Mateusz Malinowski, Dimitrios Vytiniotis, Grzegorz Swirszcz, Viorica Patraucean, Joao Carreira

Abstract: How can neural networks be trained on large-volume temporal data efficiently? To compute the gradients required to update parameters, backpropagation blocks computations until the forward and backward passes are completed. For temporal signals, this introduces high latency and hinders real-time learning. It also creates a coupling between consecutive layers, which limits model parallelism and increases memory consumption. In this paper, we build upon Sideways, which avoids blocking by propagating approximate gradients forward in time, by proposing mechanisms for temporal integration of information based on different variants of skip connections. We also show how to decouple computation and delegate individual neural modules to different devices, allowing distributed and parallel training. The proposed Skip-sideways achieves low latency training, model parallelism, and, importantly, is capable of extracting temporal features, leading to more stable training and improved performance on real-world video datasets such as HMDB51, UCF101, and the large-scale Kinetics600. Finally, we also show that models trained with Skip-sideways generate better future frames than Sideways models, and hence they can better utilize motion cues.

@InProceedings{Malinowski_2021_CVPR,
    author    = {Malinowski, Mateusz and Vytiniotis, Dimitrios and Swirszcz, Grzegorz and Patraucean, Viorica and Carreira, Joao},
    title     = {Gradient Forward-Propagation for Large-Scale Temporal Video Modelling},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {9249-9259}
}


Liang Chen, Jiawei Zhang, Jinshan Pan, Songnan Lin, Faming Fang, Jimmy S. Ren

Abstract: Deblurring night blurry images is difficult, because the common-used blur model based on the linear convolution operation does not hold in this situation due to the influence of saturated pixels. In this paper, we propose a non-blind deblurring network (NBDN) to restore night blurry images. To mitigate the side effects brought by the pixels that violate the blur model, we develop a confidence estimation unit (CEU) to estimate a map which ensures smaller contributions of these pixels to the deconvolution steps that are further optimized by the conjugate gradient (CG) method. Moreover, unlike the existing methods using manually tuned hyper-parameters in their frameworks, we propose a hyper-parameter estimation unit (HPEU) to adaptively estimate hyper-parameters  for better image restoration . The experimental results demonstrate that the proposed network performs favorably against state-of-the-art algorithms both quantitatively and qualitatively.

@InProceedings{Chen_2021_CVPR,
    author    = {Chen, Liang and Zhang, Jiawei and Pan, Jinshan and Lin, Songnan and Fang, Faming and Ren, Jimmy S.},
    title     = {Learning a Non-Blind Deblurring Network for Night Blurry Images},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10542-10550}
}


Deep Compositional Metric Learning

Wenzhao Zheng, Chengkun Wang, Jiwen Lu, Jie Zhou

Abstract: In this paper, we propose a deep compositional metric learning (DCML) framework for effective and generalizable similarity measurement between images. Conventional deep metric learning methods minimize a discriminative loss to enlarge interclass distances while suppressing intraclass variations, which might lead to inferior generalization performance since samples even from the same class may present diverse characteristics. This motivates the adoption of the ensemble technique to learn a number of sub-embeddings using different and diverse subtasks. However, most subtasks impose weaker or contradictory constraints, which essentially sacrifices the discrimination ability of each sub-embedding to improve the generalization ability of their combination. To achieve a better generalization ability without compromising, we propose to separate the sub-embeddings from direct supervisions from the subtasks and apply the losses on different composites of the sub-embeddings. We employ a set of learnable compositors to combine the sub-embeddings and use a self-reinforced loss to train the compositors, which serve as relays to distribute the diverse training signals to avoid destroying the discrimination ability. Experimental results on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate the superior performance of our framework.

@InProceedings{Zheng_2021_CVPR,
    author    = {Zheng, Wenzhao and Wang, Chengkun and Lu, Jiwen and Zhou, Jie},
    title     = {Deep Compositional Metric Learning},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {9320-9329}
}


Mihai Fieraru, Mihai Zanfir, Silviu Cristian Pirlea, Vlad Olaru, Cristian Sminchisescu

Abstract: I went to the gym today, but how well did I do? And where should I improve? Ah, my back hurts slightly... User engagement can be sustained and injuries avoided by being able to reconstruct 3d human pose and motion, relate it to good training practices, identify errors, and provide early, real-time feedback. In this paper we introduce the first automatic system, AIFit, that performs 3d human sensing for fitness training. The system can be used at home, outdoors, or at the gym. AIFit is able to reconstruct 3d human pose and motion, reliably segment exercise repetitions, and identify in real-time the deviations between standards learnt from trainers, and the execution of a trainee. As a result, localized, quantitative feedback for correct execution of exercises, reduced risk of injury, and continuous improvement is possible. To support research and evaluation, we introduce the first large scale dataset, Fit3D, containing over 3 million images and corresponding 3d human shape and motion capture ground truth configurations, with over 37 repeated exercises, covering all the major muscle groups, performed by instructors and trainees. Our statistical coach is governed by a global parameter that captures how critical it should be of a trainee's performance. This is an important aspect that helps adapt to a student's level of fitness (i.e. beginner vs. advanced vs. expert), or to the expected accuracy of a 3d pose reconstruction method. We show that, for different values of the global parameter, our feedback system based on 3d pose estimates achieves good accuracy compared to the one based on ground-truth motion capture. Our statistical coach offers feedback in natural language, and with spatio-temporal visual grounding.

@InProceedings{Fieraru_2021_CVPR,
    author    = {Fieraru, Mihai and Zanfir, Mihai and Pirlea, Silviu Cristian and Olaru, Vlad and Sminchisescu, Cristian},
    title     = {AIFit: Automatic 3D Human-Interpretable Feedback Models for Fitness Training},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {9919-9928}
}


Abstract: We present a method to estimate dense depth by optimizing a sparse set of points such that their diffusion into a depth map minimizes a multi-view reprojection error from RGB supervision. We optimize point positions, depths, and weights with respect to the loss by differential splatting that models points as Gaussians with analytic transmittance. Further, we develop an efficient optimization routine that can simultaneously optimize the 50k+ points required for complex scene reconstruction. We validate our routine using ground truth data and show high reconstruction quality. Then, we apply this to light field and wider baseline images via self supervision, and show improvements in both average and outlier error for depth maps diffused from inaccurate sparse points. Finally, we compare qualitative and quantitative results to image processing and deep learning methods.

@InProceedings{Khan_2021_CVPR,
    author    = {Khan, Numair and Kim, Min H. and Tompkin, James},
    title     = {Differentiable Diffusion for Dense Depth Estimation From Multi-View Images},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8912-8921}
}


Jiashun Wang, Huazhe Xu, Jingwei Xu, Sifei Liu, Xiaolong Wang

Abstract: Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity. While many efforts have been made on generating realistic and natural human motion, most approaches neglect the importance of modeling human-scene interactions and affordances. On the other hand, affordance reasoning (e.g., standing on the floor or sitting on the chair) has mainly been studied with static human pose and gestures, and it has rarely been addressed with human motion. In this paper, we propose to bridge human motion synthesis and scene affordance reasoning. We present a hierarchical generative framework which synthesizes long-term 3D human motion conditioning on the 3D scene structure. We also further enforce multiple geometry constraints between the human mesh and scene point clouds via optimization to improve realistic synthesis. Our experiments show significant improvements over previous approaches on generating natural and physically plausible human motion in a scene.

@InProceedings{Wang_2021_CVPR,
    author    = {Wang, Jiashun and Xu, Huazhe and Xu, Jingwei and Liu, Sifei and Wang, Xiaolong},
    title     = {Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {9401-9411}
}


Xiaohan Ding, Xiangyu Zhang, Jungong Han, Guiguang Ding

Abstract: We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single convolution by combining diverse branches of different scales and complexities to enrich the feature space, including sequences of convolutions, multi-scale convolutions, and average pooling. After training, a DBB can be equivalently converted into a single conv layer for deployment. Unlike the advancements of novel ConvNet architectures, DBB complicates the training-time microstructure while maintaining the macro architecture, so that it can be used as a drop-in replacement for regular conv layers of any architecture. In this way, the model can be trained to reach a higher level of performance and then transformed into the original inference-time structure for inference. DBB improves ConvNets on image classification (up to 1.9% higher top-1 accuracy on ImageNet), object detection and semantic segmentation. The PyTorch code and models are released at https://github.com/DingXiaoH/DiverseBranchBlock.

@InProceedings{Ding_2021_CVPR,
    author    = {Ding, Xiaohan and Zhang, Xiangyu and Han, Jungong and Ding, Guiguang},
    title     = {Diverse Branch Block: Building a Convolution as an Inception-Like Unit},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10886-10895}
}


Xin Deng, Hao Wang, Mai Xu, Yichen Guo, Yuhang Song, Li Yang

Abstract: The omnidirectional images (ODIs) are usually at low-resolution, due to the constraints of collection, storage and transmission. The traditional two-dimensional (2D) image super-resolution methods are not effective for spherical ODIs, because ODIs tend to have non-uniformly distributed pixel density and varying texture complexity across latitudes. In this work, we propose a novel latitude adaptive upscaling network (LAU-Net) for ODI super-resolution, which allows pixels at different latitudes to adopt distinct upscaling factors. Specifically, we introduce a Laplacian multi-level separation architecture to split an ODI into different latitude bands, and hierarchically upscale them with different factors. In addition, we propose a deep reinforcement learning scheme with a latitude adaptive reward, in order to automatically select optimal upscaling factors for different latitude bands. To the best of our knowledge, LAU-Net is the first attempt to consider the latitude difference for ODI super-resolution. Extensive results demonstrate that our LAU-Net significantly advances the super-resolution performance for ODIs.

@InProceedings{Deng_2021_CVPR,
    author    = {Deng, Xin and Wang, Hao and Xu, Mai and Guo, Yichen and Song, Yuhang and Yang, Li},
    title     = {LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {9189-9198}
}


Christian Tomani, Sebastian Gruber, Muhammed Ebrar Erdem, Daniel Cremers, Florian Buettner

Abstract: We address the problem of uncertainty calibration. While standard deep neural networks typically yield uncalibrated predictions, calibrated confidence scores that are representative of the true likelihood of a prediction can be achieved using post-hoc calibration methods. However, to date, the focus of these approaches has been on in-domain calibration. Our contribution is two-fold. First, we show that existing post-hoc calibration methods yield highly over-confident predictions under domain shift. Second, we introduce a simple strategy where perturbations are applied to samples in the validation set before performing the post-hoc calibration step. In extensive experiments, we demonstrate that this perturbation step results in substantially better calibration under domain shift on a wide range of architectures and modelling tasks.

@InProceedings{Tomani_2021_CVPR,
    author    = {Tomani, Christian and Gruber, Sebastian and Erdem, Muhammed Ebrar and Cremers, Daniel and Buettner, Florian},
    title     = {Post-Hoc Uncertainty Calibration for Domain Drift Scenarios},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10124-10132}
}


Tianyu Ding, Luming Liang, Zhihui Zhu, Ilya Zharkov

Abstract: DNN-based frame interpolation--that generates the intermediate frames given two consecutive frames--typically relies on heavy model architectures with a huge number of features, preventing them from being deployed on systems with limited resources, e.g., mobile devices. We propose a compression-driven network design for frame interpolation (CDFI), that leverages model pruning through sparsity-inducing optimization to significantly reduce the model size while achieving superior performance. Concretely, we first compress the recently proposed AdaCoF model and show that a 10X compressed AdaCoF performs similarly as its original counterpart; then we further improve this compressed model by introducing a multi-resolution warping module, which boosts visual consistencies with multi-level details. As a consequence, we achieve a significant performance gain with only a quarter in size compared with the original AdaCoF. Moreover, our model performs favorably against other state-of-the-arts in a broad range of datasets. Finally, the proposed compression-driven framework is generic and can be easily transferred to other DNN-based frame interpolation algorithm. Our source code is available at https://github.com/tding1/CDFI.

@InProceedings{Ding_2021_CVPR,
    author    = {Ding, Tianyu and Liang, Luming and Zhu, Zhihui and Zharkov, Ilya},
    title     = {CDFI: Compression-Driven Network Design for Frame Interpolation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8001-8011}
}


Haoyang Zhang, Ying Wang, Feras Dayoub, Niko Sunderhauf

Abstract: Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted localization scores to rank candidates. However, neither option results in a reliable ranking, thus degrading detection performance. In this paper, we propose to learn an Iou-Aware Classification Score (IACS) as a joint representation of object presence confidence and localization accuracy. We show that dense object detectors can achieve a more accurate ranking of candidate detections based on the IACS. We design a new loss function, named Varifocal Loss, to train a dense object detector to predict the IACS, and propose a new star-shaped bounding box feature representation for IACS prediction and bounding box refinement. Combining these two new components and a bounding box refinement branch, we build an IoU-aware dense object detector based on the FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by  2.0 AP with different backbones. Our best model VFNet-X-1200 with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO test-dev, which is state-of-the-art among various object detectors. Code is available at: https://github.com/hyz-xmaster/VarifocalNet.

@InProceedings{Zhang_2021_CVPR,
    author    = {Zhang, Haoyang and Wang, Ying and Dayoub, Feras and Sunderhauf, Niko},
    title     = {VarifocalNet: An IoU-Aware Dense Object Detector},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8514-8523}
}


Shang-Hua Gao, Qi Han, Duo Li, Ming-Ming Cheng, Pai Peng

Abstract: Batch Normalization (BatchNorm) has become the default component in modern neural networks to stabilize training. In BatchNorm, centering and scaling operations, along with mean and variance statistics, are utilized for feature standardization over the batch dimension. The batch dependency of BatchNorm enables stable training and better representation of the network, while inevitably ignores the representation differences among instances. We propose to add a simple yet effective feature calibration scheme into the centering and scaling operations of BatchNorm, enhancing the instance-specific representations with the negligible computational cost. The centering calibration strengthens informative features and reduces noisy features. The scaling calibration restricts the feature intensity to form a more stable feature distribution. Our proposed variant of BatchNorm, namely Representative BatchNorm, can be plugged into existing methods to boost the performance of various tasks such as classification, detection, and segmentation. The source code is available in http://mmcheng.net/rbn.

@InProceedings{Gao_2021_CVPR,
    author    = {Gao, Shang-Hua and Han, Qi and Li, Duo and Cheng, Ming-Ming and Peng, Pai},
    title     = {Representative Batch Normalization With Feature Calibration},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8669-8679}
}


Chi Zhang, Baoxiong Jia, Song-Chun Zhu, Yixin Zhu

Abstract: Spatial-temporal reasoning is a challenging task in Artificial Intelligence (AI) due to its demanding but unique nature: a theoretic requirement on representing and reasoning based on spatial-temporal knowledge in mind, and an applied requirement on a high-level cognitive system capable of navigating and acting in space and time. Recent works have focused on an abstract reasoning task of this kind---Raven's Progressive Matrices (RPM). Despite the encouraging progress on RPM that achieves human-level performance in terms of accuracy, modern approaches have neither a treatment of human-like reasoning on generalization, nor a potential to generate answers. To fill in this gap, we propose a neuro-symbolic Probabilistic Abduction and Execution (PrAE) learner; central to the PrAE learner is the process of probabilistic abduction and execution on a probabilistic scene representation, akin to the mental manipulation of objects. Specifically, we disentangle perception and reasoning from a monolithic model. The neural visual perception frontend predicts objects' attributes, later aggregated by a scene inference engine to produce a probabilistic scene representation. In the symbolic logical reasoning backend, the PrAE learner uses the representation to abduce the hidden rules. An answer is predicted by executing the rules on the probabilistic representation. The entire system is trained end-to-end in an analysis-by-synthesis manner without any visual attribute annotations. Extensive experiments demonstrate that the PrAE learner improves cross-configuration generalization and is capable of rendering an answer, in contrast to prior works that merely make a categorical choice from candidates.

@InProceedings{Zhang_2021_CVPR,
    author    = {Zhang, Chi and Jia, Baoxiong and Zhu, Song-Chun and Zhu, Yixin},
    title     = {Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {9736-9746}
}


Hyeonseob Nam, HyunJae Lee, Jongchan Park, Wonjun Yoon, Donggeun Yoo

Abstract: Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies suggest that one of the main causes of this problem is CNNs' strong inductive bias towards image styles (i.e. textures) which are sensitive to domain changes, rather than contents (i.e. shapes). Inspired by this, we propose to reduce the intrinsic style bias of CNNs to close the gap between domains. Our Style-Agnostic Networks (SagNets) disentangle style encodings from class categories to prevent style biased predictions and focus more on the contents. Extensive experiments show that our method effectively reduces the style bias and makes the model more robust under domain shift. It achieves remarkable performance improvements in a wide range of cross-domain tasks including domain generalization, unsupervised domain adaptation, and semi-supervised domain adaptation on multiple datasets.

@InProceedings{Nam_2021_CVPR,
    author    = {Nam, Hyeonseob and Lee, HyunJae and Park, Jongchan and Yoon, Wonjun and Yoo, Donggeun},
    title     = {Reducing Domain Gap by Reducing Style Bias},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8690-8699}
}


Chenchen Zhu, Fangyi Chen, Uzair Ahmed, Zhiqiang Shen, Marios Savvides

Abstract: Few-shot object detection is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data. Its performance is largely affected by the data scarcity of novel classes. But the semantic relation between the novel classes and the base classes is constant regardless of the data availability. In this work, we investigate utilizing this semantic relation together with the visual information and introduce explicit relation reasoning into the learning of novel object detection. Specifically, we represent each class concept by a semantic embedding learned from a large corpus of text. The detector is trained to project the image representations of objects into this embedding space. We also identify the problems of trivially using the raw embeddings with a heuristic knowledge graph and propose to augment the embeddings with a dynamic relation graph. As a result, our few-shot detector, termed SRR-FSD, is robust and stable to the variation of shots of novel objects. Experiments show that SRR-FSD can achieve competitive results at higher shots, and more importantly, a significantly better performance given both lower explicit and implicit shots. The benchmark protocol with implicit shots removed from the pretrained classification dataset can serve as a more realistic setting for future research.

@InProceedings{Zhu_2021_CVPR,
    author    = {Zhu, Chenchen and Chen, Fangyi and Ahmed, Uzair and Shen, Zhiqiang and Savvides, Marios},
    title     = {Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8782-8791}
}


Song Guo, Jingya Wang, Xinchao Wang, Dacheng Tao

Abstract: Modern online multiple object tracking (MOT) methods usually focus on two directions to improve tracking performance. One is to predict new positions in an incoming frame based on tracking information from previous frames, and the other is to enhance data association by generating more discriminative identity embeddings. Some works combined both directions within one framework but handled them as two individual tasks, thus gaining little mutual benefits. In this paper, we propose a novel unified model with synergy between position prediction and embedding association. The two tasks are linked by temporal-aware target attention and distractor attention, as well as identity-aware memory aggregation model. Specifically, the attention modules can make the prediction focus more on targets and less on distractors, therefore more reliable embeddings can be extracted accordingly for association. On the other hand, such reliable embeddings can boost identity-awareness through memory aggregation, hence strengthen attention modules and suppress drifts. In this way, the synergy between position prediction and embedding association is achieved, which leads to strong robustness to occlusions. Extensive experiments demonstrate the superiority of our proposed model over a wide range of existing methods on MOTChallenge benchmarks. Our code and models are publicly available at https://github.com/songguocode/TADAM

@InProceedings{Guo_2021_CVPR,
    author    = {Guo, Song and Wang, Jingya and Wang, Xinchao and Tao, Dacheng},
    title     = {Online Multiple Object Tracking With Cross-Task Synergy},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8136-8145}
}


Prior Based Human Completion

Zibo Zhao, Wen Liu, Yanyu Xu, Xianing Chen, Weixin Luo, Lei Jin, Bohui Zhu, Tong Liu, Binqiang Zhao, Shenghua Gao

Abstract: We study a very challenging task, human image completion, which tries to recover the human body part with a reasonable human shape from the corrupted region. Since each human body part is unique, it is infeasible to restore the missing part by borrowing textures from other visible regions. Thus, we propose two types of learned priors to compensate for the damaged region. One is a structure prior, it uses a human parsing map to represent the human body structure. The other is a structure-texture correlation prior. It learns a structure and a texture memory bank, which encodes the common body structures and texture patterns, respectively. With the aid of these memory banks, the model could utilize the visible pattern to query and fetch a similar structure and texture pattern to introduce additional reasonable structures and textures for the corrupted region. Besides, since multiple potential human shapes are underlying the corrupted region, we propose multi-scale structure discriminators to further restore a plausible topological structure. Experiments on various large-scale benchmarks demonstrate the effectiveness of our proposed method.

@InProceedings{Zhao_2021_CVPR,
    author    = {Zhao, Zibo and Liu, Wen and Xu, Yanyu and Chen, Xianing and Luo, Weixin and Jin, Lei and Zhu, Bohui and Liu, Tong and Zhao, Binqiang and Gao, Shenghua},
    title     = {Prior Based Human Completion},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {7951-7961}
}


Jiaxing Chen, Xinyang Jiang, Fudong Wang, Jun Zhang, Feng Zheng, Xing Sun, Wei-Shi Zheng

Abstract: It is well acknowledged that person re-identification (person ReID) highly relies on visual texture information like clothing. Despite significant progress has been made in recent years, texture-confusing situations like clothing changing and persons wearing the same clothes receive little attention from most existing ReID methods. In this paper, rather than relying on texture based information, we propose to improve the robustness of person ReID against clothing texture by exploiting the information of a person's 3D shape. Existing shape learning schemas for person ReID either ignore the 3D information of a person, or require extra physical devices to collect 3D source data. Differently, we propose a novel ReID learning framework that directly extracts a texture-insensitive 3D shape embedding from a 2D image by adding 3D body reconstruction as an auxiliary task and regularization, called 3D Shape Learning (3DSL). The 3D reconstruction based regularization forces the ReID model to decouple the 3D shape information from the visual texture, and acquire discriminative 3D shape ReID features. To solve the problem of lacking 3D ground truth, we design an adversarial self-supervised projection (ASSP) model, performing 3D reconstruction without ground truth. Extensive experiments on common ReID datasets and texture-confusing datasets validate the effectiveness of our model.

@InProceedings{Chen_2021_CVPR,
    author    = {Chen, Jiaxing and Jiang, Xinyang and Wang, Fudong and Zhang, Jun and Zheng, Feng and Sun, Xing and Zheng, Wei-Shi},
    title     = {Learning 3D Shape Feature for Texture-Insensitive Person Re-Identification},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8146-8155}
}


Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, Chi Zhang

Abstract: Emerging interests have been brought to recognize previously unseen objects given very few training examples, known as few-shot object detection (FSOD). Recent researches demonstrate that good feature embedding is the key to reach favorable few-shot learning performance. We observe object proposals with different Intersection-of-Union (IoU) scores are analogous to the intra-image augmentation used in contrastive visual representation learning. And we exploit this analogy and incorporate supervised contrastive learning to achieve more robust objects representations in FSOD. We present Few-Shot object detection via Contrastive proposals Encoding (FSCE), a simple yet effective approach to learning contrastive-aware object proposal encodings that facilitate the classification of detected objects. We notice the degradation of average precision (AP) for rare objects mainly comes from misclassifying novel instances as confusable classes. And we ease the misclassification issues by promoting instance level intra-class compactness and inter-class variance via our contrastive proposal encoding loss (CPE loss). Our design outperforms current state-of-the-art works in any shot and all data splits, with up to +8.8% on standard benchmark PASCAL VOC and +2.7% on challenging COCO benchmark. Code is available at: https://github.com/ MegviiDetection/FSCE.

@InProceedings{Sun_2021_CVPR,
    author    = {Sun, Bo and Li, Banghuai and Cai, Shengcai and Yuan, Ye and Zhang, Chi},
    title     = {FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {7352-7362}
}


Tingting Liang, Yongtao Wang, Zhi Tang, Guosheng Hu, Haibin Ling

Abstract: Recently, neural architecture search (NAS) has been exploited to design feature pyramid networks (FPNs) and achieved promising results for visual object detection. Encouraged by the success, we propose a novel One-Shot Path Aggregation Network Architecture Search (OPANAS) algorithm, which significantly improves both searching efficiency and detection accuracy. Specifically, we first introduce six heterogeneous information paths to build our search space, namely top-down, bottom-up, fusing-splitting, scale-equalizing, skip-connect, and none. Second, we propose a novel search space of FPNs, in which each FPN candidate is represented by a densely-connected directed acyclic graph (each node is a feature pyramid and each edge is one of the six heterogeneous information paths). Third, we propose an efficient one-shot search method to find the optimal path aggregation architecture, that is, we first train a super-net and then find the optimal candidate with an evolutionary algorithm. Experimental results demonstrate the efficacy of the proposed OPANAS for object detection: (1) OPANAS is more efficient than state-of-the-art methods (e.g., NAS-FPN and Auto-FPN), at significantly smaller searching cost (e.g., only 4 GPU days on MS-COCO); (2) the optimal architecture found by OPANAS significantly improves main-stream detectors including RetinaNet, Faster R-CNN and Cascade R-CNN, by 2.3 3.2 % mAP comparing to their FPN counterparts; and (3) a new state-of-the-art accuracy-speed trade-off (52.2 % mAP at 7.6 FPS) at smaller training costs than comparable state-of-the-arts.

@InProceedings{Liang_2021_CVPR,
    author    = {Liang, Tingting and Wang, Yongtao and Tang, Zhi and Hu, Guosheng and Ling, Haibin},
    title     = {OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10195-10203}
}


Tianyu He, Xu Shen, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua

Abstract: Driven by the success of deep learning, the last decade has seen rapid advances in person re-identification (re-ID). Nonetheless, most of approaches assume that the input is given with the fulfillment of expectations, while imperfect input remains rarely explored to date, which is a non-trivial problem since directly apply existing methods without adjustment can cause significant performance degradation. In this paper, we focus on recognizing partial (flawed) input with the assistance of proposed Part-Part Correspondence Learning (PPCL), a self-supervised learning framework that learns correspondence between image patches without any additional part-level supervision. Accordingly, we propose Part-Part Cycle (PP-Cycle) constraint and Part-Part Triplet (PP-Triplet) constraint that exploit the duality and uniqueness between corresponding image patches respectively. We verify our proposed PPCL on several partial person re-ID benchmarks. Experimental results demonstrate that our approach can surpass previous methods in terms of the standard evaluation metric.

@InProceedings{He_2021_CVPR,
    author    = {He, Tianyu and Shen, Xu and Huang, Jianqiang and Chen, Zhibo and Hua, Xian-Sheng},
    title     = {Partial Person Re-Identification With Part-Part Correspondence Learning},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {9105-9115}
}


Transformer Tracking

Xin Chen, Bin Yan, Jiawen Zhu, Dong Wang, Xiaoyun Yang, Huchuan Lu

Abstract: Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion manner to consider the similarity between the template and the search region. However, the correlation operation itself is a local linear matching process, leading to lose semantic information and fall into local optimum easily, which may be the bottleneck of designing high-accuracy tracking algorithms. Is there any better feature fusion method than correlation? To address this issue, inspired by Transformer, this work presents a novel attention-based feature fusion network, which effectively combines the template and search region features solely using attention. Specifically, the proposed method includes an ego-context augment module based on self-attention and a cross-feature augment module based on cross-attention. Finally, we present a Transformer tracking (named TransT) method based on the Siamese-like feature extraction backbone, the designed attention-based fusion mechanism, and the classification and regression head. Experiments show that our TransT achieves very promising results on six challenging datasets, especially on large-scale LaSOT, TrackingNet, and GOT-10k benchmarks. Our tracker runs at approximatively 50 fps on GPU. Code and models are available at https://github.com/chenxin-dlut/TransT.

@InProceedings{Chen_2021_CVPR,
    author    = {Chen, Xin and Yan, Bin and Zhu, Jiawen and Wang, Dong and Yang, Xiaoyun and Lu, Huchuan},
    title     = {Transformer Tracking},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8126-8135}
}


Hanqing Wang, Wenguan Wang, Wei Liang, Caiming Xiong, Jianbing Shen

Abstract: Recently, numerous algorithms have been developed to tackle the problem of vision-language navigation (VLN), i.e., entailing an agent to navigate 3D environments through following linguistic instructions. However, current VLN agents simply store their past experiences/observations as latent states in recurrent networks, failing to capture environment layouts and make long-term planning. To address these limitations, we propose a crucial architecture, called Structured Scene Memory (SSM). It is compartmentalized enough to accurately memorize the percepts during navigation. It also serves as a structured scene representation, which captures and disentangles visual and geometric cues in the environment. SSM has a collect-read controller that adaptively collects information for supporting current decision making and mimics iterative algorithms for long-range reasoning. As SSM provides a complete action space, i.e., all the navigable places on the map, a frontier-exploration based navigation decision making strategy is introduced to enable efficient and global planning. Experiment results on two VLN datasets (i.e., R2R and R4R) show that our method achieves state-of-the-art performance on several metrics.

@InProceedings{Wang_2021_CVPR,
    author    = {Wang, Hanqing and Wang, Wenguan and Liang, Wei and Xiong, Caiming and Shen, Jianbing},
    title     = {Structured Scene Memory for Vision-Language Navigation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8455-8464}
}


Zefan Li, Chenxi Liu, Alan Yuille, Bingbing Ni, Wenjun Zhang, Wen Gao

Abstract: Unsupervised learning methods have recently shown their competitiveness against supervised training. Typically, these methods use a single objective to train the entire network. But one distinct advantage of unsupervised over supervised learning is that the former possesses more variety and freedom in designing the objective. In this work, we explore new dimensions of unsupervised learning by proposing the Progressive Stage-wise Learning (PSL) framework. For a given unsupervised task, we design multi-level tasks and define different learning stages for the deep network. Early learning stages are forced to focus on low-level tasks while late stages are guided to extract deeper information through harder tasks. We discover that by progressive stage-wise learning, unsupervised feature representation can be effectively enhanced. Our extensive experiments show that PSL consistently improves results for the leading unsupervised learning methods.

@InProceedings{Li_2021_CVPR,
    author    = {Li, Zefan and Liu, Chenxi and Yuille, Alan and Ni, Bingbing and Zhang, Wenjun and Gao, Wen},
    title     = {Progressive Stage-Wise Learning for Unsupervised Feature Representation Enhancement},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {9767-9776}
}


Yu Wang, Rui Zhang, Shuo Zhang, Miao Li, Yangyang Xia, Xishan Zhang, Shaoli Liu

Abstract: Domain adaptation methods face performance degradation in object detection, as the complexity of tasks require more about the transferability of the model. We propose a new perspective on how CNN models gain the transferability, viewing the weights of a model as a series of motion patterns. The directions of weights, and the gradients, can be divided into domain-specific and domain-invariant parts, and the goal of domain adaptation is to concentrate on the domain-invariant direction while eliminating the disturbance from domain-specific one. Current UDA object detection methods view the two directions as a whole while optimizing, which will cause domain-invariant direction mismatch even if the output features are perfectly aligned. In this paper, we propose the domain-specific suppression, an exemplary and generalizable constraint to the original convolution gradients in backpropagation to detach the two parts of directions and suppress the domain-specific one. We further validate our theoretical analysis and methods on several domain adaptive object detection tasks, including weather, camera configuration, and synthetic to real-world adaptation. Our experiment results show significant advance over the state-of-the-art methods in the UDA object detection field, performing a promotion of 10.2 12.2% mAP on all these domain adaptation scenarios.

@InProceedings{Wang_2021_CVPR,
    author    = {Wang, Yu and Zhang, Rui and Zhang, Shuo and Li, Miao and Xia, Yangyang and Zhang, Xishan and Liu, Shaoli},
    title     = {Domain-Specific Suppression for Adaptive Object Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {9603-9612}
}


Abstract: We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction consisting of two functions, one implicit field representing the coarse 3D shape and the other capturing the details. Given an input image, our network, coined D^2IM_Net, encodes it into global and local features which are respectively fed into two decoders. The base decoder uses the global features to reconstruct a coarse implicit field, while the detail decoder reconstructs, from the local features, two displacement maps, defined over the front and back sides of the captured object. The final 3D reconstruction is a fusion between the base shape and the displacement maps, with three losses enforcing the recovery of coarse shape, overall structure, and surface details via a novel Laplacian term.

@InProceedings{Li_2021_CVPR,
    author    = {Li, Manyi and Zhang, Hao},
    title     = {D2IM-Net: Learning Detail Disentangled Implicit Fields From Single Images},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10246-10255}
}


Hansheng Chen, Yuyao Huang, Wei Tian, Zhong Gao, Lu Xiong

Abstract: Object localization in 3D space is a challenging aspect in monocular 3D object detection. Recent advances in 6DoF pose estimation have shown that predicting dense 2D-3D correspondence maps between image and object 3D model and then estimating object pose via Perspective-n-Point (PnP) algorithm can achieve remarkable localization accuracy. Yet these methods rely on training with ground truth of object geometry, which is difficult to acquire in real outdoor scenes. To address this issue, we propose MonoRUn, a novel detection framework that learns dense correspondences and geometry in a self-supervised manner, with simple 3D bounding box annotations. To regress the pixel-related 3D object coordinates, we employ a regional reconstruction network with uncertainty awareness. For self-supervised training, the predicted 3D coordinates are projected back to the image plane. A Robust KL loss is proposed to minimize the uncertainty-weighted reprojection error. During testing phase, we exploit the network uncertainty by propagating it through all downstream modules. More specifically, the uncertainty-driven PnP algorithm is leveraged to estimate object pose and its covariance. Extensive experiments demonstrate that our proposed approach outperforms current state-of-the-art methods on KITTI benchmark.

@InProceedings{Chen_2021_CVPR,
    author    = {Chen, Hansheng and Huang, Yuyao and Tian, Wei and Gao, Zhong and Xiong, Lu},
    title     = {MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10379-10388}
}


Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, Richard Zhang

Abstract: Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting. In this work, we seek to utilize a large source domain for pretraining and transfer the diversity information from source to target. We propose to preserve the relative similarities and differences between instances in the source via a novel cross-domain distance consistency loss. To further reduce overfitting, we present an anchor-based strategy to encourage different levels of realism over different regions in the latent space. With extensive results in both photorealistic and non-photorealistic domains, we demonstrate qualitatively and quantitatively that our few-shot model automatically discovers correspondences between source and target domains and generates more diverse and realistic images than previous methods.

@InProceedings{Ojha_2021_CVPR,
    author    = {Ojha, Utkarsh and Li, Yijun and Lu, Jingwan and Efros, Alexei A. and Lee, Yong Jae and Shechtman, Eli and Zhang, Richard},
    title     = {Few-Shot Image Generation via Cross-Domain Correspondence},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10743-10752}
}


Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, Xiaowei Zhou

Abstract: This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will be ill-posed if the views are highly sparse. To solve this ill-posed problem, our key idea is to integrate observations over video frames. To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated. The deformable mesh also provides geometric guidance for the network to learn 3D representations more efficiently. Experiments on a newly collected multi-view dataset show that our approach outperforms prior works by a large margin in terms of the novel view synthesis quality. We also demonstrate the capability of our approach to reconstruct a moving person from a monocular video on the People-Snapshot dataset. We will release the code and dataset for reproducibility.

@InProceedings{Peng_2021_CVPR,
    author    = {Peng, Sida and Zhang, Yuanqing and Xu, Yinghao and Wang, Qianqian and Shuai, Qing and Bao, Hujun and Zhou, Xiaowei},
    title     = {Neural Body: Implicit Neural Representations With Structured Latent Codes for Novel View Synthesis of Dynamic Humans},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {9054-9063}
}


Yao-Chih Lee, Kuan-Wei Tseng, Yu-Ta Chen, Chien-Cheng Chen, Chu-Song Chen, Yi-Ping Hung

Abstract: Video stabilization is an essential component of visual quality enhancement. Early methods rely on feature tracking to recover either 2D or 3D frame motion, which suffer from the robustness of local feature extraction and tracking in shaky videos. Recently, learning-based methods seek to find frame transformations with high-level information via deep neural networks to overcome the robustness issue of feature tracking. Nevertheless, to our best knowledge, no learning-based methods leverage 3D cues for the transformation inference yet; hence they would lead to artifacts on complex scene-depth scenarios. In this paper, we propose Deep3D Stabilizer, a novel 3D depth-based learning method for video stabilization. We take advantage of the recent self-supervised framework on jointly learning depth and camera ego-motion estimation on raw videos. Our approach requires no data for pre-training but stabilizes the input video via 3D reconstruction directly. The rectification stage incorporates the 3D scene depth and camera motion to smooth the camera trajectory and synthesize the stabilized video. Unlike most one-size-fits-all learning-based methods, our smoothing algorithm allows users to manipulate the stability of a video efficiently. Experimental results on challenging benchmarks show that the proposed solution consistently outperforms the state-of-the-art methods on almost all motion categories.

@InProceedings{Lee_2021_CVPR,
    author    = {Lee, Yao-Chih and Tseng, Kuan-Wei and Chen, Yu-Ta and Chen, Chien-Cheng and Chen, Chu-Song and Hung, Yi-Ping},
    title     = {3D Video Stabilization With Depth Estimation by CNN-Based Optimization},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10621-10630}
}


Abstract: Attention has been an important mechanism for both humans and computer vision systems. While state-of-the-art models to predict attention focus on estimating a static probabilistic saliency map with free-viewing behavior, real-life scenarios are filled with tasks of varying types and complexities, and visual exploration is a temporal process that contributes to task performance. To bridge the gap, we conduct a first study to understand and predict the temporal sequences of eye fixations (a.k.a. scanpaths) during performing general tasks, and examine how scanpaths affect task performance. We present a new deep reinforcement learning method to predict scanpaths leading to different performances in visual question answering. Conditioned on a task guidance map, the proposed model learns question-specific attention patterns to generate scanpaths. It addresses the exposure bias in scanpath prediction with self-critical sequence training and designs a Consistency-Divergence loss to generate distinguishable scanpaths between correct and incorrect answers. The proposed model not only accurately predicts the spatio-temporal patterns of human behavior in visual question answering, such as fixation position, duration, and order, but also generalizes to free-viewing and visual search tasks, achieving human-level performance in all tasks and significantly outperforming the state of the art.

@InProceedings{Chen_2021_CVPR,
    author    = {Chen, Xianyu and Jiang, Ming and Zhao, Qi},
    title     = {Predicting Human Scanpaths in Visual Question Answering},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10876-10885}
}


Siyuan Qiao, Liang-Chieh Chen, Alan Yuille

Abstract: Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature Pyramid, which incorporates extra feedback connections from Feature Pyramid Networks into the bottom-up backbone layers. At the micro level, we propose Switchable Atrous Convolution, which convolves the features with different atrous rates and gathers the results using switch functions. Combining them results in DetectoRS, which significantly improves the performances of object detection. On COCO test-dev, DetectoRS achieves state-of-the-art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation. The code is made publicly available.

@InProceedings{Qiao_2021_CVPR,
    author    = {Qiao, Siyuan and Chen, Liang-Chieh and Yuille, Alan},
    title     = {DetectoRS: Detecting Objects With Recursive Feature Pyramid and Switchable Atrous Convolution},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10213-10224}
}


Hyungjun Kim, Jihoon Park, Changhun Lee, Jae-Joon Kim

Abstract: Binarization of neural network models is considered as one of the promising methods to deploy deep neural network models on resource-constrained environments such as mobile devices. However, Binary Neural Networks (BNNs) tend to suffer from severe accuracy degradation compared to the full-precision counterpart model. Several techniques were proposed to improve the accuracy of BNNs. One of the approaches is to balance the distribution of binary activations so that the amount of information in the binary activations becomes maximum. Based on extensive analysis, in stark contrast to previous work, we argue that unbalanced activation distribution can actually improve the accuracy of BNNs. We also show that adjusting the threshold values of binary activation functions results in the unbalanced distribution of the binary activation, which increases the accuracy of BNN models. Experimental results show that the accuracy of previous BNN models (e.g. XNOR-Net and Bi-Real-Net) can be improved by simply shifting the threshold values of binary activation functions without requiring any other modification.

@InProceedings{Kim_2021_CVPR,
    author    = {Kim, Hyungjun and Park, Jihoon and Lee, Changhun and Kim, Jae-Joon},
    title     = {Improving Accuracy of Binary Neural Networks Using Unbalanced Activation Distribution},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {7862-7871}
}


Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, Dahua Lin

Abstract: State-of-the-art methods for large-scale driving-scene LiDAR segmentation often project the point clouds to 2D space and then process them via 2D convolution. Although this corporation shows the competitiveness in the point cloud, it inevitably alters and abandons the 3D topology and geometric relations. A natural remedy is to utilize the 3D voxelization and 3D convolution network. However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited. An important reason is the property of the outdoor point cloud, namely sparsity and varying density. Motivated by this investigation, we propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern while maintaining these inherent properties. Moreover, a point-wise refinement module is introduced to alleviate the interference of lossy voxel-based label encoding. We evaluate the proposed model on two large-scale datasets , i.e., SemanticKITTI and nuScenes. Our method achieves the 1st place in the leaderboard of SemanticKITTI and outperforms existing methods on nuScenes with a noticeable margin. Furthermore, the proposed 3D framework also generalizes well to LiDAR panoptic segmentation and LiDAR 3D detection.

@InProceedings{Zhu_2021_CVPR,
    author    = {Zhu, Xinge and Zhou, Hui and Wang, Tai and Hong, Fangzhou and Ma, Yuexin and Li, Wei and Li, Hongsheng and Lin, Dahua},
    title     = {Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {9939-9948}
}


Xing Shen, Jirui Yang, Chunbo Wei, Bing Deng, Jianqiang Huang, Xian-Sheng Hua, Xiaoliang Cheng, Kewei Liang

Abstract: Binary grid mask representation is broadly used in instance segmentation. A representative instantiation is Mask R-CNN which predicts masks on a 28*28 binary grid. Generally, a low-resolution grid is not sufficient to capture the details, while a high-resolution grid dramatically increases the training complexity. In this paper, we propose a new mask representation by applying the discrete cosine transform(DCT) to encode the high-resolution binary grid mask into a compact vector. Our method, termed DCT-Mask, could be easily integrated into most pixel-based instance segmentation methods. Without any bells and whistles, DCT-Mask yields significant gains on different frameworks, backbones, datasets, and training schedules. It does not require any pre-processing or pre-training, and almost no harm to the running speed. Especially, for higher-quality annotations and more complex backbones, our method has a greater improvement. Moreover, we analyze the performance of our method from the perspective of the quality of mask representation. The main reason why DCT-Mask works well is that it obtains a high-quality mask representation with low complexity.

@InProceedings{Shen_2021_CVPR,
    author    = {Shen, Xing and Yang, Jirui and Wei, Chunbo and Deng, Bing and Huang, Jianqiang and Hua, Xian-Sheng and Cheng, Xiaoliang and Liang, Kewei},
    title     = {DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8720-8729}
}


Sk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul, Samet Oymak, Amit K. Roy-Chowdhury

Abstract: Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an unlabeled dataset by transferring knowledge from a labeled source data, which has been trained on similar tasks. However, most of these conventional UDA approaches have a strong assumption of having access to the source data during training, which may not be very practical due to privacy, security and storage concerns. A recent line of work addressed this problem and proposed an algorithm that transfers knowledge to the unlabeled target domain only from a single learned source model without requiring access to the source data. However, for adaptation purpose, if there are multiple trained source models available to choose from, this method has to go through adapting each and every model individually, to check for the best source. Thus, we ask the question: can we find the optimal combination of source models, with no source data and without target labels, whose performance is no worse than the single best source? To answer this, we propose a novel and efficient algorithm which automatically combines the source models with suitable weights in such a way that it performs at least as good as the best source model. We provide intuitive theoretical insights to justify our claim. Moreover, extensive experiments are conducted on several benchmark datasets to show the effectiveness of our algorithm, where in most cases, our method not only reaches best source accuracy but also outperform it.

@InProceedings{Ahmed_2021_CVPR,
    author    = {Ahmed, Sk Miraj and Raychaudhuri, Dripta S. and Paul, Sujoy and Oymak, Samet and Roy-Chowdhury, Amit K.},
    title     = {Unsupervised Multi-Source Domain Adaptation Without Access to Source Data},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10103-10112}
}


Abstract: Automatic methods for detecting presentation attacks are essential to ensure the reliable use of facial recognition technology. Most of the methods available in the literature for presentation attack detection (PAD) fails in generalizing to unseen attacks. In recent years, multi-channel methods have been proposed to improve the robustness of PAD systems. Often, only a limited amount of data is available for additional channels, which limits the effectiveness of these methods. In this work, we present a new framework for PAD that uses RGB and depth channels together with a novel loss function. The new architecture uses complementary information from the two modalities while reducing the impact of overfitting. Essentially, a cross-modal focal loss function is proposed to modulate the loss contribution of each channel as a function of the confidence of individual channels. Extensive evaluations in two publicly available datasets demonstrate the effectiveness of the proposed approach.

@InProceedings{George_2021_CVPR,
    author    = {George, Anjith and Marcel, Sebastien},
    title     = {Cross Modal Focal Loss for RGBD Face Anti-Spoofing},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {7882-7891}
}


Abstract: We address the problem of performing backpropagation for computation graphs involving 3D transformation groups SO(3), SE(3), and Sim(3). 3D transformation groups are widely used in 3D vision and robotics, but they do not form vector spaces and instead lie on smooth manifolds. The standard backpropagation approach, which embeds 3D transformations in Euclidean spaces, suffers from numerical difficulties. We introduce a new library, which exploits the group structure of 3D transformations and performs backpropagation in the tangent spaces of manifolds. We show that our approach is numerically more stable, easier to implement, and beneficial to a diverse set of tasks. Our plug-and-play PyTorch library is available at https://github.com/princeton-vl/lietorch.

@InProceedings{Teed_2021_CVPR,
    author    = {Teed, Zachary and Deng, Jia},
    title     = {Tangent Space Backpropagation for 3D Transformation Groups},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10338-10347}
}


Shuang Li, JinMing Zhang, Wenxuan Ma, Chi Harold Liu, Wei Li

Abstract: Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy. Most prior DA approaches leverage complicated and powerful deep neural networks to improve the adaptation capacity and have shown remarkable success. However, they may have a lack of applicability to real-world situations such as real-time interaction, where low target inference latency is an essential requirement under limited computational budget. In this paper, we tackle the problem by proposing a dynamic domain adaptation (DDA) framework, which can simultaneously achieve efficient target inference in low-resource scenarios and inherit the favorable cross-domain generalization brought by DA. In contrast to static models, as a simple yet generic method, DDA can integrate various domain confusion constraints into any typical adaptive network, where multiple intermediate classifiers can be equipped to infer "easier" and "harder" target data dynamically. Moreover, we present two novel strategies to further boost the adaptation performance of multiple prediction exits: 1) a confidence score learning strategy to derive accurate target pseudo labels by fully exploring the prediction consistency of different classifiers; 2) a class-balanced self-training strategy to explicitly adapt multi-stage classifiers from source to target without losing prediction diversity. Extensive experiments on multiple benchmarks are conducted to verify that DDA can consistently improve the adaptation performance and accelerate target inference under domain shift and limited resources scenarios.

@InProceedings{Li_2021_CVPR,
    author    = {Li, Shuang and Zhang, JinMing and Ma, Wenxuan and Liu, Chi Harold and Li, Wei},
    title     = {Dynamic Domain Adaptation for Efficient Inference},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {7832-7841}
}


Davis Wertheimer, Luming Tang, Bharath Hariharan

Abstract: In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The ability of the network to reconstruct a query feature map from support features of a given class predicts membership of the query in that class. We introduce a novel mechanism for few-shot classification by regressing directly from support features to query features in closed form, without introducing any new modules or large-scale learnable parameters. The resulting Feature Map Reconstruction Networks are both more performant and computationally efficient than previous approaches. We demonstrate consistent and substantial accuracy gains on four fine-grained benchmarks with varying neural architectures. Our model is also competitive on the non-fine-grained mini-ImageNet and tiered-ImageNet benchmarks with minimal bells and whistles.

@InProceedings{Wertheimer_2021_CVPR,
    author    = {Wertheimer, Davis and Tang, Luming and Hariharan, Bharath},
    title     = {Few-Shot Classification With Feature Map Reconstruction Networks},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8012-8021}
}


Cody Reading, Ali Harakeh, Julia Chae, Steven L. Waslander

Abstract: Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a solution with simple configuration compared to typical multi-sensor systems. The main challenge in monocular 3D detection lies in accurately predicting object depth, which must be inferred from object and scene cues due to the lack of direct range measurement. Many methods attempt to directly estimate depth to assist in 3D detection, but show limited performance as a result of depth inaccuracy. Our proposed solution, Categorical Depth Distribution Network (CaDDN), uses a predicted categorical depth distribution for each pixel to project rich contextual feature information to the appropriate depth interval in 3D space. We then use the computationally efficient bird's-eye-view projection and single-stage detector to produce the final output bounding boxes. We design CaDDN as a fully differentiable end-to-end approach for joint depth estimation and object detection. We validate our approach on the KITTI 3D object detection benchmark, where we rank 1st among published monocular methods. We also provide the first monocular 3D detection results on the newly released Waymo Open Dataset. We provide a code release for CaDDN which will be made publicly available.

@InProceedings{Reading_2021_CVPR,
    author    = {Reading, Cody and Harakeh, Ali and Chae, Julia and Waslander, Steven L.},
    title     = {Categorical Depth Distribution Network for Monocular 3D Object Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8555-8564}
}


Huiyuan Yang, Lijun Yin, Yi Zhou, Jiuxiang Gu

Abstract: Recent study on detecting facial action units (AU) has utilized auxiliary information (i.e., facial landmarks, relationship among AUs and expressions, web facial images, etc.), in order to improve the AU detection performance. As of now, no semantic information of AUs has yet been explored for such a task. As a matter of fact, AU semantic descriptions provide much more information than the binary AU labels alone, thus we propose to exploit the Semantic Embedding and Visual feature (SEV-Net) for AU detection. More specifically, AU semantic embeddings are obtained through both Intra-AU and Inter-AU attention modules, where the Intra-AU attention module captures the relation among words within each sentence that describes individual AU, and the Inter-AU attention module focuses on the relation among those sentences. The learned AU semantic embeddings are then used as guidance for the generation of attention maps through a cross-modality attention network. The generated cross-modality attention maps are further used as weights for the aggregated feature. Our proposed method is unique in that the semantic features are exploited as the first of this kind. The approach has been evaluated on three public AU-coded facial expression databases, and has achieved a superior performance than the state-of-the-art peer methods.

@InProceedings{Yang_2021_CVPR,
    author    = {Yang, Huiyuan and Yin, Lijun and Zhou, Yi and Gu, Jiuxiang},
    title     = {Exploiting Semantic Embedding and Visual Feature for Facial Action Unit Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10482-10491}
}


Giang Truong, Huu Le, David Suter, Erchuan Zhang, Syed Zulqarnain Gilani

Abstract: Robust model fitting is a core algorithm in a large number of computer vision applications. Solving this problem efficiently for highly contaminated datasets is, however, still challenging due to its underlying computational complexity. Recent attention has been focused on learning-based algorithms. However, most approaches are supervised (which require a large amount of labelled training data). In this paper, we introduce a novel unsupervised learning framework that learns to directly solve robust model fitting. Unlike other methods, our work is agnostic to the underlying input features, and can be easily generalized to a wide variety of LP-type problems with quasi-convex residuals. We empirically show that our method outperforms existing unsupervised learning approaches, and achieves competitive results compared to traditional methods on several important computer vision problems.

@InProceedings{Truong_2021_CVPR,
    author    = {Truong, Giang and Le, Huu and Suter, David and Zhang, Erchuan and Gilani, Syed Zulqarnain},
    title     = {Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10348-10357}
}


Xiaolin Song, Sicheng Zhao, Jingyu Yang, Huanjing Yue, Pengfei Xu, Runbo Hu, Hua Chai

Abstract: Unsupervised domain adaptation (UDA) for human action recognition is a practical and challenging problem. Compared with image-based UDA, video-based UDA is comprehensive to bridge the domain shift on both spatial representation and temporal dynamics. Most previous works focus on short-term modeling and alignment with frame-level or clip-level features, which is not discriminative sufficiently for video-based UDA tasks. To address these problems, in this paper we propose to establish the cross-modal domain alignment via self-supervised contrastive framework, i.e., spatio-temporal contrastive domain adaptation (STCDA), to learn the joint clip-level and video-level representation alignment. Since the effective representation is modeled from unlabeled data by self-supervised learning (SSL), spatio-temporal contrastive learning (STCL) is proposed to explore the useful long-term feature representation for classification, using self-supervision setting trained from the contrastive clip/video pairs with positive or negative properties. Besides, we involve a novel domain metric scheme, i.e., video-based contrastive alignment (VCA), to optimize the category-aware video-level alignment and generalization between source and target. The proposed STCDA achieves stat-of-the-art results on several UDA benchmarks for action recognition.

@InProceedings{Song_2021_CVPR,
    author    = {Song, Xiaolin and Zhao, Sicheng and Yang, Jingyu and Yue, Huanjing and Xu, Pengfei and Hu, Runbo and Chai, Hua},
    title     = {Spatio-temporal Contrastive Domain Adaptation for Action Recognition},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {9787-9795}
}


Yang Fu, Sifei Liu, Umar Iqbal, Shalini De Mello, Humphrey Shi, Jan Kautz

Abstract: Tracking segmentation masks of multiple instances has been intensively studied, but still faces two fundamental challenges: 1) the requirement of large-scale, frame-wise annotation, and 2) the complexity of two-stage approaches. To resolve these challenges, we introduce a novel semi-supervised framework by learning instance tracking networks with only a labeled image dataset and unlabeled video sequences. With an instance contrastive objective, we learn an embedding to discriminate each instance from the others. We show that even when only trained with images, the learned feature representation is robust to instance appearance variations, and is thus able to track objects steadily across frames. We further enhance the tracking capability of the embedding by learning correspondence from unlabeled videos in a self-supervised manner. In addition, we integrate this module into single-stage instance segmentation and pose estimation frameworks, which significantly reduce the computational complexity of tracking compared to two-stage networks. We conduct experiments on the YouTube-VIS and PoseTrack datasets. Without any video annotation efforts, our proposed method can achieve comparable or even better performance than most fully-supervised methods.

@InProceedings{Fu_2021_CVPR,
    author    = {Fu, Yang and Liu, Sifei and Iqbal, Umar and De Mello, Shalini and Shi, Humphrey and Kautz, Jan},
    title     = {Learning to Track Instances without Video Annotations},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8680-8689}
}


Isma Hadji, Konstantinos G. Derpanis, Allan D. Jepson

Abstract: We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e.g., videos) of the same process (e.g., human action). The main idea is to use the global temporal ordering of latent correspondences across sequence pairs as a supervisory signal. In particular, we propose a loss based on scoring the optimal sequence alignment to train an embedding network. Our loss is based on a novel probabilistic path finding view of dynamic time warping (DTW) that contains the following three key features: (i) the local path routing decisions are contrastive and differentiable, (ii) pairwise distances are cast as probabilities that are contrastive as well, and (iii) our formulation naturally admits a global cycle consistency loss that verifies correspondences. For evaluation, we consider the tasks of fine-grained action classification, few shot learning, and video synchronization. We report significant performance increases over previous methods. In addition, we report two applications of our temporal alignment framework, namely 3D pose reconstruction and fine-grained audio/visual retrieval.

@InProceedings{Hadji_2021_CVPR,
    author    = {Hadji, Isma and Derpanis, Konstantinos G. and Jepson, Allan D.},
    title     = {Representation Learning via Global Temporal Alignment and Cycle-Consistency},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {11068-11077}
}


Zhizhong Huang, Junping Zhang, Hongming Shan

Abstract: To minimize the effects of age variation in face recognition, previous work either extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features, called age-invariant face recognition (AIFR), or removes age variation by transforming the faces of different age groups into the same age group, called face age synthesis (FAS); however, the former lacks visual results for model interpretation while the latter suffers from artifacts compromising downstream recognition. Therefore, this paper proposes a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn age-invariant identity-related representation while achieving pleasing face synthesis. Specifically, we first decompose the mixed face features into two uncorrelated components---identity- and age-related features---through an attention mechanism, and then decorrelate these two components using multi-task training and continuous domain adaption. In contrast to the conventional one-hot encoding that achieves group-level FAS, we propose a novel identity conditional module to achieve identity-level FAS, with a weight-sharing strategy to improve the age smoothness of synthesized faces. In addition, we collect and release a large cross-age face dataset with age and gender annotations to advance AIFR and FAS. Extensive experiments on five benchmark cross-age datasets demonstrate the superior performance of our proposed MTLFace over state-of-the-art methods for AIFR and FAS. We further validate MTLFace on two popular general face recognition datasets, showing competitive performance for face recognition in the wild. The source code and dataset are available at https://github.com/Hzzone/MTLFace.

@InProceedings{Huang_2021_CVPR,
    author    = {Huang, Zhizhong and Zhang, Junping and Shan, Hongming},
    title     = {When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {7282-7291}
}


Yiding Yang, Zhou Ren, Haoxiang Li, Chunluan Zhou, Xinchao Wang, Gang Hua

Abstract: Multi-person pose estimation and tracking serve as crucial steps for video understanding. Most state-of-the-art approaches rely on first estimating poses in each frame and only then implementing data association and refinement. Despite the promising results achieved, such a strategy is inevitably prone to missed detections especially in heavily-cluttered scenes, since this tracking-by-detection paradigm is, by nature, largely dependent on visual evidences that are absent in the case of occlusion. In this paper, we propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame, and hence may serve as a robust estimation even in challenging scenarios including occlusion. Specifically, we derive this prediction of dynamics through a graph neural network (GNN) that explicitly accounts for both spatial-temporal and visual information. It takes as input the historical pose tracklets and directly predicts the corresponding poses in the following frame for each tracklet. The predicted poses will then be aggregated with the detected poses, if any, at the same frame so as to produce the final pose, potentially recovering the occluded joints missed by the estimator. Experiments on PoseTrack 2017 and PoseTrack 2018 datasets demonstrate that the proposed method achieves results superior to the state of the art on both human pose estimation and tracking tasks.

@InProceedings{Yang_2021_CVPR,
    author    = {Yang, Yiding and Ren, Zhou and Li, Haoxiang and Zhou, Chunluan and Wang, Xinchao and Hua, Gang},
    title     = {Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8074-8084}
}


Yahui Liu, Enver Sangineto, Yajing Chen, Linchao Bao, Haoxian Zhang, Nicu Sebe, Bruno Lepri, Wei Wang, Marco De Nadai

Abstract: Image-to-Image (I2I) multi-domain translation models are usually evaluated also using the quality of their semantic interpolation results. However, state-of-the-art models frequently show abrupt changes in the image appearance during interpolation, and usually perform poorly in interpolations across domains. In this paper, we propose a new training protocol based on three specific losses which help a translation network to learn a smooth and disentangled latent style space in which: 1) Both intra- and inter-domain interpolations correspond to gradual changes in the generated images and 2) The content of the source image is better preserved during the translation. Moreover, we propose a novel evaluation metric to properly measure the smoothness of latent style space of I2I translation models. The proposed method can be plugged in existing translation approaches, and our extensive experiments on different datasets show that it can significantly boost the quality of the generated images and the graduality of the interpolations.

@InProceedings{Liu_2021_CVPR,
    author    = {Liu, Yahui and Sangineto, Enver and Chen, Yajing and Bao, Linchao and Zhang, Haoxian and Sebe, Nicu and Lepri, Bruno and Wang, Wei and De Nadai, Marco},
    title     = {Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10785-10794}
}


Xiaoding Yuan, Adam Kortylewski, Yihong Sun, Alan Yuille

Abstract: Analyzing complex scenes with Deep Neural Networks is a challenging task, particularly when images contain multiple objects that partially occlude each other. Existing approaches to image analysis mostly process objects independently and do not take into account the relative occlusion of nearby objects. In this paper, we propose a deep network for multi-object instance segmentation that is robust to occlusion and can be trained from bounding box supervision only. Our work builds on Compositional Networks, which learn a generative model of neural feature activations to locate occluders and to classify objects based on their non-occluded parts. We extend their generative model to include multiple objects and introduce a framework for efficient inference in challenging occlusion scenarios. In particular, we obtain feed-forward predictions of the object classes and their instance and occluder segmentations. We introduce an Occlusion Reasoning Module (ORM) that locates erroneous segmentations and estimates the occlusion order to correct them. The improved segmentation masks are, in turn, integrated into the network in a top-down manner to improve the image classification. Our experiments on the KITTI INStance dataset (KINS) and a synthetic occlusion dataset demonstrate the effectiveness and robustness of our model at multi-object instance segmentation under occlusion. Code is publically available at https://github.com/XD7479/Multi-Object-Occlusion.

@InProceedings{Yuan_2021_CVPR,
    author    = {Yuan, Xiaoding and Kortylewski, Adam and Sun, Yihong and Yuille, Alan},
    title     = {Robust Instance Segmentation Through Reasoning About Multi-Object Occlusion},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {11141-11150}
}


George Cazenavette, Calvin Murdock, Simon Lucey

Abstract: Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of this sensitivity is not well understood, theoretical analyses can be simplified by reframing each layer of a feed-forward network as an approximate solution to a sparse coding problem. Iterative solutions using basis pursuit are theoretically more stable and have improved adversarial robustness. However, cascading layer-wise pursuit implementations suffer from error accumulation in deeper networks. In contrast, our new method of deep pursuit approximates the activations of all layers as a single global optimization problem, allowing us to consider deeper, real-world architectures with skip connections such as residual networks. Experimentally, our approach demonstrates improved robustness to adversarial noise.

@InProceedings{Cazenavette_2021_CVPR,
    author    = {Cazenavette, George and Murdock, Calvin and Lucey, Simon},
    title     = {Architectural Adversarial Robustness: The Case for Deep Pursuit},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {7150-7158}
}


Scene Essence

Jiayan Qiu, Yiding Yang, Xinchao Wang, Dacheng Tao

Abstract: What scene elements, if any, are indispensable for recognizing a scene? We strive to answer this question through the lens of an end-to-end learning scheme. Our goal is to identify a collection of such pivotal elements, which we term as Scene Essence, to be those that would alter scene recognition if taken out from the scene. To this end, we devise a novel approach that learns to partition the scene objects into two groups, essential ones and minor ones, under the supervision that if only the essential ones are kept while the minor ones are erased in the input image, a scene recognizer would preserve its original prediction. Specifically, we introduce a learnable graph neural network (GNN) for labelling scene objects, based on which the minor ones are wiped off by an off-the-shelf image inpainter. The features of the inpainted image derived in this way, together with those learned from the GNN with the minor-object nodes pruned, are expected to fool the scene discriminator. Both subjective and objective evaluations on Places365, SUN397, and MIT67 datasets demonstrate that, the learned Scene Essence yields a visually plausible image that convincingly retains the original scene category.

@InProceedings{Qiu_2021_CVPR,
    author    = {Qiu, Jiayan and Yang, Yiding and Wang, Xinchao and Tao, Dacheng},
    title     = {Scene Essence},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8322-8333}
}


Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden

Abstract: Disentangled representations support a range of downstream tasks including causal reasoning, generative modeling, and fair machine learning. Unfortunately, disentanglement has been shown to be impossible without the incorporation of supervision or inductive bias. Given that supervision is often expensive or infeasible to acquire, we choose to incorporate structural inductive bias and present an unsupervised, deep State-Space-Model for Video Disentanglement (VDSM). The model disentangles latent time-varying and dynamic factors via the incorporation of hierarchical structure with a dynamic prior and a Mixture of Experts decoder. VDSM learns separate disentangled representations for the identity of the object or person in the video, and for the action being performed. We evaluate VDSM across a range of qualitative and quantitative tasks including identity and dynamics transfer, sequence generation, Frechet Inception Distance, and factor classification. VDSM achieves state-of-the-art performance and exceeds adversarial methods, even when the methods use additional supervision.

@InProceedings{Vowels_2021_CVPR,
    author    = {Vowels, Matthew J. and Camgoz, Necati Cihan and Bowden, Richard},
    title     = {VDSM: Unsupervised Video Disentanglement With State-Space Modeling and Deep Mixtures of Experts},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8176-8186}
}


Abstract: We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of a category and generating dense correspondences among shapes. With DIF, a 3D shape is represented by a template implicit field shared across the category, together with a 3D deformation field and a correction field dedicated for each shape instance. Shape correspondences can be easily established using their deformation fields. Our neural network, dubbed DIF-Net, jointly learns a shape latent space and these fields for 3D objects belonging to a category without using any correspondence or part label. The learned DIF-Net can also provides reliable correspondence uncertainty measurement reflecting shape structure discrepancy. Experiments show that DIF-Net not only produces high-fidelity 3D shapes but also builds high-quality dense correspondences across different shapes. We also demonstrate several applications such as texture transfer and shape editing, where our method achieves compelling results that cannot be achieved by previous methods.

@InProceedings{Deng_2021_CVPR,
    author    = {Deng, Yu and Yang, Jiaolong and Tong, Xin},
    title     = {Deformed Implicit Field: Modeling 3D Shapes With Learned Dense Correspondence},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10286-10296}
}


Wen Shen, Zhihua Wei, Shikun Huang, Binbin Zhang, Panyue Chen, Ping Zhao, Quanshi Zhang

Abstract: In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different network architectures. We propose a number of hypotheses on the effects of specific network architectures on the representation capacity of DNNs. In order to prove the hypotheses, we design five metrics to diagnose various types of DNNs from the following perspectives, information discarding, information concentration, rotation robustness, adversarial robustness, and neighborhood inconsistency. We conduct comparative studies based on such metrics to verify the hypotheses. We further use the verified hypotheses to revise architectures of existing DNNs and improve their utilities. Experiments demonstrate the effectiveness of our method. The code will be released when this paper is accepted.

@InProceedings{Shen_2021_CVPR,
    author    = {Shen, Wen and Wei, Zhihua and Huang, Shikun and Zhang, Binbin and Chen, Panyue and Zhao, Ping and Zhang, Quanshi},
    title     = {Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10703-10712}
}


Francis Williams, Matthew Trager, Joan Bruna, Denis Zorin

Abstract: We present Neural Splines, a technique for 3D surface reconstruction that is based on random feature kernels arising from infinitely-wide shallow ReLU networks. Our method achieves state-of-the-art results, outperforming recent neural network-based techniques and widely used Poisson Surface Reconstruction (which, as we demonstrate, can also be viewed as a type of kernel method). Because our approach is based on a simple kernel formulation, it is easy to analyze and can be accelerated by general techniques designed for kernel-based learning. We provide explicit analytical expressions for our kernel and argue that our formulation can be seen as a generalization of cubic spline interpolation to higher dimensions. In particular, the RKHS norm associated with Neural Splines biases toward smooth interpolants.

@InProceedings{Williams_2021_CVPR,
    author    = {Williams, Francis and Trager, Matthew and Bruna, Joan and Zorin, Denis},
    title     = {Neural Splines: Fitting 3D Surfaces With Infinitely-Wide Neural Networks},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {9949-9958}
}


Abstract: Many existing approaches for 3D point cloud semantic segmentation are fully supervised. These fully supervised approaches heavily rely on large amounts of labeled training data that are difficult to obtain and cannot segment new classes after training. To mitigate these limitations, we propose a novel attention-aware multi-prototype transductive few-shot point cloud semantic segmentation method to segment new classes given a few labeled examples. Specifically, each class is represented by multiple prototypes to model the complex data distribution of labeled points. Subsequently, we employ a transductive label propagation method to exploit the affinities between labeled multi-prototypes and unlabeled points, and among the unlabeled points. Furthermore, we design an attention-aware multi-level feature learning network to learn the discriminative features that capture the geometric dependencies and semantic correlations between points. Our proposed method shows significant and consistent improvements compared to baselines in different few-shot point cloud semantic segmentation settings (i.e., 2/3-way 1/5-shot) on two benchmark datasets. Our code is available at https://github.com/Na-Z/attMPTI.

@InProceedings{Zhao_2021_CVPR,
    author    = {Zhao, Na and Chua, Tat-Seng and Lee, Gim Hee},
    title     = {Few-Shot 3D Point Cloud Semantic Segmentation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8873-8882}
}


Chao Ren, Xiaohai He, Chuncheng Wang, Zhibo Zhao

Abstract: Recent studies have shown that deep networks can achieve promising results for image denoising. However, how to simultaneously incorporate the valuable achievements of traditional methods into the network design and improve network interpretability is still an open problem. To solve this problem, we propose a novel model-based denoising method to inform the design of our denoising network. First, by introducing a non-linear filtering operator, a reliability matrix, and a high-dimensional feature transformation function into the traditional consistency prior, we propose a novel adaptive consistency prior (ACP). Second, by incorporating the ACP term into the maximum a posteriori framework, a model-based denoising method is proposed. This method is further used to inform the network design, leading to a novel end-to-end trainable and interpretable deep denoising network, called DeamNet. Note that the unfolding process leads to a promising module called dual element-wise attention mechanism (DEAM) module. To the best of our knowledge, both our ACP constraint and DEAM module have not been reported in the previous literature. Extensive experiments verify the superiority of DeamNet on both synthetic and real noisy image datasets.

@InProceedings{Ren_2021_CVPR,
    author    = {Ren, Chao and He, Xiaohai and Wang, Chuncheng and Zhao, Zhibo},
    title     = {Adaptive Consistency Prior Based Deep Network for Image Denoising},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {8596-8606}
}


Kevin Chen, Junshen K. Chen, Jo Chuang, Marynel Vazquez, Silvio Savarese

Abstract: Conventional approaches to vision-and-language navigation (VLN) are trained end-to-end but struggle to perform well in freely traversable environments. Inspired by the robotics community, we propose a modular approach to VLN using topological maps. Given a natural language instruction and topological map, our approach leverages attention mechanisms to predict a navigation plan in the map. The plan is then executed with low-level actions (e.g. forward, rotate) using a robust controller. Experiments show that our method outperforms previous end-to-end approaches, generates interpretable navigation plans, and exhibits intelligent behaviors such as backtracking.

@InProceedings{Chen_2021_CVPR,
    author    = {Chen, Kevin and Chen, Junshen K. and Chuang, Jo and Vazquez, Marynel and Savarese, Silvio},
    title     = {Topological Planning With Transformers for Vision-and-Language Navigation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {11276-11286}
}