mtybilly/Instruct2DS huggingface dataset:
Visualizing Adapted Knowledge in Domain Transfer
Authors: Liang Zheng Yunzhong Hou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: A source model trained on source data and a target model learned through unsupervised domain adaptation (UDA) usually encode different knowledge. To understand the adaptation process, we portray their knowledge difference with image translation. Specifically, we feed a translated image and its original version to the two models respectively, formulating two branches. Through updating the translated image, we force similar outputs from the two branches. When such requirements are met, differences between the two images can compensate for and hence represent the knowledge difference between models. To enforce similar outputs from the two branches and depict the adapted knowledge, we propose a source-free image translation method that generates source-style images using only target images and the two models. We visualize the adapted knowledge on several datasets with different UDA methods and find that generated images successfully capture the style difference between the two domains. For application, we show that generated images enable further tuning of the target model without accessing source data. Code available at https://github.com/hou-yz/DA_visualization.
Delving into Data: Effectively Substitute Training for Black-box Attack
Authors: Bangjie Yin, Taiping Yao, Li Zhang, Yanwei Fu, Shouhong Ding, Jilin Li, Feiyue Huang, Xiangyang Xue Wenxuan Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Deep models have shown their vulnerability when processing adversarial samples. As for the black-box attack, without access to the architecture and weights of the attacked model, training a substitute model for adversarial attacks has attracted wide attention. Previous substitute training approaches focus on stealing the knowledge of the target model based on real training data or synthetic data, without exploring what kind of data can further improve the transferability between the substitute and target models. In this paper, we propose a novel perspective substitute training that focuses on designing the distribution of data used in the knowledge stealing process. More specifically, a diverse data generation module is proposed to synthesize large-scale data with wide distribution. And adversarial substitute training strategy is introduced to focus on the data distributed near the decision boundary. The combination of these two modules can further boost the consistency of the substitute model and target model, which greatly improves the effectiveness of adversarial attack. Extensive experiments demonstrate the efficacy of our method against state-of-the-art competitors under non-target and target attack settings. Detailed visualization and analysis are also provided to help understand the advantage of our method.
How To Exploit the Transferability of Learned Image Compression to Conventional Codecs
Authors: Keng-Chi Liu, Liang-Gee Chen, Shao-Yi Chien Jan P. Klopp
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Lossy image compression is often limited by the simplicity of the chosen loss measure. Recent research suggests that generative adversarial networks have the ability to overcome this limitation and serve as a multi-modal loss, especially for textures. Together with learned image compression, these two techniques can be used to great effect when relaxing the commonly employed tight measures of distortion. However, convolutional neural network-based algorithms have a large computational footprint. Ideally, an existing conventional codec should stay in place, ensuring faster adoption and adherence to a balanced computational envelope. As a possible avenue to this goal, we propose and investigate how learned image coding can be used as a surrogate to optimise an image for encoding. A learned filter alters the image to optimise a different performance measure or a particular task. Extending this idea with a generative adversarial network, we show how entire textures are replaced by ones that are less costly to encode but preserve a sense of detail. Our approach can remodel a conventional codec to adjust for the MS-SSIM distortion with over 20% rate improvement without any decoding overhead. On task-aware image compression, we perform favourably against a similar but codec-specific approach.
CorrNet3D: Unsupervised End-to-End Learning of Dense Correspondence for 3D Point Clouds
Authors: Yue Qian, Zhiyu Zhu, Junhui Hou, Hui Yuan, Ying He Yiming Zeng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Motivated by the intuition that one can transform two aligned point clouds to each other more easily and meaningfully than a misaligned pair, we propose CorrNet3D -the first unsupervised and end-to-end deep learning-based framework - to drive the learning of dense correspondence between 3D shapes by means of deformation-like reconstruction to overcome the need for annotated data. Specifically, CorrNet3D consists of a deep feature embedding module and two novel modules called correspondence indicator and symmetric deformer. Feeding a pair of raw point clouds, our model first learns the pointwise features and passes them into the indicator to generate a learnable correspondence matrix used to permute the input pair. The symmetric deformer, with an additional regularized loss, transforms the two permuted point clouds to each other to drive the unsupervised learning of the correspondence. The extensive experiments on both synthetic and real-world datasets of rigid and non-rigid 3D shapes show our CorrNet3D outperforms state-of-the-art methods to a large extent, including those taking meshes as input. CorrNet3D is a flexible framework in that it can be easily adapted to supervised learning if annotated data are available. The source code and pre-trained model will be available at https://github.com/ZENGYIMINGEAMON/CorrNet3D.git.
Single-View Robot Pose and Joint Angle Estimation via Render & Compare
Authors: Justin Carpentier, Mathieu Aubry, Josef Sivic Yann Labbe
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We introduce RoboPose, a method to estimate the joint angles and the 6D camera-to-robot pose of a known articulated robot from a single RGB image. This is an important problem to grant mobile and itinerant autonomous systems the ability to interact with other robots using only visual information in non-instrumented environments, especially in the context of collaborative robotics. It is also challenging because robots have many degrees of freedom and an infinite space of possible configurations that often result in self-occlusions and depth ambiguities when imaged by a single camera. The contributions of this work are three-fold. First, we introduce a new render & compare approach for estimating the 6D pose and joint angles of an articulated robot that can be trained from synthetic data, generalizes to new unseen robot configurations at test time, and can be applied to a variety of robots. Second, we experimentally demonstrate the importance of the robot parametrization for the iterative pose updates and design a parametrization strategy that is independent of the robot structure. Finally, we show experimental results on existing benchmark datasets for four different robots and demonstrate that our method significantly outperforms the state of the art. Code and pre-trained models are available on the project webpage.
Harmonious Semantic Line Detection via Maximal Weight Clique Selection
Authors: Wonhui Park, Seong-Gyun Jeong, Chang-Su Kim Dongkwon Jin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: A novel algorithm to detect an optimal set of semantic lines is proposed in this work. We develop two networks: selection network (S-Net) and harmonization network (H-Net). First, S-Net computes the probabilities and offsets of line candidates. Second, we filter out irrelevant lines through a selection-and-removal process. Third, we construct a complete graph, whose edge weights are computed by H-Net. Finally, we determine a maximal weight clique representing an optimal set of semantic lines. Moreover, to assess the overall harmony of detected lines, we propose a novel metric, called HIoU. Experimental results demonstrate that the proposed algorithm can detect harmonious semantic lines effectively and efficiently. Our codes are available at https://github.com/dongkwonjin/Semantic-Line-MWCS.
Learning the Non-Differentiable Optimization for Blind Super-Resolution
Authors: Jie Li, Xiumei Wang, Xinbo Gao Zheng Hui
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Previous convolutional neural network (CNN) based blind super-resolution (SR) methods usually adopt an iterative optimization way to approximate the ground-truth (GT) step-by-step. This solution always involves more computational costs to bring about time-consuming inference. At present, most blind SR algorithms are dedicated to obtaining high-fidelity results; their loss function generally employs L1 loss. To further improve the visual quality of SR results, perceptual metric, such as NIQE, is necessary to guide the network optimization. However, due to the non-differentiable property of NIQE, it cannot be as the loss function. Towards these issues, we propose an adaptive modulation network (AMNet) for multiple degradations SR, which is composed of the pivotal adaptive modulation layer (AMLayer). It is an efficient yet lightweight fusion layer between blur kernel and image features. Equipped with the blur kernel predictor, we naturally upgrade the AMNet to the blind SR model. Instead of considering iterative strategy, we make the blur kernel predictor trainable in the whole blind SR model, in which AMNet is well-trained. Also, we fit deep reinforcement learning into the blind SR model (AMNet-RL) to tackle the non-differentiable optimization problem. Specifically, the blur kernel predictor will be the actor to estimate the blur kernel from the input low-resolution (LR) image. The reward is designed by the pre-defined differentiable or non-differentiable metric. Extensive experiments show that our model can outperform state-of-the-art methods in both fidelity and perceptual metrics.
Progressive Temporal Feature Alignment Network for Video Inpainting
Authors: Linjie Yang, Ding Liu, Yong Jae Lee Xueyan Zou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Video inpainting aims to fill spatio-temporal "corrupted" regions with plausible content. To achieve this goal, it is necessary to find correspondences from neighbouring frames to faithfully hallucinate the unknown content. Current methods achieve this goal through attention, flow-based warping, or 3D temporal convolution. However, flow-based warping can create artifacts when optical flow is not accurate, while temporal convolution may suffer from spatial misalignment. We propose `Progressive Temporal Feature Alignment Network', which progressively enriches features extracted from the current frame with the feature warped from neighbouring frames using optical flow. Our approach corrects the spatial misalignment in the temporal feature propagation stage, greatly improving visual quality and temporal consistency of the inpainted videos. Using the proposed architecture, we achieve state-of-the-art performance on the DAVIS and FVI datasets compared to existing deep learning approaches. Code is available at https://github.com/MaureenZOU/TSAM.
Bottleneck Transformers for Visual Recognition
Authors: Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani Aravind Srinivas
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. By just replacing the spatial convolutions with global self-attention in the final three bottleneck blocks of a ResNet and no other changes, our approach improves upon the baselines significantly on instance segmentation and object detection while also reducing the parameters, with minimal overhead in latency. Through the design of BoTNet, we also point out how ResNet bottleneck blocks with self-attention can be viewed as Transformer blocks. Without any bells and whistles, BoTNet achieves 44.4% Mask AP and 49.7% Box AP on the COCO Instance Segmentation benchmark using the Mask R-CNN framework; surpassing the previous best published single model and single scale results of ResNeSt evaluated on the COCO validation set. Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84.7% top-1 accuracy on the ImageNet benchmark while being up to 1.64x faster in compute time than the popular EfficientNet models on TPU-v3 hardware. We hope our simple and effective approach will serve as a strong baseline for future research in self-attention models for vision.
Calibrated RGB-D Salient Object Detection
Authors: Jingjing Li, Shuang Yu, Miao Zhang, Yongri Piao, Shunyu Yao, Qi Bi, Kai Ma, Yefeng Zheng, Huchuan Lu, Li Cheng Wei Ji
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD). This naturally leads to the incorporation of depth information in addition to the conventional RGB image as input, known as RGB-D SOD or depth-aware SOD. Meanwhile, this emerging line of research has been considerably hindered by the noise and ambiguity that prevail in raw depth images. To address the aforementioned issues, we propose a Depth Calibration and Fusion (DCF) framework that contains two novel components: 1) a learning strategy to calibrate the latent bias in the original depth maps towards boosting the SOD performance; 2) a simple yet effective cross reference module to fuse features from both RGB and depth modalities. Extensive empirical experiments demonstrate that the proposed approach achieves superior performance against 27 state-of-the-art methods. Moreover, the proposed depth calibration strategy as a preprocessing step, can be further applied to existing cutting-edge RGB-D SOD models and noticeable improvements are achieved.
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
Authors: Shenlong Wang, Sivabalan Manivasagam, Zeng Huang, Wei-Chiu Ma, Xinchen Yan, Ersin Yumer, Raquel Urtasun Ze Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation. As there are exponentially many variations of humans with different shape, pose and clothing, it is critical to develop methods that can automatically reconstruct and animate humans at scale from real world data. Towards this goal, we represent the pedestrian's shape, pose and skinning weights as neural implicit functions that are directly learned from data. This representation enables us to handle a wide variety of different pedestrian shapes and poses without explicitly fitting a human parametric body model, allowing us to handle a wider range of human geometries and topologies. We demonstrate the effectiveness of our approach on various datasets and show that our reconstructions outperform existing state-of-the-art methods. Furthermore, our re-animation experiments show that we can generate 3D human animations at scale from a single RGB image (and/or an optional LiDAR sweep) as input.
OSTeC: One-Shot Texture Completion
Authors: Jiankang Deng, Stefanos Zafeiriou Baris Gecer
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: The last few years have witnessed the great success of non-linear generative models in synthesizing high-quality photorealistic face images. Many recent 3D facial texture reconstruction and pose manipulation from a single image approaches still rely on large and clean face datasets to train image-to-image Generative Adversarial Networks (GANs). Yet the collection of such a large scale high-resolution 3D texture dataset is still very costly and difficult to maintain age/ethnicity balance. Moreover, regression-based approaches suffer from generalization to the in-the-wild conditions and are unable to fine-tune to a target-image. In this work, we propose an unsupervised approach for one-shot 3D facial texture completion that does not require large-scale texture datasets, but rather harnesses the knowledge stored in 2D face generators. The proposed approach rotates an input image in 3D and fill-in the unseen regions by reconstructing the rotated image in a 2D face generator, based on the visible parts. Finally, we stitch the most visible textures at different angles in the UV image-plane. Further, we frontalize the target image by projecting the completed texture into the generator. The qualitative and quantitative experiments demonstrate that the completed UV textures and frontalized images are of high quality, resembles the original identity, can be used to train a texture GAN model for 3DMM fitting and improve pose-invariant face recognition.
Learning To Count Everything
Authors: Udbhav Sharma, Thu Nguyen, Minh Hoai Viresh Ranjan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. To this end, we pose counting as a few-shot regression task. To tackle this task, we present a novel method that takes a query image together with a few exemplar objects from the query image and predicts a density map for the presence of all objects of interest in the query image. We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category. We also introduce a dataset of 147 object categories containing over 6000 images that are suitable for the few-shot counting task. The images are annotated with two types of annotation, dots and bounding boxes, and they can be used for developing few-shot counting models. Experiments on this dataset shows that our method outperforms several state-of-the-art object detectors and few-shot counting approaches. Our code and dataset can be found at https://github.com/cvlab-stonybrook/LearningToCountEverything.
Robust Representation Learning With Feedback for Single Image Deraining
Authors: Hao Li Chenghao Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: A deraining network can be interpreted as a conditional generator that aims at removing rain streaks from image. Most existing image deraining methods ignore model errors caused by uncertainty that reduces embedding quality. Unlike existing image deraining methods that embed low-quality features into the model directly, we replace low-quality features by latent high-quality features. The spirit of closed-loop feedback in the automatic control field is borrowed to obtain latent high-quality features. A new method for error detection and feature compensation is proposed to address model errors. Extensive experiments on benchmark datasets as well as specific real datasets demonstrate that the proposed method outperforms recent state-of-the-art methods. Code is available at: https://github.com/LI-Hao-SJTU/DerainRLNet
Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction
Authors: Luan Tran, Xiaoming Liu Feng Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Inferring 3D structure of a generic object from a 2D image is a long-standing objective of computer vision. Conventional approaches either learn completely from CAD-generated synthetic data, which have difficulty in inference from real images, or generate 2.5D depth image via intrinsic decomposition, which is limited compared to the full 3D reconstruction. One fundamental challenge lies in how to leverage numerous real 2D images without any 3D ground truth. To address this issue, we take an alternative approach with semi-supervised learning. That is, for a 2D image of a generic object, we decompose it into latent representations of category, shape and albedo, lighting and camera projection matrix, decode the representations to segmented 3D shape and albedo respectively, and fuse these components to render an image well approximating the input image. Using a category-adaptive 3D joint occupancy field (JOF), we show that the complete shape and albedo modeling enables us to leverage real 2D images in both modeling and model fitting. The effectiveness of our approach is demonstrated through superior 3D reconstruction from a single image, being either synthetic or real, and shape segmentation.
SSN: Soft Shadow Network for Image Compositing
Authors: Jianming Zhang, Bedrich Benes Yichen Sheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We introduce an interactive Soft Shadow Network (SSN) to generates controllable soft shadows for image compositing. SSN takes a 2D object mask as input and thus is agnostic to image types such as painting and vector art. An environment light map is used to control the shadow's characteristics, such as angle and softness. SSN employs an Ambient Occlusion Prediction module to predict an intermediate ambient occlusion map, which can be further refined by the user to provides geometric cues to modulate the shadow generation. To train our model, we design an efficient pipeline to produce diverse soft shadow training data using 3D object models. In addition, we propose an inverse shadow map representation to improve model training. We demonstrate that our model produces realistic soft shadows in real-time. Our user studies show that the generated shadows are often indistinguishable from shadows calculated by a physics-based renderer and users can easily use SSN through an interactive application to generate specific shadow effects in minutes.
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
Authors: Fa-Ting Hong, Wei-Shi Zheng Jia-Chang Feng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations. Most existing works are limited in insufficient video representations. In this work, we develop a multiple instance self-training framework (MIST) to efficiently refine task-specific discriminative representations with only video-level annotations. In particular, MIST is composed of 1) a multiple instance pseudo label generator, which adapts a sparse continuous sampling strategy to produce more reliable clip-level pseudo labels, and 2) a self-guided attention boosted feature encoder that aims to automatically focus on anomalous regions in frames while extracting task-specific representations. Moreover, we adopt a self-training scheme to optimize both components and finally obtain a task-specific feature encoder. Extensive experiments on two public datasets demonstrate the efficacy of our method, and our method performs comparably or even better with existing supervised and weakly supervised methods, specifically obtaining a frame-level AUC 94.83% on ShanghaiTech.
VinVL: Revisiting Visual Representations in Vision-Language Models
Authors: Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, Jianfeng Gao Pengchuan Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: This paper presents a detailed study of improving vision features and develops an improved object detection model for vision language (VL) tasks. Compared to the most widely used bottom-up and top-down model [2], the new model is bigger, pre-trained on much larger training corpora that combine multiple public annotated object detection datasets, and thus can generate representations of a richer collection of visual objects and concepts. While previous VL research focuses solely on improving the vision-language fusion model and leaves the object detection model improvement untouched, we present an empirical study to show that vision features matter significantly in VL models. In our experiments we feed the vision features generated by the new object detection model into a pre-trained transformer-based VL fusion model Oscar+, and fine-tune Oscar+ on a wide range of downstream VL tasks. Our results show that the new vision features significantly improve the performance across all VL tasks, creating new state-of-the-art results on seven public benchmarks. We will release the new object detection model to public.
Bottom-Up Human Pose Estimation via Disentangled Keypoint Regression
Authors: Ke Sun, Bin Xiao, Zhaoxiang Zhang, Jingdong Wang Zigang Geng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In this paper, we are interested in the bottom-up paradigm of estimating human poses from an image. We study the dense keypoint regression framework that is previously inferior to the keypoint detection and grouping framework. Our motivation is that regressing keypoint positions accurately needs to learn representations that focus on the keypoint regions. We present a simple yet effective approach, named disentangled keypoint regression (DEKR). We adopt adaptive convolutions through pixel-wise spatial transformer to activate the pixels in the keypoint regions and accordingly learn representations from them. We use a multi-branch structure for separate regression: each branch learns a representation with dedicated adaptive convolutions and regresses one keypoint. The resulting disentangled representations are able to attend to the keypoint regions, respectively, and thus the keypoint regression is spatially more accurate. We empirically show that the proposed direct regression method outperforms keypoint detection and grouping methods and achieves superior bottom-up pose estimation results on two benchmark datasets, COCO and CrowdPose. The code and models are available at https://github.com/HRNet/DEKR.
CoMoGAN: Continuous Model-Guided Image-to-Image Translation
Authors: Pietro Cerri, Raoul de Charette Fabio Pizzati
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target data on a functional manifold. To that matter, we introduce a new Functional Instance Normalization layer and residual mechanism, which together disentangle image content from position on target manifold. We rely on naive physics-inspired models to guide the training while allowing private model/translations features. CoMoGAN can be used with any GAN backbone and allows new types of image translation, such as cyclic image translation like timelapse generation, or detached linear translation. On all datasets, it outperforms the literature. Our code is available in this page: https://github.com/cv-rits/CoMoGAN.
Self-Supervised Video Hashing via Bidirectional Transformers
Authors: Xiu Li, Jiwen Lu, Jie Zhou Shuyan Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Most existing unsupervised video hashing methods are built on unidirectional models with less reliable training objectives, which underuse the correlations among frames and the similarity structure between videos. To enable efficient scalable video retrieval, we propose a self-supervised video Hashing method based on Bidirectional Transformers (BTH). Based on the encoder-decoder structure of transformers, we design a visual cloze task to fully exploit the bidirectional correlations between frames. To unveil the similarity structure between unlabeled video data, we further develop a similarity reconstruction task by establishing reliable and effective similarity connections in the video space. Furthermore, we develop a cluster assignment task to exploit the structural statistics of the whole dataset such that more discriminative binary codes can be learned. Extensive experiments implemented on three public benchmark datasets, FCVID, ActivityNet and YFCC, demonstrate the superiority of our proposed approach.
From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation
Authors: Gim Hee Lee Chen Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Animal pose estimation is an important field that has received increasing attention in the recent years. The main challenge for this task is the lack of labeled data. Existing works circumvent this problem with pseudo labels generated from data of other easily accessible domains such as synthetic data. However, these pseudo labels are noisy even with consistency check or confidence-based filtering due to the domain shift in the data. To solve this problem, we design a multi-scale domain adaptation module (MDAM) to reduce the domain gap between the synthetic and real data. We further introduce an online coarse-to-fine pseudo label updating strategy. Specifically, we propose a self-distillation module in an inner coarse-update loop and a mean-teacher in an outer fine-update loop to generate new pseudo labels that gradually replace the old ones. Consequently, our model is able to learn from the old pseudo labels at the early stage, and gradually switch to the new pseudo labels to prevent overfitting in the later stage. We evaluate our approach on the TigDog and VisDA 2019 datasets, where we outperform existing approaches by a large margin. We also demonstrate the generalization ability of our model by testing extensively on both unseen domains and unseen animal categories. Our code is available at the project website.
Safe Local Motion Planning With Self-Supervised Freespace Forecasting
Authors: Aaron Huang, John Dolan, David Held, Deva Ramanan Peiyun Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Safe local motion planning for autonomous driving in dynamic environments requires forecasting how the scene evolves. Practical autonomy stacks adopt a semantic object-centric representation of a dynamic scene and build object detection, tracking, and prediction modules to solve forecasting. However, training these modules comes at an enormous human cost of manually annotated objects across frames. In this work, we explore future freespace as an alternative representation to support motion planning. Our key intuition is that it is important to avoid straying into occupied space regardless of what is occupying it. Importantly, computing ground-truth future freespace is annotation-free. First, we explore freespace forecasting as a self-supervised learning task. We then demonstrate how to use forecasted freespace to identify collision-prone plans from off-the-shelf motion planners. Finally, we propose future freespace as an additional source of annotation-free supervision. We demonstrate how to integrate such supervision into the learning-based planners. Experimental results on nuScenes and CARLA suggest both approaches lead to a significant reduction in collision rates.
Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration
Authors: Yufeng Liu, Chongyang Ma, Jianlong Chang, Huayan Wang, Tian Chen, Xiaoyan Guo, Pengfei Wan, Wen Zheng Xingyu Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Recent years have witnessed significant progress in 3D hand mesh recovery. Nevertheless, because of the intrinsic 2D-to-3D ambiguity, recovering camera-space 3D information from a single RGB image remains challenging. To tackle this problem, we divide camera-space mesh recovery into two sub-tasks, i.e., root-relative mesh recovery and root recovery. First, joint landmarks and silhouette are extracted from a single input image to provide 2D cues for the 3D tasks. In the root-relative mesh recovery task, we exploit semantic relations among joints to generate a 3D mesh from the extracted 2D cues. Such generated 3D mesh coordinates are expressed relative to a root position, i.e., wrist of the hand. In the root recovery task, the root position is registered to the camera space by aligning the generated 3D mesh back to 2D cues, thereby completing camera-space 3D mesh recovery. Our pipeline is novel in that (1) it explicitly makes use of known semantic relations among joints and (2) it exploits 1D projections of the silhouette and mesh to achieve robust registration. Extensive experiments on popular datasets such as FreiHAND, RHD, and Human3.6M demonstrate that our approach achieves state-of-the-art performance on both root-relative mesh recovery and root recovery. Our code is publicly available at https://github.com/SeanChenxy/HandMesh.
CondenseNet V2: Sparse Feature Reactivation for Deep Networks
Authors: Haojun Jiang, Ruojin Cai, Yulin Wang, Shiji Song, Gao Huang, Qi Tian Le Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency. The recent proposed CondenseNet has shown that this mechanism can be further improved if redundant features are removed. In this paper, we propose an alternative approach named sparse feature reactivation (SFR), aiming at actively increasing the utility of features for reusing. In the proposed network, named CondenseNetV2, each layer can simultaneously learn to 1) selectively reuse a set of most important features from preceding layers; and 2) actively update a set of preceding features to increase their utility for later layers. Our experiments show that the proposed models achieve promising performance on image classification (ImageNet and CIFAR) and object detection (MS COCO) in terms of both theoretical efficiency and practical speed.
Learning Graphs for Knowledge Transfer With Limited Labels
Authors: Nirat Saini, Larry S. Davis, Abhinav Shrivastava Pallabi Ghosh
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Fixed input graphs are a mainstay in approaches that utilize Graph Convolution Networks (GCNs) for knowledge transfer. The standard paradigm is to utilize relationships in the input graph to transfer information using GCNs from training to testing nodes in the graph; for example, the semi-supervised, zero-shot, and few-shot learning setups. We propose a generalized framework for learning and improving the input graph as part of the standard GCN-based learning setup. Moreover, we use additional constraints between similar and dissimilar neighbors for each node in the graph by applying triplet loss on the intermediate layer output. We present results of semi-supervised learning on Citeseer, Cora, and Pubmed benchmarking datasets, and zero/few-shot action recognition on UCF101 and HMDB51 datasets, significantly outperforming current approaches. We also present qualitative results visualizing the graph connections that our approach learns to update.
DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation
Authors: Sunghyun Cho, Sunghoon Im Seunghun Lee
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In this paper, we present DRANet, a network architecture that disentangles image representations and transfers the visual attributes in a latent space for unsupervised cross-domain adaptation. Unlike the existing domain adaptation methods that learn associated features sharing a domain, DRANet preserves the distinctiveness of each domain's characteristics. Our model encodes individual representations of content (scene structure) and style (artistic appearance) from both source and target images. Then, it adapts the domain by incorporating the transferred style factor into the content factor along with learnable weights specified for each domain. This learning framework allows bi-/multi-directional domain adaptation with a single encoder-decoder network and aligns their domain shift. Additionally, we propose a content-adaptive domain transfer module that helps retain scene structure while transferring style. Extensive experiments show our model successfully separates content-style factors and synthesizes visually pleasing domain-transferred images. The proposed method demonstrates state-of-the-art performance on standard digit classification tasks as well as semantic segmentation tasks.
Look Before You Leap: Learning Landmark Features for One-Stage Visual Grounding
Authors: Dongze Lian, Weixin Luo, Shenghua Gao Binbin Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: An LBYL ( 'Look Before You Leap' ) Network is proposed for end-to-end trainable one-stage visual grounding. The idea behind LBYL-Net is intuitive and straightforward: we follow a language's description to localize the target object based on its relative spatial relation to 'Landmarks', which is characterized by some spatial positional words and some descriptive words about the object. The core of our LBYL-Net is a landmark feature convolution module that transmits the visual features with the guidance of linguistic description along with different directions. Consequently, such a module encodes the relative spatial positional relations between the current object and its context. Then we combine the contextual information from the landmark feature convolution module with the target's visual features for grounding. To make this landmark feature convolution light-weight, we introduce a dynamic programming algorithm (termed dynamic max pooling) with low complexity to extract the landmark feature. Thanks to the landmark feature convolution module, we mimic the human behavior of `Look Before You Leap` to design an LBYL-Net, which takes full consideration of contextual information. Extensive experiments show our method's effectiveness in four grounding datasets. Specifically, our LBYL-Net outperforms all state-of-the-art two-stage and one-stage methods on ReferitGame. On RefCOCO and RefCOCO+, Our LBYL-Net also achieves comparable results or even better results than existing one-stage methods. Code is available at https://github.com/svip-lab/LBYLNet.
Information Bottleneck Disentanglement for Identity Swapping
Authors: Huaibo Huang, Chaoyou Fu, Zhaoyang Li, Ran He Gege Gao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Improving the performance of face forgery detectors often requires more identity-swapped images of higher-quality. One core objective of identity swapping is to generate identity-discriminative faces that are distinct from the target while identical to the source. To this end, properly disentangling identity and identity-irrelevant information is critical and remains a challenging endeavor. In this work, we propose a novel information disentangling and swapping network, called InfoSwap, to extract the most expressive information for identity representation from a pre-trained face recognition model. The key insight of our method is to formulate the learning of disentangled representations as optimizing an information bottleneck trade-off, in terms of finding an optimal compression of the pre-trained latent features. Moreover, a novel identity contrastive loss is proposed for further disentanglement by requiring a proper distance between the generated identity and the target. While the most prior works have focused on using various loss functions to implicitly guide the learning of representations, we demonstrate that our model can provide explicit supervision for learning disentangled representations, achieving impressive performance in generating more identity-discriminative swapped faces.
DualGraph: A Graph-Based Method for Reasoning About Label Noise
Authors: XiMing Xing, Liang Liu HaiYang Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Unreliable labels derived from large-scale dataset prevent neural networks from fully exploring the data. Existing methods of learning with noisy labels primarily take noise-cleaning-based and sample-selection-based methods. However, for numerous studies on account of the above two views, selected samples cannot take full advantage of all data points and cannot represent actual distribution of categories, in particular if label annotation is corrupted. In this paper, we start from a different perspective and propose a robust learning algorithm called DualGraph, which aims to capture structural relations among labels at two different levels with graph neural networks including instance-level and distribution-level relations. Specifically, the instance-level relation utilizes instance similarity characterize sample category, while the distribution-level relation describes instance similarity distribution from each sample to all other samples. Since the distribution-level relation is robust to label noise, our network propagates it as supervised signals to refine instance-level similarity. Combining two level relations, we design an end-to-end training paradigm to counteract noisy labels while generating reliable predictions. We conduct extensive experiments on the noisy CIFAR-10 dataset, CIFAR-100 dataset, and the Clothing1M dataset. The results demonstrate the advantageous performance of the proposed method in comparison to state-of-the-art baselines.
Automatic Correction of Internal Units in Generative Neural Networks
Authors: Haedong Jeong, Jiyeon Han, Hwanil Choi, Jaesik Choi Ali Tousi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Generative Adversarial Networks (GANs) have shown satisfactory performance in synthetic image generation by devising complex network structure and adversarial training scheme. Even though GANs are able to synthesize realistic images, there exists a number of generated images with defective visual patterns which are known as artifacts. While most of the recent work tries to fix artifact generations by perturbing latent code, few investigate internal units of a generator to fix them. In this work, we devise a method that automatically identifies the internal units generating various types of artifact images. We further propose the sequential correction algorithm which adjusts the generation flow by modifying the detected artifact units to improve the quality of generation while preserving the original outline. Our method outperforms the baseline method in terms of FID-score and shows satisfactory results with human evaluation.
Generating Manga From Illustrations via Mimicking Manga Creation Workflow
Authors: Xinrui Wang, Qingnan Fan, Yi Ji, Chunping Liu Lvmin Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We present a framework to generate manga from digital illustrations. In professional mange studios, the manga create workflow consists of three key steps: (1) Artists use line drawings to delineate the structural outlines in manga storyboards. (2) Artists apply several types of regular screentones to render the shading, occlusion, and object materials. (3) Artists selectively paste irregular screen textures onto the canvas to achieve various background layouts or special effects. Motivated by this workflow, we propose a data-driven framework to convert a digital illustration into three corresponding components: manga line drawing, regular screentone, and irregular screen texture. These components can be directly composed into manga images and can be further retouched for more plentiful manga creations. To this end, we create a large-scale dataset with these three components annotated by artists in a human-in-the-loop manner. We conduct both perceptual user study and qualitative evaluation of the generated manga, and observe that our generated image layers for these three components are practically usable in the daily works of manga artists. We provide 60 qualitative results and 15 additional comparisons in the supplementary material. We will make our presented manga dataset publicly available to assist related applications.
Multi-Decoding Deraining Network and Quasi-Sparsity Based Training
Authors: Chao Ma, Bing Zeng Yinglong Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Existing deep deraining models are mainly learned via directly minimizing the statistical differences between rainy images and rain-free ground truths. They emphasize learning a mapping from rainy images to rain-free images with supervision. Despite the demonstrated success, these methods do not perform well on restoring the fine-grained local details or removing blurry rainy traces. In this work, we aim to exploit the intrinsic priors of rainy images and develop intrinsic loss functions to facilitate training deraining networks, which decompose a rainy image into a rain-free background layer and a rainy layer containing intact rain streaks. To this end, we introduce the quasi-sparsity prior to train network so as to generate two sparse layers with intact textures of different objects. Then we explore the low-value prior to compensate sparsity, forcing all rain streaks to enter into one layer while non-rain contents into another layer to restore image details. We introduce a multi-decoding structure to specially supervise the generation of multi-type deraining features. This helps to learn the most contributory features to deraining in respective spaces. Moreover, our model stabilizes the feature values from multi-spaces via information sharing to alleviate potential artifacts, which also accelerates the running speed. Extensive experiments show that the proposed deraining method outperforms the state-of-the-art approaches in terms of effectiveness and efficiency.