Jun Hu, Qiang Qiu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12302-12311 | null | null | 2,021 | iccv |
Warp-Refine Propagation: Semi-Supervised Auto-Labeling via Cycle-Consistency | null | Deep learning models for semantic segmentation rely on expensive, large-scale, manually annotated datasets. Labelling is a tedious process that can take hours per image. Automatically annotating video sequences by propagating sparsely labeled frames through time is a more scalable alternative. In this work, we propose a novel label propagation method, termed Warp-Refine Propagation, that combines semantic cues with geometric cues to efficiently auto-label videos. Our method learns to refine geometrically-warped labels and infuse them with learned semantic priors in a semi-supervised setting by leveraging cycle consistency across time. We quantitatively show that our method improves label-propagation by a noteworthy margin of 13.1 mIoU on the ApolloScape dataset. Furthermore, by training with the auto-labelled frames, we achieve competitive results on three semantic-segmentation benchmarks, improving the state-of-the-art by a large margin of 1.8 and 3.61 mIoU on NYU-V2 and KITTI, while matching the current best results on Cityscapes. | Aditya Ganeshan, Alexis Vallet, Yasunori Kudo, Shin-ichi Maeda, Tommi Kerola, Rares Ambrus, Dennis Park, Adrien Gaidon; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15499-15509 | null | null | 2,021 | iccv |
Dual Contrastive Loss and Attention for GANs | null | Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. Yet generated images are still easy to spot especially on datasets with high variance (e.g. bedroom, church). In this paper, we propose various improvements to further push the boundaries in image generation. Specifically, we propose a novel dual contrastive loss and show that, with this loss, discriminator learns more generalized and distinguishable representations to incentivize generation. In addition, we revisit attention and extensively experiment with different attention blocks in the generator. We find attention to be still an important module for successful image generation even though it was not used in the recent state-of-the-art models. Lastly, we study different attention architectures in the discriminator, and propose a reference attention mechanism. By combining the strengths of these remedies, we improve the compelling state-of-the-art Frechet Inception Distance (FID) by at least 17.5% on several benchmark datasets. We obtain even more significant improvements on compositional synthetic scenes (up to 47.5% in FID). | Ning Yu, Guilin Liu, Aysegul Dundar, Andrew Tao, Bryan Catanzaro, Larry S. Davis, Mario Fritz; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6731-6742 | null | null | 2,021 | iccv |
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models | null | Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in unconditional image generation. However, due to the stochasticity of the generative process in DDPM, it is challenging to generate images with the desired semantics. In this work, we propose Iterative Latent Variable Refinement (ILVR), a method to guide the generative process in DDPM to generate high-quality images based on a given reference image. Here, the refinement of the generative process in DDPM enables a single DDPM to sample images from various sets directed by the reference image. The proposed ILVR method generates high-quality images while controlling the generation. The controllability of our method allows adaptation of a single DDPM without any additional learning in various image generation tasks, such as generation from various downsampling factors, multi-domain image translation, paint-to-image, and editing with scribbles. | Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, Sungroh Yoon; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14367-14376 | null | null | 2,021 | iccv |
End-to-End Video Instance Segmentation via Spatial-Temporal Graph Neural Networks | null | Video instance segmentation is a challenging task that extends image instance segmentation to the video domain. Existing methods either rely only on single-frame information for the detection and segmentation subproblems or handle tracking as a separate post-processing step, which limit their capability to fully leverage and share useful spatial-temporal information for all the subproblems. In this paper, we propose a novel graph-neural-network (GNN) based method to handle the aforementioned limitation. Specifically, graph nodes representing instance features are used for detection and segmentation while graph edges representing instance relations are used for tracking. Both inter and intra-frame information is effectively propagated and shared via graph updates and all the subproblems (i.e. detection, segmentation and tracking) are jointly optimized in an unified framework. The performance of our method shows great improvement on the YoutubeVIS validation dataset compared to existing methods and achieves 36.5% AP with a ResNet-50 backbone, operating at 22 FPS. | Tao Wang, Ning Xu, Kean Chen, Weiyao Lin; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10797-10806 | null | null | 2,021 | iccv |
Collaborative Optimization and Aggregation for Decentralized Domain Generalization and Adaptation | null | Contemporary domain generalization (DG) and multi-source unsupervised domain adaptation (UDA) methods mostly collect data from multiple domains together for joint optimization. However, this centralized training paradigm poses a threat to data privacy and is not applicable when data are non-shared across domains. In this work, we propose a new approach called Collaborative Optimization and Aggregation (COPA), which aims at optimizing a generalized target model for decentralized DG and UDA, where data from different domains are non-shared and private. Our base model consists of a domain-invariant feature extractor and an ensemble of domain-specific classifiers. In an iterative learning process, we optimize a local model for each domain, and then centrally aggregate local feature extractors and assemble domain-specific classifiers to construct a generalized global model, without sharing data from different domains. To improve generalization of feature extractors, we employ hybrid batch-instance normalization and collaboration of frozen classifiers. For better decentralized UDA, we further introduce a prediction agreement mechanism to overcome local disparities towards central model aggregation. Extensive experiments on five DG and UDA benchmark datasets show that COPA is capable of achieving comparable performance against the state-of-the-art DG and UDA methods without the need for centralized data collection in model training. | Guile Wu, Shaogang Gong; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6484-6493 | null | null | 2,021 | iccv |
Calibrated and Partially Calibrated Semi-Generalized Homographies | null | In this paper, we propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera. The proposed solvers use five 2D-2D image point correspondences induced by a scene plane. One group of solvers assumes the perspective camera to be fully calibrated, while the other estimates the unknown focal length together with the absolute pose parameters. This setup is particularly important in structure-from-motion and visual localization pipelines, where a new camera is localized in each step with respect to a set of known cameras and 2D-3D correspondences might not be available. Thanks to a clever parametrization and the elimination ideal method, our solvers only need to solve a univariate polynomial of degree five or three, respectively a system of polynomial equations in two variables. All proposed solvers are stable and efficient as demonstrated by a number of synthetic and real-world experiments. | Snehal Bhayani, Torsten Sattler, Daniel Barath, Patrik Beliansky, Janne Heikkilä, Zuzana Kukelova; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5936-5945 | null | null | 2,021 | iccv |
The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data | null | Event cameras, inspired by biological vision systems, provide a natural and data efficient representation of visual information. Visual information is acquired in the form of events that are triggered by local brightness changes. However, because most brightness changes are triggered by relative motion of the camera and the scene, the events recorded at a single sensor location seldom correspond to the same world point. To extract meaningful information from event cameras, it is helpful to register events that were triggered by the same underlying world point. In this work we propose a new model of event data that captures its natural spatio-temporal structure. We start by developing a model for aligned event data. That is, we develop a model for the data as though it has been perfectly registered already. In particular, we model the aligned data as a spatio-temporal Poisson point process. Based on this model, we develop a maximum likelihood approach to registering events that are not yet aligned. That is, we find transformations of the observed events that make them as likely as possible under our model. In particular we extract the camera rotation that leads to the best event alignment. We show new state of the art accuracy for rotational velocity estimation on the DAVIS 240C dataset [??]. In addition, our method is also faster and has lower computational complexity than several competing methods. | Cheng Gu, Erik Learned-Miller, Daniel Sheldon, Guillermo Gallego, Pia Bideau; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13495-13504 | null | null | 2,021 | iccv |
CR-Fill: Generative Image Inpainting With Auxiliary Contextual Reconstruction | null | Recent deep generative inpainting methods use attention layers to allow the generator to explicitly borrow feature patches from the known region to complete a missing region. Due to the lack of supervision signals for the correspondence between missing regions and known regions, it may fail to find proper reference features, which often leads to artifacts in the results. Also, it computes pair-wise similarity across the entire feature map during inference bringing a significant computational overhead. To address this issue, we propose to teach such patch-borrowing behavior to an attention-free generator by joint training of an auxiliary contextual reconstruction task, which encourages the generated output to be plausible even when reconstructed by surrounding regions. The auxiliary branch can be seen as a learnable loss function, i.e. named as contextual reconstruction (CR) loss, where query-reference feature similarity and reference-based reconstructor are jointly optimized with the inpainting generator. The auxiliary branch (i.e. CR loss) is required only during training, and only the inpainting generator is required during the inference. Experimental results demonstrate that the proposed inpainting model compares favourably against the state-of-the-art in terms of quantitative and visual performance. Code is available at https://github.com/zengxianyu/crfill. | Yu Zeng, Zhe Lin, Huchuan Lu, Vishal M. Patel; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 14164-14173 | null | null | 2,021 | iccv |
Learning Dual Priors for JPEG Compression Artifacts Removal | null | Deep learning (DL)-based methods have achieved great success in solving the ill-posed JPEG compression artifacts removal problem. However, as most DL architectures are designed to directly learn pixel-level mapping relationships, they largely ignore semantic-level information and lack sufficient interpretability. To address the above issues, in this work, we propose an interpretable deep network to learn both pixel-level regressive prior and semantic-level discriminative prior. Specifically, we design a variational model to formulate the image de-blocking problem and propose two prior terms for the image content and gradient, respectively. The content-relevant prior is formulated as a DL-based image-to-image regressor to perform as a de-blocker from the pixel-level. The gradient-relevant prior serves as a DL-based classifier to distinguish whether the image is compressed from the semantic-level. To effectively solve the variational model, we design an alternating minimization algorithm and unfold it into a deep network architecture. In this way, not only the interpretability of the deep network is increased, but also the dual priors can be well estimated from training samples. By integrating the two priors into a single framework, the image de-blocking problem can be well-constrained, leading to a better performance. Experiments on benchmarks and real-world use cases demonstrate the superiority of our method to the existing state-of-the-art approaches. | Xueyang Fu, Xi Wang, Aiping Liu, Junwei Han, Zheng-Jun Zha; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4086-4095 | null | null | 2,021 | iccv |
EC-DARTS: Inducing Equalized and Consistent Optimization Into DARTS | null | Based on the relaxed search space, differential architecture search (DARTS) is efficient in searching for a high-performance architecture. However, the unbalanced competition among operations that have different trainable parameters causes the model collapse. Besides, the inconsistent structures in the search and retraining stages causes cross-stage evaluation to be unstable. In this paper, we call these issues as an operation gap and a structure gap in DARTS. To shrink these gaps, we propose to induce equalized and consistent optimization in differentiable architecture search (EC-DARTS). EC-DARTS decouples different operations based on their categories to optimize the operation weights so that the operation gap between them is shrinked. Besides, we introduce an induced structural transition to bridge the structure gap between the model structures in the search and retraining stages. Extensive experiments on CIFAR10 and ImageNet demonstrate the effectiveness of our method. Specifically, on CIFAR10, we achieve a test error of 2.39%, while only 0.3 GPU days on NVIDIA TITAN V. On ImageNet, our method achieves a top-1 error of 23.6% under the mobile setting. | Qinqin Zhou, Xiawu Zheng, Liujuan Cao, Bineng Zhong, Teng Xi, Gang Zhang, Errui Ding, Mingliang Xu, Rongrong Ji; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11986-11995 | null | null | 2,021 | iccv |
Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing | null | We present Neural Generalized Implicit Functions(Neural-GIF), to animate people in clothing as a function of the body pose. Given a sequence of scans of a subject in various poses, we learn to animate the character for new poses. Existing methods have relied on template-based representations of the human body(or clothing). However such models usually have fixed and limited resolutions, and require difficult data pre-processing steps, and cannot be used for complex clothing. We draw inspiration from template-based methods, which factorize motion into articulation and non-rigid deformation, but generalize this concept for implicit shape learning to obtain a more flexible model. We learn to map every point in the space to a canonical space, where a learned deformation field is applied to model non-rigid effects, before evaluating the signed distance field. Our formulation allows the learning of complex and non-rigid deformations of clothing and soft tissue, without computing a template registration as it is common with current approaches. Neural-GIF can be trained on raw 3D scans and reconstructs detailed complex surface geometry and deformations. Moreover, the model can generalize to new poses. We evaluate our method on a variety of characters from different public datasets in diverse clothing styles and show significant improvement over baseline methods, quantitatively and qualitatively. We also extend our model to multiple shape setting. To stimulate further research, we will make the model, code, and data publicly available. | Garvita Tiwari, Nikolaos Sarafianos, Tony Tung, Gerard Pons-Moll; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11708-11718 | null | null | 2,021 | iccv |
Refining Activation Downsampling With SoftPool | null | Convolutional Neural Networks (CNNs) use pooling to decrease the size of activation maps. This process is crucial to increase the receptive fields and to reduce computational requirements of subsequent convolutions. An important feature of the pooling operation is the minimization of information loss, with respect to the initial activation maps, without a significant impact on the computation and memory overhead. To meet these requirements, we propose SoftPool: a fast and efficient method for exponentially weighted activation downsampling. Through experiments across a range of architectures and pooling methods, we demonstrate that SoftPool can retain more information in the reduced activation maps. This refined downsampling leads to improvements in a CNN's classification accuracy. Experiments with pooling layer substitutions on ImageNet1K show an increase in accuracy over both original architectures and other pooling methods. We also test SoftPool on video datasets for action recognition. Again, through the direct replacement of pooling layers, we observe consistent performance improvements while computational loads and memory requirements remain limited. | Alexandros Stergiou, Ronald Poppe, Grigorios Kalliatakis; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10357-10366 | null | null | 2,021 | iccv |
Learning Multiple Pixelwise Tasks Based on Loss Scale Balancing | null | We propose a novel loss weighting algorithm, called loss scale balancing (LSB), for multi-task learning (MTL) of pixelwise vision tasks. An MTL model is trained to estimate multiple pixelwise predictions using an overall loss, which is a linear combination of individual task losses. The proposed algorithm dynamically adjusts the linear weights to learn all tasks effectively. Instead of controlling the trend of each loss value directly, we balance the loss scale --- the product of the loss value and its weight --- periodically. In addition, by evaluating the difficulty of each task based on the previous loss record, the proposed algorithm focuses more on difficult tasks during training. Experimental results show that the proposed algorithm outperforms conventional weighting algorithms for MTL of various pixelwise tasks. Codes are available at https://github.com/jaehanlee-mcl/LSB-MTL. | Jae-Han Lee, Chul Lee, Chang-Su Kim; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5107-5116 | null | null | 2,021 | iccv |
Learning Self-Similarity in Space and Time As Generalized Motion for Video Action Recognition | null | Spatio-temporal convolution often fails to learn motion dynamics in videos and thus an effective motion representation is required for video understanding in the wild. In this paper, we propose a rich and robust motion representation based on spatio-temporal self-similarity (STSS). Given a sequence of frames, STSS represents each local region as similarities to its neighbors in space and time. By converting appearance features into relational values, it enables the learner to better recognize structural patterns in space and time. We leverage the whole volume of STSS and let our model learn to extract an effective motion representation from it. The proposed neural block, dubbed SELFY, can be easily inserted into neural architectures and trained end-to-end without additional supervision. With a sufficient volume of the neighborhood in space and time, it effectively captures long-term interaction and fast motion in the video, leading to robust action recognition. Our experimental analysis demonstrates its superiority over previous methods for motion modeling as well as its complementarity to spatio-temporal features from direct convolution. On the standard action recognition benchmarks, SomethingSomething-V1 & V2, Diving-48, and FineGym, the proposed method achieves the state-of-the-art results. | Heeseung Kwon, Manjin Kim, Suha Kwak, Minsu Cho; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13065-13075 | null | null | 2,021 | iccv |
Searching for Two-Stream Models in Multivariate Space for Video Recognition | null | Conventional video models rely on a single stream to capture the complex spatial-temporal features. Recent work on two-stream video models, such as SlowFast network and AssembleNet, prescribe separate streams to learn complementary features, and achieve stronger performance. However, manually designing both streams as well as the in-between fusion blocks is a daunting task, requiring to explore a tremendously large design space. Such manual exploration is time-consuming and often ends up with sub-optimal architectures when computational resources are limited and the exploration is insufficient. In this work, we present a pragmatic neural architecture search approach, which is able to search for two-stream video models in giant spaces efficiently. We design a multivariate search space, including 6 search variables to capture a wide variety of choices in designing two-stream models. Furthermore, we propose a progressive search procedure, by searching for the architecture of individual streams, fusion blocks and attention blocks one after the other. We demonstrate two-stream models with significantly better performance can be automatically discovered in our design space. Our searched two-stream models, namely Auto-TSNet, consistently outperform other models on standard benchmarks. On Kinetics, compared with the SlowFast model, our Auto-TSNet-L model reduces FLOPS by nearly 11 times while achieving the same accuracy 78.9%. On Something-Something-V2, Auto-TSNet-M improves the accuracy by at least 2% over other methods which use less than 50 GFLOPS per video. | Xinyu Gong, Heng Wang, Mike Zheng Shou, Matt Feiszli, Zhangyang Wang, Zhicheng Yan; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8033-8042 | null | null | 2,021 | iccv |
Causal Attention for Unbiased Visual Recognition | null | Attention module does not always help deep models learn causal features that are robust in any confounding context, e.g., a foreground object feature is invariant to different backgrounds. This is because the confounders trick the attention to capture spurious correlations that benefit the prediction when the training and testing data are IID (identical & independent distribution); while harm the prediction when the data are OOD (out-of-distribution). The sole fundamental solution to learn causal attention is by causal intervention, which requires additional annotations of the confounders, e.g., a "dog" model is learned within "grass+dog" and "road+dog" respectively, so the "grass" and "road" contexts will no longer confound the "dog" recognition. However, such annotation is not only prohibitively expensive, but also inherently problematic, as the confounders are elusive in nature. In this paper, we propose a causal attention module (CaaM) that self-annotates the confounders in unsupervised fashion. In particular, multiple CaaMs can be stacked and integrated in conventional attention CNN and self-attention Vision Transformer. In OOD settings, deep models with CaaM outperform those without it significantly; even in IID settings, the attention localization is also improved by CaaM, showing a great potential in applications that require robust visual saliency. Codes are available at https://github.com/Wangt-CN/CaaM. | Tan Wang, Chang Zhou, Qianru Sun, Hanwang Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3091-3100 | null | null | 2,021 | iccv |
ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot | null | One-stage long-tailed recognition methods improve the overall performance in a "seesaw" manner, i.e., either sacrifice the head's accuracy for better tail classification or elevate the head's accuracy even higher but ignore the tail. Existing algorithms bypass such trade-off by a multi-stage training process: pre-training on imbalanced set and fine-tuning on balanced set. Though achieving promising performance, not only are they sensitive to the generalizability of the pre-trained model, but also not easily integrated into other computer vision tasks like detection and segmentation, where pre-training of classifier solely is not applicable. In this paper, we propose a one-stage long-tailed recognition scheme, ally complementary experts (ACE), where the expert is the most knowledgeable specialist in a sub-set that dominates its training, and is complementary to other experts in the less-seen categories without disturbed by what it has never seen. We design a distribution-adaptive optimizer to adjust the learning pace of each expert to avoid over-fitting. Without special bells and whistles, the vanilla ACE outperforms the current one-stage SOTA method by 3 10% on CIFAR10-LT, CIFAR100-LT, ImageNet-LT and iNaturalist datasets. It is also shown to be the first one to break the "seesaw" trade-off by improving the accuracy of the majority and minority categories simultaneously in only one stage. | Jiarui Cai, Yizhou Wang, Jenq-Neng Hwang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 112-121 | null | null | 2,021 | iccv |
Pseudo-Loss Confidence Metric for Semi-Supervised Few-Shot Learning | null | Semi-supervised few-shot learning is developed to train a classifier that can adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data. Most semi-supervised few-shot learning methods select pseudo-labeled data of unlabeled set by task-specific confidence estimation. This work presents a task-unified confidence estimation approach for semi-supervised few-shot learning, named pseudo-loss confidence metric (PLCM). It measures the data credibility by the loss distribution of pseudo-labels, which is synthetical considered multi-tasks. Specifically, pseudo-labeled data of different tasks are mapped to a unified metric space by mean of the pseudo-loss model, making it possible to learn the prior pseudo-loss distribution. Then, confidence of pseudo-labeled data is estimated according to the distribution component confidence of its pseudo-loss. Thus highly reliable pseudo-labeled data are selected to strengthen the classifier. Moreover, to overcome the pseudo-loss distribution shift and improve the effectiveness of classifier, we advance the multi-step training strategy coordinated with the class balance measures of class-apart selection and class weight. Experimental results on four popular benchmark datasets demonstrate that the proposed approach can effectively select pseudo-labeled data and achieve the state-of-the-art performance. | Kai Huang, Jie Geng, Wen Jiang, Xinyang Deng, Zhe Xu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8671-8680 | null | null | 2,021 | iccv |
STVGBert: A Visual-Linguistic Transformer Based Framework for Spatio-Temporal Video Grounding | null | Spatio-temporal video grounding (STVG) aims to localize a spatio-temporal tube of a target object in an untrimmed video based on a query sentence. In this work, we propose a one-stage visual-linguistic transformer based framework called STVGBert for the STVG task, which can simultaneously localize the target object in both spatial and temporal domains. Specifically, without resorting to pre-generated object proposals, our STVGBert directly takes a video and a query sentence as the input, and then produces the cross-modal features by using the newly introduced cross-modal feature learning module ST-ViLBert. Based on the cross-modal features, our method then generates bounding boxes and predicts the starting and ending frames to produce the predicted object tube. To the best of our knowledge, our STVGBert is the first one-stage method, which can handle the STVG task without relying on any pre-trained object detectors. Comprehensive experiments demonstrate our newly proposed framework outperforms the state-of-the-art multi-stage methods on two benchmark datasets Vid-STG and HC-STVG. | Rui Su, Qian Yu, Dong Xu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1533-1542 | null | null | 2,021 | iccv |
MLVSNet: Multi-Level Voting Siamese Network for 3D Visual Tracking | null | Benefiting from the excellent performance of Siamese-based trackers, huge progress on 2D visual tracking has been achieved. However, 3D visual tracking is still under-explored. Inspired by the idea of Hough voting in 3D object detection, in this paper, we propose a Multi-level Voting Siamese Network (MLVSNet) for 3D visual tracking from outdoor point cloud sequences. To deal with sparsity in outdoor 3D point clouds, we propose to perform Hough voting on multi-level features to get more vote centers and retain more useful information, instead of voting only on the final level feature as in previous methods. We also design an efficient and lightweight Target-Guided Attention (TGA) module to transfer the target information and highlight the target points in the search area. Moreover, we propose a Vote-cluster Feature Enhancement (VFE) module to exploit the relationships between different vote clusters. Extensive experiments on the 3D tracking benchmark of KITTI dataset demonstrate that our MLVSNet outperforms state-of-the-art methods with significant margins. Code will be available at https://github.com/CodeWZT/MLVSNet. | Zhoutao Wang, Qian Xie, Yu-Kun Lai, Jing Wu, Kun Long, Jun Wang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3101-3110 | null | null | 2,021 | iccv |
Universal Representation Learning From Multiple Domains for Few-Shot Classification | null | In this paper, we look at the problem of few-shot image classification that aims to learn a classifier for previously unseen classes and domains from few labeled samples. Recent methods use various adaptation strategies for aligning their visual representations to new domains or select the relevant ones from multiple domain-specific feature extractors. In this work, we present URL, which learns a single set of universal visual representations by distilling knowledge of multiple domain-specific networks after co-aligning their features with the help of adapters and centered kernel alignment. We show that the universal representations can be further refined for previously unseen domains by an efficient adaptation step in a similar spirit to distance learning methods. We rigorously evaluate our model in the recent Meta-Dataset benchmark and demonstrate that it significantly outperforms the previous methods while being more efficient. | Wei-Hong Li, Xialei Liu, Hakan Bilen; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9526-9535 | null | null | 2,021 | iccv |
Rethinking Noise Synthesis and Modeling in Raw Denoising | null | The lack of large-scale real raw image denoising dataset gives the rise to challenges on synthesizing realistic raw image noise for training denoising models. However, the real raw image noise is contributed by many noise sources and varies greatly among different sensors. Existing methods are unable to model all noise sources accurately, and building a noise model for each sensor is also laborious. In this paper, we introduce a new perspective to synthesize noise by directly sampling from the sensor's real noise. It inherently generates accurate raw image noise for different camera sensors. Two efficient and generic techniques: pattern-aligned patch sampling and high-bit reconstruction help accurate synthesis of spatial-correlated noise and high-bit noise respectively. We conduct systematic experiments on SIDD and ELD datasets. The results show that (1) our method outperforms existing methods and demonstrates wide generalization on different sensors and lighting conditions. (2) Recent conclusions derived from DNN-based noise modeling methods are actually based on inaccurate noise parameters. The DNN-based methods still cannot outperform physics-based statistical methods. | Yi Zhang, Hongwei Qin, Xiaogang Wang, Hongsheng Li; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4593-4601 | null | null | 2,021 | iccv |
Contrastive Attention Maps for Self-Supervised Co-Localization | null | The goal of unsupervised co-localization is to locate the object in a scene under the assumptions that 1) the dataset consists of only one superclass, e.g., birds, and 2) there are no human-annotated labels in the dataset. The most recent method achieves impressive co-localization performance by employing self-supervised representation learning approaches such as predicting rotation. In this paper, we introduce a new contrastive objective directly on the attention maps to enhance co-localization performance. Our contrastive loss function exploits rich information of location, which induces the model to activate the extent of the object effectively. In addition, we propose a pixel-wise attention pooling that selectively aggregates the feature map regarding their magnitudes across channels. Our methods are simple and shown effective by extensive qualitative and quantitative evaluation, achieving state-of-the-art co-localization performances by large margins on four datasets: CUB-200-2011, Stanford Cars, FGVC-Aircraft, and Stanford Dogs. Our code will be publicly available online for the research community. | Minsong Ki, Youngjung Uh, Junsuk Choe, Hyeran Byun; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2803-2812 | null | null | 2,021 | iccv |
Multitask AET With Orthogonal Tangent Regularity for Dark Object Detection | null | Dark environment becomes a challenge for computer vision algorithms owing to insufficient photons and undesirable noises. Most of the existing studies tackle this by either targeting human vision for better visual perception or improving the machine vision for specific high-level tasks. In addition, these methods rely on data argumentation and directly train their models based on real-world or over-simplified synthetic datasets without exploring the intrinsic pattern behind illumination translation. Here, we propose a novel multitask auto encoding transformation (MAET) model that combines human vision and machine vision tasks to enhance object detection in a dark environment. With a self-supervision learning, the MAET learns an intrinsic visual structure by encoding and decoding the realistic illumination-degrading transformation considering the physical noise model and image signal processing (ISP). Based on this representation, we achieve object detection task by decoding the bounding box coordinates and classes. To avoid the over-entanglement of two tasks, our MAET disentangles the object and degrading features by imposing an orthogonal tangent regularity. This forms a parametric manifold along which multitask predictions can be geometrically formulated by maximizing the orthogonality between the tangents along the outputs of respective tasks. Our framework can be implemented based on the mainstream object detection architecture and directly trained end-to-end using the normal target detection datasets, such as COCO and VOC. We have achieved the state-of-the-art performance using synthetic and real-world datasets. | Ziteng Cui, Guo-Jun Qi, Lin Gu, Shaodi You, Zenghui Zhang, Tatsuya Harada; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2553-2562 | null | null | 2,021 | iccv |
Contact-Aware Retargeting of Skinned Motion | null | This paper introduces a motion retargeting method that preserves self-contacts and prevents inter-penetration. Self-contacts, such as when hands touch each other or the torso or the head, are important attributes of human body language and dynamics, yet existing methods do not model or preserve these contacts. Likewise, self-penetrations, such as a hand passing into the torso, are a typical artifact of motion estimation methods. The input to our method is a human motion sequence and a target skeleton and character geometry. The method identifies self-contacts and ground contacts in the input motion, and optimizes the motion to apply to the output skeleton, while preserving these contacts and reducing self-penetrations. We introduce a novel geometry-conditioned recurrent network with an encoder-space optimization strategy that achieves efficient retargeting while satisfying contact constraints. In experiments, our results quantitatively outperform previous methods and in the user study our retargeted motions are rated as higher-quality than those produced by recent works. We also show our method generalizes to motion estimated from human videos where we improve over previous works that produce noticeable interpenetration. | Ruben Villegas, Duygu Ceylan, Aaron Hertzmann, Jimei Yang, Jun Saito; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9720-9729 | null | null | 2,021 | iccv |
Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds | null | Current 3D single object tracking approaches track the target based on a feature comparison between the target template and the search area. However, due to the common occlusion in LiDAR scans, it is non-trivial to conduct accurate feature comparisons on severe sparse and incomplete shapes. In this work, we exploit the ground truth bounding box given in the first frame as a strong cue to enhance the feature description of the target object, enabling a more accurate feature comparison in a simple yet effective way. In particular, we first propose the BoxCloud, an informative and robust representation, to depict an object using the point-to-box relation. We further design an efficient box-aware feature fusion module, which leverages the aforementioned BoxCloud for reliable feature matching and embedding. Integrating the proposed general components into an existing model P2B, we construct a superior box-aware tracker (BAT). Experiments confirm that our proposed BAT outperforms the previous state-of-the-art by a large margin on both KITTI and NuScenes benchmarks, achieving a 12.8% improvement in terms of precision while running 20% faster. | Chaoda Zheng, Xu Yan, Jiantao Gao, Weibing Zhao, Wei Zhang, Zhen Li, Shuguang Cui; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13199-13208 | null | null | 2,021 | iccv |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.