mtybilly/Instruct2DS huggingface dataset:
3DStyleNet: Creating 3D Shapes With Geometric and Texture Style Variations
Authors: Kangxue Yin, Jun Gao, Maria Shugrina, Sameh Khamis, Sanja Fidler
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We propose a method to create plausible geometric and texture style variations of 3D objects in the quest to democratize 3D content creation. Given a pair of textured source and target objects, our method predicts a part-aware affine transformation field that naturally warps the source shape to imitate the overall geometric style of the target. In addition, the texture style of the target is transferred to the warped source object with the help of a multi-view differentiable renderer. Our model, 3DStyleNet, is composed of two sub-networks trained in two stages. First, the geometric style network is trained on a large set of untextured 3D shapes. Second, we jointly optimize our geometric style network and a pre-trained image style transfer network with losses defined over both the geometry and the rendering of the result. Given a small set of high-quality textured objects, our method can create many novel stylized shapes, resulting in effortless 3D content creation and style-ware data augmentation. We showcase our approach qualitatively on 3D content stylization, and provide user studies to validate the quality of our results. In addition, our method can serve as a valuable tool to create 3D data augmentations for computer vision tasks. Extensive quantitative analysis shows that 3DStyleNet outperforms alternative data augmentation techniques for the downstream task of single-image 3D reconstruction.
NeRD: Neural Reflectance Decomposition From Image Collections
Authors: Mark Boss, Raphael Braun, Varun Jampani, Jonathan T. Barron, Ce Liu, Hendrik P.A. Lensch
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Decomposing a scene into its shape, reflectance, and illumination is a challenging but important problem in computer vision and graphics. This problem is inherently more challenging when the illumination is not a single light source under laboratory conditions but is instead an unconstrained environmental illumination. Though recent work has shown that implicit representations can be used to model the radiance field of an object, most of these techniques only enable view synthesis and not relighting. Additionally, evaluating these radiance fields is resource and time-intensive. We propose a neural reflectance decomposition (NeRD) technique that uses physically-based rendering to decompose the scene into spatially varying BRDF material properties. In contrast to existing techniques, our input images can be captured under different illumination conditions. In addition, we also propose techniques to convert the learned reflectance volume into a relightable textured mesh enabling fast real-time rendering with novel illuminations. We demonstrate the potential of the proposed approach with experiments on both synthetic and real datasets, where we are able to obtain high-quality relightable 3D assets from image collections. The datasets and code are available at the project page: https://markboss.me/publication/2021-nerd/
Deep Hybrid Self-Prior for Full 3D Mesh Generation
Authors: Xingkui Wei, Zhengqing Chen, Yanwei Fu, Zhaopeng Cui, Yinda Zhang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We present a deep learning pipeline that leverages network self-prior to recover a full 3D model consisting of both a triangular mesh and a texture map from the colored 3D point cloud. Different from previous methods either exploiting 2D self-prior for image editing or 3D self-prior for pure surface reconstruction, we propose to exploit a novel hybrid 2D-3D self-prior in deep neural networks to significantly improve the geometry quality and produce a high-resolution texture map, which is typically missing from the output of commodity-level 3D scanners. In particular, we first generate an initial mesh using a 3D convolutional neural network with 3D self-prior, and then encode both 3D information and color information in the 2D UV atlas, which is further refined by 2D convolutional neural networks with the self-prior. In this way, both 2D and 3D self-priors are utilized for the mesh and texture recovery. Experiments show that, without the need of any additional training data, our method recovers the 3D textured mesh model of high quality from sparse input, and outperforms the state-of-the-art methods in terms of both the geometry and texture quality.
MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction
Authors: Lingwei Dang, Yongwei Nie, Chengjiang Long, Qing Zhang, Guiqing Li
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Human motion prediction is a challenging task due to the stochasticity and aperiodicity of future poses. Recently, graph convolutional network has been proven to be very effective to learn dynamic relations among pose joints, which is helpful for pose prediction. On the other hand, one can abstract a human pose recursively to obtain a set of poses at multiple scales. With the increase of the abstraction level, the motion of the pose becomes more stable, which benefits pose prediction too. In this paper, we propose a novel Multi-Scale Residual Graph Convolution Network (MSR-GCN) for human pose prediction task in the manner of end-to-end. The GCNs are used to extract features from fine to coarse scale and then from coarse to fine scale. The extracted features at each scale are then combined and decoded to obtain the residuals between the input and target poses. Intermediate supervisions are imposed on all the predicted poses, which enforces the network to learn more representative features. Our proposed approach is evaluated on two standard benchmark datasets, i.e., the Human3.6M dataset and the CMU Mocap dataset. Experimental results demonstrate that our method outperforms the state-of-the-art approaches. Code and pre-trained models are available at https://github.com/Droliven/MSRGCN.
Super Resolve Dynamic Scene From Continuous Spike Streams
Authors: Jing Zhao, Jiyu Xie, Ruiqin Xiong, Jian Zhang, Zhaofei Yu, Tiejun Huang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Recently, a novel retina-inspired camera, namely spike camera, has shown great potential for recording high-speed dynamic scenes. Unlike the conventional digital cameras that compact the visual information within the exposure interval into a single snapshot, the spike camera continuously outputs binary spike streams to record the dynamic scenes, yielding a very high temporal resolution. Most of the existing reconstruction methods for spike camera focus on reconstructing images with the same resolution as spike camera. However, as a trade-off of high temporal resolution, the spatial resolution of spike camera is limited, resulting in inferior details of the reconstruction. To address this issue, we develop a spike camera super-resolution framework, aiming to super resolve high-resolution intensity images from the low-resolution binary spike streams. Due to the relative motion between the camera and the objects to capture, the spikes fired by the same sensor pixel no longer describes the same points in the external scene. In this paper, we properly exploit the relative motion and derive the relationship between light intensity and each spike, so as to recover the external scene with both high temporal and high spatial resolution. Experimental results demonstrate that the proposed method can reconstruct pleasant high-resolution images from low-resolution spike streams.
Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation
Authors: Beibei Lin, Shunli Zhang, Xin Yu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Gait recognition is one of the most important biometric technologies and has been applied in many fields. Recent gait recognition frameworks represent each gait frame by descriptors extracted from either global appearances or local regions of humans. However, the representations based on global information often neglect the details of the gait frame, while local region based descriptors cannot capture the relations among neighboring regions, thus reducing their discriminativeness. In this paper, we propose a novel feature extraction and fusion framework to achieve discriminative feature representations for gait recognition. Towards this goal, we take advantage of both global visual information and local region details and develop a Global and Local Feature Extractor (GLFE). Specifically, our GLFE module is composed of our newly designed multiple global and local convolutional layers (GLConv) to ensemble global and local features in a principle manner. Furthermore, we present a novel operation, namely Local Temporal Aggregation (LTA), to further preserve the spatial information by reducing the temporal resolution to obtain higher spatial resolution. With the help of our GLFE and LTA, our method significantly improves the discriminativeness of our visual features, thus improving the gait recognition performance. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art gait recognition methods on two popular datasets.
Labels4Free: Unsupervised Segmentation Using StyleGAN
Authors: Rameen Abdal, Peihao Zhu, Niloy J. Mitra, Peter Wonka
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We propose an unsupervised segmentation framework for StyleGAN generated objects. We build on two main observations. First, the features generated by StyleGAN hold valuable information that can be utilized towards training segmentation networks. Second, the foreground and background can often be treated to be largely independent and be swapped across images to produce plausible composited images. For our solution, we propose to augment the Style-GAN2 generator architecture with a segmentation branch and to split the generator into a foreground and background network. This enables us to generate soft segmentation masks for the foreground object in an unsupervised fashion. On multiple object classes, we report comparable results against state-of-the-art supervised segmentation networks, while against the best unsupervised segmentation approach we demonstrate a clear improvement, both in qualitative and quantitative metrics. Project Page : https:/rameenabdal.github.io/Labels4Free
Harnessing the Conditioning Sensorium for Improved Image Translation
Authors: Cooper Nederhood, Nicholas Kolkin, Deqing Fu, Jason Salavon
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Existing methods for multi-modal domain translation learn to embed the input images into a domain-invariant "content" space and a domain-specific "style" space from which novel images can be synthesized. Rather than learning to embed the RGB image from scratch we propose deriving our content representation from conditioning data produced by pretrained off-the-shelf networks. Motivated by the inherent ambiguity of "content", which has different meanings depending on the desired level of abstraction, this approach gives intuitive control over which aspects of content are preserved across domains. We evaluate our method on traditional, well-aligned, datasets such as CelebA-HQ, and propose two novel datasets for evaluation on more complex scenes: ClassicTV and FFHQ-WildCrops. Our approach, which we call Sensorium, enables higher quality domain translation for complex scenes than prior work.
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation
Authors: Maosheng Ye, Shuangjie Xu, Tongyi Cao, Qifeng Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We present a novel and flexible architecture for point cloud segmentation with dual-representation iterative learning. In point cloud processing, different representations have their own pros and cons. Thus, finding suitable ways to represent point cloud data structure while keeping its own internal physical property such as permutation and scale-invariant is a fundamental problem. Therefore, we propose our work, DRINet, which serves as the basic network structure for dual-representation learning with great flexibility at feature transferring and less computation cost, especially for large-scale point clouds. DRINet mainly consists of two modules called Sparse Point-Voxel Feature Extraction and Sparse Voxel-Point Feature Extraction. By utilizing these two modules iteratively, features can be propagated between two different representations. We further propose a novel multi-scale pooling layer for pointwise locality learning to improve context information propagation. Our network achieves state-of-the-art results for point cloud classification and segmentation tasks on several datasets while maintaining high runtime efficiency. For large-scale outdoor scenarios, our method outperforms state-of-the-art methods with a real-time inference speed of 62ms per frame.
Spectral Leakage and Rethinking the Kernel Size in CNNs
Authors: Nergis Tomen, Jan C. van Gemert
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Convolutional layers in CNNs implement linear filters which decompose the input into different frequency bands. However, most modern architectures neglect standard principles of filter design when optimizing their model choices regarding the size and shape of the convolutional kernel. In this work, we consider the well-known problem of spectral leakage caused by windowing artifacts in filtering operations in the context of CNNs. We show that the small size of CNN kernels make them susceptible to spectral leakage, which may induce performance-degrading artifacts. To address this issue, we propose the use of larger kernel sizes along with the Hamming window function to alleviate leakage in CNN architectures. We demonstrate improved classification accuracy on multiple benchmark datasets including Fashion-MNIST, CIFAR-10, CIFAR-100 and ImageNet with the simple use of a standard window function in convolutional layers. Finally, we show that CNNs employing the Hamming window display increased robustness against various adversarial attacks.
High-Fidelity Pluralistic Image Completion With Transformers
Authors: Ziyu Wan, Jingbo Zhang, Dongdong Chen, Jing Liao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Image completion has made tremendous progress with convolutional neural networks (CNNs), because of their powerful texture modeling capacity. However, due to some inherent properties (eg, local inductive prior, spatial-invariant kernels), CNNs do not perform well in understanding global structures or naturally support pluralistic completion. Recently, transformers demonstrate their power in modeling the long-term relationship and generating diverse results, but their computation complexity is quadratic to input length, thus hampering the application in processing high-resolution images. This paper brings the best of both worlds to pluralistic image completion: appearance prior reconstruction with transformer and texture replenishment with CNN. The former transformer recovers pluralistic coherent structures together with some coarse textures, while the latter CNN enhances the local texture details of coarse priors guided by the high-resolution masked images. The proposed method vastly outperforms state-of-the-art methods in terms of three aspects: 1) large performance boost on image fidelity even compared to deterministic completion methods; 2) better diversity and higher fidelity for pluralistic completion; 3) exceptional generalization ability on large masks and generic dataset, like ImageNet. Code and pre-trained models have been publicly released at https://github.com/raywzy/ICT.
Dance With Self-Attention: A New Look of Conditional Random Fields on Anomaly Detection in Videos
Authors: Didik Purwanto, Yie-Tarng Chen, Wen-Hsien Fang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: This paper proposes a novel weakly supervised approach for anomaly detection, which begins with a relation-aware feature extractor to capture the multi-scale convolutional neural network (CNN) features from a video. Afterwards, self-attention is integrated with conditional random fields (CRFs), the core of the network, to make use of the ability of self-attention in capturing the short-range correlations of the features and the ability of CRFs in learning the inter-dependencies of these features. Such a framework can learn not only the spatio-temporal interactions among the actors which are important for detecting complex movements, but also their short- and long-term dependencies across frames. Also, to deal with both local and non-local relationships of the features, a new variant of self-attention is developed by taking into consideration a set of cliques with different temporal localities. Moreover, a contrastive multi-instance learning scheme is considered to broaden the gap between the normal and abnormal instances, resulting in more accurate abnormal discrimination. Simulations reveal that the new method provides superior performance to the state-of-the-art works on the widespread UCF-Crime and ShanghaiTech datasets.
Text Is Text, No Matter What: Unifying Text Recognition Using Knowledge Distillation
Authors: Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Yi-Zhe Song
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Text recognition remains a fundamental and extensively researched topic in computer vision, largely owing to its wide array of commercial applications. The challenging nature of the very problem however dictated a fragmentation of research efforts: Scene Text Recognition (STR) that deals with text in everyday scenes, and Handwriting Text Recognition (HTR) that tackles hand-written text. In this paper, for the first time, we argue for their unification -- we aim for a single model that can compete favourably with two separate state-of-the-art STR and HTR models. We first show that cross-utilisation of STR and HTR models trigger significant performance drops due to differences in their inherent challenges. We then tackle their union by introducing a knowledge distillation (KD) based framework. This however is non-trivial, largely due to the variable-length and sequential nature of text sequences, which renders off-the-shelf KD techniques that mostly work with global fixed length data, inadequate. For that, we propose four distillation losses, all of which are specifically designed to cope with the aforementioned unique characteristics of text recognition. Empirical evidence suggests that our proposed unified model performs at par with individual models, even surpassing them in certain cases. Ablative studies demonstrate that naive baselines such as a two-stage framework, multi-task and domain adaption/generalisation alternatives do not work that well, further authenticating our design.
Unsupervised 3D Pose Estimation for Hierarchical Dance Video Recognition
Authors: Xiaodan Hu, Narendra Ahuja
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Dance experts often view dance as a hierarchy of information, spanning low-level (raw images, image sequences), mid-levels (human poses and bodypart movements), and high-level (dance genre). We propose a Hierarchical Dance Video Recognition framework (HDVR). HDVR estimates 2D pose sequences, tracks dancers, and then simultaneously estimates corresponding 3D poses and 3D-to-2D imaging parameters, without requiring ground truth for 3D poses. Unlike most methods that work on a single person, our tracking works on multiple dancers, under occlusions. From the estimated 3D pose sequence, HDVR extracts body part movements, and therefrom dance genre. The resulting hierarchical dance representation is explainable to experts. To overcome noise and interframe correspondence ambiguities, we enforce spatial and temporal motion smoothness and photometric continuity over time. We use an LSTM network to extract 3D movement subsequences from which we recognize dance genre. For experiments, we have identified 154 movement types, of 16 body parts, and assembled a new University of Illinois Dance (UID) Dataset, containing 1143 video clips of 9 genres covering 30 hours, annotated with movement and genre labels. Our experimental results demonstrate that our algorithms outperform the state-of-the-art 3D pose estimation methods, which also enhances our dance recognition performance.
What You Can Learn by Staring at a Blank Wall
Authors: Prafull Sharma, Miika Aittala, Yoav Y. Schechner, Antonio Torralba, Gregory W. Wornell, William T. Freeman, Frédo Durand
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We present a passive non-line-of-sight method that infers the number of people or activity of a person from the observation of a blank wall in an unknown room. Our technique analyzes complex imperceptible changes in indirect illumination in a video of the wall to reveal a signal that is correlated with motion in the hidden part of a scene. We use this signal to classify between zero, one, or two moving people, or the activity of a person in the hidden scene. We train two convolutional neural networks using data collected from 20 different scenes, and achieve an accuracy of approximately 94% for both tasks in unseen test environments and real-time online settings. Unlike other passive non-line-of-sight methods, the technique does not rely on known occluders or controllable light sources, and generalizes to unknown rooms with no recalibration. We analyze the generalization and robustness of our method with both real and synthetic data, and study the effect of the scene parameters on the signal quality.
Improving Contrastive Learning by Visualizing Feature Transformation
Authors: Rui Zhu, Bingchen Zhao, Jingen Liu, Zhenglong Sun, Chang Wen Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Contrastive learning, which aims at minimizing the distance between positive pairs while maximizing that of negative ones, has been widely and successfully applied in unsupervised feature learning, where the design of positive and negative (pos/neg) pairs is one of its keys. In this paper, we attempt to devise a feature-level data manipulation, differing from data augmentation, to enhance the generic contrastive self-supervised learning. To this end, we first design a visualization scheme for pos/neg score (pos/neg score indicates cosine similarity of pos/neg pair.) distribution, which enables us to analyze, interpret and understand the learning process. To our knowledge, this is the first attempt of its kind. More importantly, leveraging this tool, we gain some significant observations, which inspire our novel Feature Transformation proposals including the extrapolation of positives. This operation creates harder positives to boost the learning because hard positives enable the model to be more view-invariant. Besides, we propose the interpolation among negatives, which provides diversified negatives and makes the model more discriminative. It is the first attempt to deal with both challenges simultaneously. Experiment results show that our proposed Feature Transformation can improve at least 6.0% accuracy on ImageNet-100 over MoCo baseline, and about 2.0% accuracy on ImageNet-1K over the MoCoV2 baseline. Transferring to the downstream tasks successfully demonstrate our model is less task-bias. Visualization tools and codes: https://github.com/DTennant/CL-Visualizing-Feature-Transformation.
Complementary Patch for Weakly Supervised Semantic Segmentation
Authors: Fei Zhang, Chaochen Gu, Chenyue Zhang, Yuchao Dai
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels has been greatly advanced by exploiting the outputs of Class Activation Map (CAM) to generate the pseudo labels for semantic segmentation. However, CAM merely discovers seeds from a small number of regions, which may be insufficient to serve as pseudo masks for semantic segmentation. In this paper, we formulate the expansion of object regions in CAM as an increase in information. From the perspective of information theory, we propose a novel Complementary Patch (CP) Representation and prove that the information of the sum of the CAMs by a pair of input images with complementary hidden (patched) parts, namely CP Pair, is greater than or equal to the information of the baseline CAM. Therefore, a CAM with more information related to object seeds can be obtained by narrowing down the gap between the sum of CAMs generated by the CP Pair and the original CAM. We propose a CP Network (CPN) implemented by a triplet network and three regularization functions. To further improve the quality of the CAMs, we propose a Pixel-Region Correlation Module (PRCM) to augment the contextual information by using object-region relations between the feature maps and the CAMs. Experimental results on the PASCAL VOC 2012 datasets show that our proposed method achieves a new state-of-the-art in WSSS, validating the effectiveness of our CP Representation and CPN.
Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-Modal Pretraining
Authors: Xunlin Zhan, Yangxin Wu, Xiao Dong, Yunchao Wei, Minlong Lu, Yichi Zhang, Hang Xu, Xiaodan Liang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Nowadays, customer's demands for E-commerce are more diversified, which introduces more complications to the product retrieval industry. Previous methods are either subject to single-modal input or perform supervised image-level product retrieval, thus fail to accommodate real-life scenarios where enormous weakly annotated multi-modal data are present. In this paper, we investigate a more realistic setting that aims to perform weakly-supervised multi-modal instance-level product retrieval among fine-grained product categories. To promote the study of this challenging task, we contribute Product1M, one of the largest multi-modal cosmetic datasets for real-world instance-level retrieval. Notably, Product1M contains over 1 million image-caption pairs and consists of two sample types, i.e., single-product and multi-product samples, which encompass a wide variety of cosmetics brands. In addition to the great diversity, Product1M enjoys several appealing characteristics including fine-grained categories, complex combinations, and fuzzy correspondence that well mimic the real-world scenes. Moreover, we propose a novel model named Cross-modal contrAstive Product Transformer for instance-level prodUct REtrieval (CAPTURE), that excels in capturing the potential synergy between multi-modal inputs via a hybrid-stream transformer in a self-supervised manner. CAPTURE generates discriminative instance features via masked multi-modal learning as well as cross-modal contrastive pretraining and it outperforms several SOTA cross-modal baselines. Extensive ablation studies well demonstrate the effectiveness and the generalization capacity of our model.
StyleFormer: Real-Time Arbitrary Style Transfer via Parametric Style Composition
Authors: Xiaolei Wu, Zhihao Hu, Lu Sheng, Dong Xu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: In this work, we propose a new feed-forward arbitrary style transfer method, referred to as StyleFormer, which can simultaneously fulfill fine-grained style diversity and semantic content coherency. Specifically, our transformer-inspired feature-level stylization method consists of three modules: (a) the style bank generation module for sparse but compact parametric style pattern extraction, (b) the transformer-driven style composition module for content-guided global style composition, and (c) the parametric content modulation module for flexible but faithful stylization. The output stylized images are impressively coherent with the content structure, sensitive to the detailed style variations, but still holistically adhere to the style distributions from the style images. Qualitative and quantitative comparisons as well as comprehensive user studies demonstrate that our StyleFormer outperforms the existing SOTA methods in generating visually plausible stylization results with real-time efficiency.
Hypercorrelation Squeeze for Few-Shot Segmentation
Authors: Juhong Min, Dahyun Kang, Minsu Cho
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class. This challenging task requires to understand diverse levels of visual cues and analyze fine-grained correspondence relations between the query and the support images. To address the problem, we propose Hypercorrelation Squeeze Networks (HSNet) that leverages multi-level feature correlation and efficient 4D convolutions. It extracts diverse features from different levels of intermediate convolutional layers and constructs a collection of 4D correlation tensors, i.e., hypercorrelations. Using efficient center-pivot 4D convolutions in a pyramidal architecture, the method gradually squeezes high-level semantic and low-level geometric cues of the hypercorrelation into precise segmentation masks in coarse-to-fine manner. The significant performance improvements on standard few-shot segmentation benchmarks of PASCAL-5i, COCO-20i, and FSS-1000 verify the efficacy of the proposed method.
Digging Into Uncertainty in Self-Supervised Multi-View Stereo
Authors: Hongbin Xu, Zhipeng Zhou, Yali Wang, Wenxiong Kang, Baigui Sun, Hao Li, Yu Qiao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Self-supervised Multi-view stereo (MVS) with a pretext task of image reconstruction has achieved significant progress recently. However, previous methods are built upon intuitions, lacking comprehensive explanations about the effectiveness of the pretext task in self-supervised MVS. To this end, we propose to estimate epistemic uncertainty in self-supervised MVS, accounting for what the model ignores. Specially, the limitations can be resorted into two folds: ambiguious supervision in foreground and noisy disturbance in background. To address these issues, we propose a novel Uncertainty reduction Multi-view Stereo (U-MVS) framework for self-supervised learning. To alleviate ambiguous supervision in foreground, we involve extra correspondence prior with a flow-depth consistency loss. The dense 2D correspondence of optical flows is used to regularize the 3D stereo correspondence in MVS. To handle the noisy disturbance in background, we use Monte-Carlo Dropout to acquire the uncertainty map and further filter the unreliable supervision signals on invalid regions. Extensive experiments on DTU and Tank&Temples benchmark show that our U-MVS framework achieves the best performance among unsupervised MVS methods, with competitive performance with its supervised opponents.
A Style and Semantic Memory Mechanism for Domain Generalization
Authors: Yang Chen, Yu Wang, Yingwei Pan, Ting Yao, Xinmei Tian, Tao Mei
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Mainstream state-of-the-art domain generalization algorithms tend to prioritize the assumption on semantic invariance across domains. Meanwhile, the inherent intra-domain style invariance is usually underappreciated and put on the shelf. In this paper, we reveal that leveraging intra-domain style invariance is also of pivotal importance in improving the efficiency of domain generalization. We verify that it is critical for the network to be informative on what domain features are invariant and shared among instances, so that the network sharpens its understanding and improves its semantic discriminative ability. Correspondingly, we also propose a novel "jury" mechanism, which is particularly effective in learning useful semantic feature commonalities among domains. Our complete model called STEAM can be interpreted as a novel probabilistic graphical model, for which the implementation requires convenient constructions of two kinds of memory banks: semantic feature bank and style feature bank. Empirical results show that our proposed framework surpasses the state-of-the-art methods by clear margins.
EigenGAN: Layer-Wise Eigen-Learning for GANs
Authors: Zhenliang He, Meina Kan, Shiguang Shan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Recent studies on Generative Adversarial Network (GAN) reveal that different layers of a generative CNN hold different semantics of the synthesized images. However, few GAN models have explicit dimensions to control the semantic attributes represented in a specific layer. This paper proposes EigenGAN which is able to unsupervisedly mine interpretable and controllable dimensions from different generator layers. Specifically, EigenGAN embeds one linear subspace with orthogonal basis into each generator layer. Via generative adversarial training to learn a target distribution, these layer-wise subspaces automatically discover a set of "eigen-dimensions" at each layer corresponding to a set of semantic attributes or interpretable variations. By traversing the coefficient of a specific eigen-dimension, the generator can produce samples with continuous changes corresponding to a specific semantic attribute. Taking the human face for example, EigenGAN can discover controllable dimensions for high-level concepts such as pose and gender in the subspace of deep layers, as well as low-level concepts such as hue and color in the subspace of shallow layers. Moreover, in the linear case, we theoretically prove that our algorithm derives the principal components as PCA does. Codes can be found in https://github.com/LynnHo/EigenGAN-Tensorflow.
Uncertainty-Aware Human Mesh Recovery From Video by Learning Part-Based 3D Dynamics
Authors: Gun-Hee Lee, Seong-Whan Lee
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Despite the recent success of 3D human reconstruction methods, recovering the accurate and smooth 3D human motion from video is still challenging. Designing a temporal model in the encoding stage is not sufficient enough to settle the trade-off problem between the per-frame accuracy and the motion smoothness. To address this problem, we approach some of the fundamental problems of 3D reconstruction tasks, simultaneously predicting 3D pose and 3D motion dynamics. First, we utilize the power of uncertainty to address the problem of multiple 3D configurations resulting in the same 2D projections. Second, we confirmed that dividing the body into local regions shows outstanding results for estimating 3D motion dynamics. In this paper, we propose (i) an encoder that makes two different estimations: a static feature that presents 2D pose feature as distribution and a dynamic feature that includes optical flow information and (ii) a decoder that divides the body into five different local regions to estimate the 3D motion dynamics of each region. We demonstrate how our method recovers the accurate and smooth motion and achieves the state-of-the-art results for both constrained and in-the-wild videos.
Neural TMDlayer: Modeling Instantaneous Flow of Features via SDE Generators
Authors: Zihang Meng, Vikas Singh, Sathya N. Ravi
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We study how stochastic differential equation (SDE) based ideas can inspire new modifications to existing algorithms for a set of problems in computer vision. Loosely speaking, our formulation is related to both explicit and implicit strategies for data augmentation and group equivariance, but is derived from new results in the SDE literature on estimating infinitesimal generators of a class of stochastic processes. If and when there is nominal agreement between the needs of an application/task and the inherent properties and behavior of the types of processes that we can efficiently handle, we obtain a very simple and efficient plug-in layer that can be incorporated within any existing network architecture, with minimal modification and only a few additional parameters. We show promising experiments on a number of vision tasks including few shot learning, point cloud transformers and deep variational segmentation obtaining efficiency or performance improvements.
Rethinking Transformer-Based Set Prediction for Object Detection
Authors: Zhiqing Sun, Shengcao Cao, Yiming Yang, Kris M. Kitani
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: DETR is a recently proposed Transformer-based method which views object detection as a set prediction problem and achieves state-of-the-art performance but demands extra-long training time to converge. In this paper, we investigate the causes of the optimization difficulty in the training of DETR. Our examinations reveal several factors contributing to the slow convergence of DETR, primarily the issues with the Hungarian loss and the Transformer cross attention mechanism. To overcome these issues we propose two solutions, namely, TSP-FCOS (Transformer-based Set Prediction with FCOS) and TSP-RCNN (Transformer-based Set Prediction with RCNN). Experimental results show that the proposed methods not only converge much faster than the original DETR, but also significantly outperform DETR and other baselines in terms of detection accuracy.
FIERY: Future Instance Prediction in Bird's-Eye View From Surround Monocular Cameras
Authors: Anthony Hu, Zak Murez, Nikhil Mohan, Sofía Dudas, Jeffrey Hawke, Vijay Badrinarayanan, Roberto Cipolla, Alex Kendall
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Driving requires interacting with road agents and predicting their future behaviour in order to navigate safely. We present FIERY: a probabilistic future prediction model in bird's-eye view from monocular cameras. Our model predicts future instance segmentation and motion of dynamic agents that can be transformed into non-parametric future trajectories. Our approach combines the perception, sensor fusion and prediction components of a traditional autonomous driving stack by estimating bird's-eye-view prediction directly from surround RGB monocular camera inputs. FIERY learns to model the inherent stochastic nature of the future solely from camera driving data in an end-to-end manner, without relying on HD maps, and predicts multimodal future trajectories. We show that our model outperforms previous prediction baselines on the NuScenes and Lyft datasets. The code and trained models are available at https://github.com/wayveai/fiery.
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks
Authors: Liuwan Zhu, Rui Ning, Chunsheng Xin, Chonggang Wang, Hongyi Wu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: The data poisoning attack has raised serious security concerns on the safety of deep neural networks since it can lead to neural backdoor that misclassifies certain inputs crafted by an attacker. In particular, the sample-targeted backdoor attack is a new challenge. It targets at one or a few specific samples, called target samples, to misclassify them to a target class. Without a trigger planted in the backdoor model, the existing backdoor detection schemes fail to detect the sample-targeted backdoor as they depend on reverse-engineering the trigger or strong features of the trigger. In this paper, we propose a novel scheme to detect and mitigate sample-targeted backdoor attacks. We discover and demonstrate a unique property of the sample-targeted backdoor, which forces a boundary change such that small "pockets" are formed around the target sample. Based on this observation, we propose a novel defense mechanism to pinpoint a malicious pocket by "wrapping" them into a tight convex hull in the feature space. We design an effective algorithm to search for such a convex hull and remove the backdoor by fine-tuning the model using the identified malicious samples with the corrected label according to the convex hull. The experiments show that the proposed approach is highly efficient for detecting and mitigating a wide range of sample-targeted backdoor attacks.
Motion-Augmented Self-Training for Video Recognition at Smaller Scale
Authors: Kirill Gavrilyuk, Mihir Jain, Ilia Karmanov, Cees G. M. Snoek
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: The goal of this paper is to self-train a 3D convolutional neural network on an unlabeled video collection for deployment on small-scale video collections. As smaller video datasets benefit more from motion than appearance, we strive to train our network using optical flow, but avoid its computation during inference. We propose the first motion-augmented self-training regime, we call MotionFit. We start with supervised training of a motion model on a small, and labeled, video collection. With the motion model we generate pseudo-labels for a large unlabeled video collection, which enables us to transfer knowledge by learning to predict these pseudo-labels with an appearance model. Moreover, we introduce a multi-clip loss as a simple yet efficient way to improve the quality of the pseudo-labeling, even without additional auxiliary tasks. We also take into consideration the temporal granularity of videos during self-training of the appearance model, which was missed in previous works. As a result we obtain a strong motion-augmented representation model suited for video downstream tasks like action recognition and clip retrieval. On small-scale video datasets, MotionFit outperforms alternatives for knowledge transfer by 5%-8%, video-only self-supervision by 1%-7% and semisupervised learning by 9%-18% using the same amount of class labels.
Generating Smooth Pose Sequences for Diverse Human Motion Prediction
Authors: Wei Mao, Miaomiao Liu, Mathieu Salzmann
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Recent progress in stochastic motion prediction, i.e., predicting multiple possible future human motions given a single past pose sequence, has led to producing truly diverse future motions and even providing control over the motion of some body parts. However, to achieve this, the state-of-the-art method requires learning several mappings for diversity and a dedicated model for controllable motion prediction. In this paper, we introduce a unified deep generative network for both diverse and controllable motion prediction. To this end, we leverage the intuition that realistic human motions consist of smooth sequences of valid poses, and that, given limited data, learning a pose prior is much more tractable than a motion one. We therefore design a generator that predicts the motion of different body parts sequentially, and introduce a normalizing flow based pose prior, together with a joint angle loss, to achieve motion realism.Our experiments on two standard benchmark datasets, Human3.6M and HumanEva-I, demonstrate that our approach outperforms the state-of-the-art baselines in terms of both sample diversity and accuracy. The code is available at https://github.com/wei-mao-2019/gsps
DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension
Authors: Roman Shapovalov, David Novotny, Benjamin Graham, Patrick Labatut, Andrea Vedaldi
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We tackle the problem of monocular 3D reconstruction of articulated objects like humans and animals. Our key contribution is DensePose 3D, a novel parametric model of an articulated mesh, which can be learned in a self-supervised fashion from 2D image annotations only. This is in stark contrast with previous human body reconstruction methods that utilize a parametric model like SMPL pre-trained on a large dataset of 3D body scans that had to be obtained in a controlled environment. DensePose 3D can thus be applied for modelling broad range of articulated categories such as animal species. In an end-to-end fashion, it automatically learns to softly assign each vertex of a category-specific 3D template mesh to one of the rigidly moving latent parts and trains a single-view network predicting rigid motions of the parts to deform the template so that it re-projects correctly to the dense 2D surface annotations of objects (such as DensePose). In order to prevent unrealistic template deformations, we further propose to align the motions of nearby mesh vertices by expressing the part assignment as a function of the smooth eigenfunctions of the Laplace--Beltrami operator computed on the template mesh. Our experiments demonstrate improvements over the state-of-the-art non-rigid structure-from-motion baselines on both synthetic and real data on categories of humans and animals.
Unpaired Learning for Deep Image Deraining With Rain Direction Regularizer
Authors: Yang Liu, Ziyu Yue, Jinshan Pan, Zhixun Su
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We present a simple yet effective unpaired learning based image rain removal method from an unpaired set of synthetic images and real rainy images by exploring the properties of rain maps. The proposed algorithm mainly consists of a semi-supervised learning part and a knowledge distillation part. The semi-supervised part estimates the rain map and reconstructs the derained image based on the well-established layer separation principle. To facilitate rain removal, we develop a rain direction regularizer to constrain the rain estimation network in the semi-supervised learning part. With the estimated rain maps from the semi-supervised learning part, we first synthesize a new paired set by adding to rain-free images based on the superimposition model. The real rainy images and the derained results constitute another paired set. Then we develop an effective knowledge distillation method to explore such two paired sets so that the deraining model in the semi-supervised learning part is distilled. We propose two new rainy datasets, named RainDirection and Real3000, to validate the effectiveness of the proposed method. Both quantitative and qualitative experimental results demonstrate that the proposed method achieves favorable results against state-of-the-art methods in benchmark datasets and real-world images.