mtybilly/Instruct2DS huggingface dataset:
Memory Oriented Transfer Learning for Semi-Supervised Image Deraining
Authors: Huaibo Huang, Aijing Yu, Ran He
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Deep learning based methods have shown dramatic improvements in image rain removal by using large-scale paired data of synthetic datasets. However, due to the various appearances of real rain streaks that may be different from those in the synthetic training data, it is challenging to directly extend existing methods to the real-world scenes. To address this issue, we propose a memory-oriented semi-supervised (MOSS) method which enables the network to explore and exploit the properties of rain streaks from both synthetic and real data. The key aspect of our method is designing an encoder-decoder neural network that is augmented with a self-supervised memory module, where items in the memory record the prototypical patterns of rain degradations and are updated in a self-supervised way. Consequently, the rainy styles can be comprehensively derived from synthetic or real-world degraded images without the need for clean labels. Furthermore, we present a self-training mechanism that attempts to transfer deraining knowledge from supervised rain removal to unsupervised cases. An additional target network, which is updated with an exponential moving average of the online deraining network, is utilized to produce pseudo-labels for unlabeled rainy images. Meanwhile, the deraining network is optimized with supervised objectives on both synthetic paired data and pseudo-paired noisy data. Extensive experiments show that the proposed method achieves more appealing results not only on limited labeled data but also on unlabeled real-world images than recent state-of-the-art methods.
Instance Localization for Self-Supervised Detection Pretraining
Authors: Ceyuan Yang, Zhirong Wu, Bolei Zhou, Stephen Lin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Prior research on self-supervised learning has led to considerable progress on image classification, but often with degraded transfer performance on object detection. The objective of this paper is to advance self-supervised pretrained models specifically for object detection. Based on the inherent difference between classification and detection, we propose a new self-supervised pretext task, called instance localization. Image instances are pasted at various locations and scales onto background images. The pretext task is to predict the instance category given the composited images as well as the foreground bounding boxes. We show that integration of bounding boxes into pretraining promotes better alignment between convolutional features and region boxes. In addition, we propose an augmentation method on the bounding boxes to further enhance this feature alignment. As a result, our model becomes weaker at Imagenet semantic classification but stronger at image patch localization, with an overall stronger pretrained model for object detection. Experimental results demonstrate that our approach yields state-of-the-art transfer learning results for object detection on PASCAL VOC and MSCOCO.
Adaptive Methods for Real-World Domain Generalization
Authors: Abhimanyu Dubey, Vignesh Ramanathan, Alex Pentland, Dhruv Mahajan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Invariant approaches have been remarkably successful in tackling the problem of domain generalization, where the objective is to perform inference on data distributions different from those used in training. In our work, we investigate whether it is possible to leverage domain information from the unseen test samples themselves. We propose a domain-adaptive approach consisting of two steps: a) we first learn a discriminative domain embedding from unsupervised training examples, and b) use this domain embedding as supplementary information to build a domain-adaptive model, that takes both the input as well as its domain into account while making predictions. For unseen domains, our method simply uses few unlabelled test examples to construct the domain embedding. This enables adaptive classification on any unseen domain. Our approach achieves state-of-the-art performance on various domain generalization benchmarks. In addition, we introduce the first real-world, large-scale domain generalization benchmark, Geo-YFCC, containing 1.1M samples over 40 training, 7 validation and 15 test domains, orders of magnitude larger than prior work. We show that the existing approaches either do not scale to this dataset or underperform compared to the simple baseline of training a model on the union of data from all training domains. In contrast, our approach achieves a significant 1% improvement.
Deep Animation Video Interpolation in the Wild
Authors: Li Siyao, Shiyu Zhao, Weijiang Yu, Wenxiu Sun, Dimitris Metaxas, Chen Change Loy, Ziwei Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In the animation industry, cartoon videos are usually produced at low frame rate since hand drawing of such frames is costly and time-consuming. Therefore, it is desirable to develop computational models that can automatically interpolate the in-between animation frames. However, existing video interpolation methods fail to produce satisfying results on animation data. Compared to natural videos, animation videos possess two unique characteristics that make frame interpolation difficult: 1) cartoons comprise lines and smooth color pieces. The smooth areas lack textures and make it difficult to estimate accurate motions on animation videos. 2) cartoons express stories via exaggeration. Some of the motions are non-linear and extremely large. In this work, we formally define and study the animation video interpolation problem for the first time. To address the aforementioned challenges, we propose an effective framework, AnimeInterp, with two dedicated modules in a coarse-to-fine manner. Specifically, 1) Segment-Guided Matching resolves the "lack of textures" challenge by exploiting global matching among color pieces that are piece-wise coherent. 2) Recurrent Flow Refinement resolves the "non-linear and extremely large motion" challenge by recurrent predictions using a transformer-like architecture. To facilitate comprehensive training and evaluations, we build a large-scale animation triplet dataset, ATD-12K, which comprises 12,000 triplets with rich annotations. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art interpolation methods for animation videos. Notably, AnimeInterp shows favorable perceptual quality and robustness for animation scenarios in the wild. The proposed dataset and code are available at https://github.com/lisiyao21/AnimeInterp/.
Isometric Multi-Shape Matching
Authors: Maolin Gao, Zorah Lahner, Johan Thunberg, Daniel Cremers, Florian Bernard
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer. The vast majority of correspondence methods aim to find a solution between pairs of shapes, even if multiple instances of the same class are available. While isometries are often studied in shape correspondence problems, they have not been considered explicitly in the multi-matching setting. This paper closes this gap by proposing a novel optimisation formulation for isometric multi-shape matching. We present a suitable optimisation algorithm for solving our formulation and provide a convergence and complexity analysis. Moreover, our algorithm obtains multi-matchings that are cycle-consistent without having to explicitly enforce cycle-consistency constraints. We demonstrate the superior performance of our method on various datasets and set the new state-of-the-art in isometric multi-shape matching.
Spatially Consistent Representation Learning
Authors: Byungseok Roh, Wuhyun Shin, Ildoo Kim, Sungwoong Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. Especially, recent contrastive learning methods have shown impressive performances on downstream image classification tasks. While these contrastive methods mainly focus on generating invariant global representations at the image-level under semantic-preserving transformations, they are prone to overlook spatial consistency of local representations and therefore have a limitation in pretraining for localization tasks such as object detection and instance segmentation. Moreover, aggressively cropped views used in existing contrastive methods can minimize representation distances between the semantically different regions of a single image. In this paper, we propose a spatially consistent representation learning algorithm (SCRL) for multi-object and location-specific tasks. In particular, we devise a novel self-supervised objective that tries to produce coherent spatial representations of a randomly cropped local region according to geometric translations and zooming operations. On various downstream localization tasks with benchmark datasets, the proposed SCRL shows significant performance improvements over the image-level supervised pretraining as well as the state-of-the-art self-supervised learning methods.
Semantic Scene Completion via Integrating Instances and Scene In-the-Loop
Authors: Yingjie Cai, Xuesong Chen, Chao Zhang, Kwan-Yee Lin, Xiaogang Wang, Hongsheng Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Semantic Scene Completion aims at reconstructing a complete 3D scene with precise voxel-wise semantics from a single-view depth or RGBD image. It is a crucial but challenging problem for indoor scene understanding. In this work, we present a novel framework named Scene-Instance-Scene Network (SISNet), which takes advantages of both instance and scene level semantic information. Our method is capable of inferring fine-grained shape details as well as nearby objects whose semantic categories are easily mixedup. The key insight is that we decouple the instances from a coarsely completed semantic scene instead of a raw input image to guide the reconstruction of instances and the overall scene. SISNet conducts iterative scene-to-instance (SI) and instance-to-scene (IS) semantic completion. Specifically, the SI is able to encode objects' surrounding context for effectively decoupling instances from the scene and each instance could be voxelized into higher resolution to capture finer details. With IS, fine-grained instance information can be integrated back into the 3D scene and thus leads to more accurate semantic scene completion. Utilizing such an iterative mechanism, the scene and instance completion benefits each other to achieve higher completion accuracy. Extensively experiments show that our proposed method consistently outperforms state-of-the-art methods on both real NYU, NYUCAD and synthetic SUNCG-RGBD datasets. The code and the supplementary material will be available at https://github.com/yjcaimeow/SISNet.
Efficient Deformable Shape Correspondence via Multiscale Spectral Manifold Wavelets Preservation
Authors: Ling Hu, Qinsong Li, Shengjun Liu, Xinru Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: The functional map framework has proven to be extremely effective for representing dense correspondences between deformable shapes. A key step in this framework is to formulate suitable preservation constraints to encode the geometric information that must be preserved by the unknown map. For this issue, we construct novel and powerful constraints to determine the functional map, where multiscale spectral manifold wavelets are required to be preserved at each scale correspondingly. Such constraints allow us to extract significantly more information than previous methods, especially those based on descriptor preservation constraints, and strongly ensure the isometric property of the map. In addition, we also propose a remarkable efficient iterative method to alternatively update the functional maps and pointwise maps. Moreover, when we use the tight wavelet frames in iterations, the computation of the functional maps boils down to a simple filtering procedure with low-pass and various band-pass filters, which avoids time-consuming solving large systems of linear equations commonly presented in functional maps. We demonstrate on a wide variety of experiments with different datasets that our approach achieves significant improvements both in the shape correspondence quality and the computing efficiency.
TearingNet: Point Cloud Autoencoder To Learn Topology-Friendly Representations
Authors: Jiahao Pang, Duanshun Li, Dong Tian
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Topology matters. Despite the recent success of point cloud processing with geometric deep learning, it remains arduous to capture the complex topologies of point cloud data with a learning model. Given a point cloud dataset containing objects with various genera, or scenes with multiple objects, we propose an autoencoder, TearingNet, which tackles the challenging task of representing the point clouds using a fixed-length descriptor. Unlike existing works directly deforming predefined primitives of genus zero (e.g., a 2D square patch) to an object-level point cloud, our TearingNet is characterized by a proposed Tearing network module and a Folding network module interacting with each other iteratively. Particularly, the Tearing network module learns the point cloud topology explicitly. By breaking the edges of a primitive graph, it tears the graph into patches or with holes to emulate the topology of a target point cloud, leading to faithful reconstructions. Experimentation shows the superiority of our proposal in terms of reconstructing point clouds as well as generating more topology-friendly representations than benchmarks.
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics
Authors: Yanzhao Wu, Ling Liu, Zhongwei Xie, Ka-Ho Chow, Wenqi Wei
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Neural network ensembles are gaining popularity by harnessing the complementary wisdom of multiple base models. Ensemble teams with high diversity promote high failure independence, which is effective for boosting the overall ensemble accuracy. This paper provides an in-depth study on how to design and compute ensemble diversity, which can capture the complementary decision capacity of ensemble member models. We make three original contributions. First, we revisit the ensemble diversity metrics in the literature and analyze the inherent problems of poor correlation between ensemble diversity and ensemble accuracy, which leads to the low quality ensemble selection using such diversity metrics. Second, instead of computing diversity scores for ensemble teams of different sizes using the same criteria, we introduce focal model based ensemble diversity metrics, coined as FQ-diversity metrics. Our new metrics significantly improve the intrinsic correlation between high ensemble diversity and high ensemble accuracy. Third, we introduce a diversity fusion method, coined as the EQ-diversity metric, by integrating the top three most representative FQ-diversity metrics. Comprehensive experiments on two benchmark datasets (CIFAR-10 and ImageNet) show that our FQ and EQ diversity metrics are effective for selecting high diversity ensemble teams to boost overall ensemble accuracy.
WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition
Authors: Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jiwen Lu, Dalong Du, Jie Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In this paper, we contribute a new million-scale face benchmark containing noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol. Firstly, we collect 4M name list and download 260M faces from the Internet. Then, a Cleaning Automatically utilizing Self-Training (CAST) pipeline is devised to purify the tremendous WebFace260M, which is efficient and scalable. To the best of our knowledge, the cleaned WebFace42M is the largest public face recognition training set and we expect to close the data gap between academia and industry. Referring to practical scenarios, Face Recognition Under Inference Time conStraint (FRUITS) protocol and a test set are constructed to comprehensively evaluate face matchers. Equipped with this benchmark, we delve into million-scale face recognition problems. A distributed framework is developed to train face recognition models efficiently without tampering with the performance. Empowered by WebFace42M, we reduce relative 40% failure rate on the challenging IJB-C set, and rank the 3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior performance compared with public training set. Furthermore, comprehensive baselines are established on our rich-attribute test set under FRUITS-100ms/500ms/1000ms protocol, including MobileNet, EfficientNet, AttentionNet, ResNet, SENet, ResNeXt and RegNet families. Benchmark website is https://www.face-benchmark.org.
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection
Authors: Pei Sun, Weiyue Wang, Yuning Chai, Gamaleldin Elsayed, Alex Bewley, Xiao Zhang, Cristian Sminchisescu, Dragomir Anguelov
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: The detection of 3D objects from LiDAR data is a critical component in most autonomous driving systems. Safe, high speed driving needs larger detection ranges, which are enabled by new LiDARs. These larger detection ranges require more efficient and accurate detection models. Towards this goal, we propose Range Sparse Net (RSN) - a simple, efficient, and accurate 3D object detector - in order to tackle real time 3D object detection in this extended detection regime. RSN predicts foreground points from range images and applies sparse convolutions on the selected fore-ground points to detect objects. The lightweight 2D convolutions on dense range images results in significantly fewer selected foreground points, thus enabling the later sparse convolutions in RSN to efficiently operate. Combining features from the range image further enhance detection ac-curacy. RSN runs at more than 60 frames per second on a 150mx150m detection region on Waymo Open Dataset (WOD) while being more accurate than previously published detectors. RSN is ranked first in the WOD leaderboard based on the APH/LEVEL1 metrics for LiDAR-based pedestrian and vehicle detection, while being several times faster than alternatives.
Labeled From Unlabeled: Exploiting Unlabeled Data for Few-Shot Deep HDR Deghosting
Authors: K. Ram Prabhakar, Gowtham Senthil, Susmit Agrawal, R. Venkatesh Babu, Rama Krishna Sai S Gorthi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: High Dynamic Range (HDR) deghosting is an indispensable tool in capturing wide dynamic range scenes without ghosting artifacts. Recently, convolutional neural networks (CNNs) have shown tremendous success in HDR deghosting. However, CNN-based HDR deghosting methods require collecting large datasets with ground truth, which is a tedious and time-consuming process. This paper proposes a pioneering work by introducing zero and few-shot learning strategies for data-efficient HDR deghosting. Our approach consists of two stages of training. In stage one, we train the model with few labeled (5 or less) dynamic samples and a pool of unlabeled samples with a self-supervised loss. We use the trained model to predict HDRs for the unlabeled samples. To derive data for the next stage of training, we propose a novel method for generating corresponding dynamic inputs from the predicted HDRs of unlabeled data. The generated artificial dynamic inputs and predicted HDRs are used as paired labeled data. In stage two, we finetune the model with the original few labeled data and artificially generated labeled data. Our few-shot approach outperforms many fully-supervised methods in two publicly available datasets, using as little as five labeled dynamic samples.
Convolutional Dynamic Alignment Networks for Interpretable Classifications
Authors: Moritz Bohle, Mario Fritz, Bernt Schiele
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA-Nets), which are performant classifiers with a high degree of inherent interpretability. Their core building blocks are Dynamic Alignment Units (DAUs), which linearly transform their input with weight vectors that dynamically align with task-relevant patterns. As a result, CoDA-Nets model the classification prediction through a series of input-dependent linear transformations, allowing for linear decomposition of the output into individual input contributions. Given the alignment of the DAUs, the resulting contribution maps align with discriminative input patterns. These model-inherent decompositions are of high visual quality and outperform existing attribution methods under quantitative metrics. Further, CoDA-Nets constitute performant classifiers, achieving on par results to ResNet and VGG models on e.g. CIFAR-10 and TinyImagenet.
EDNet: Efficient Disparity Estimation With Cost Volume Combination and Attention-Based Spatial Residual
Authors: Songyan Zhang, Zhicheng Wang, Qiang Wang, Jinshuo Zhang, Gang Wei, Xiaowen Chu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Existing state-of-the-art disparity estimation works mostly leverage the 4D concatenation volume and construct a very deep 3D convolution neural network (CNN) for disparity regression, which is inefficient due to the high memory consumption and slow inference speed. In this paper, we propose a network named EDNet for efficient disparity estimation. Firstly, we construct a combined volume which incorporates contextual information from the squeezed concatenation volume and feature similarity measurement from the correlation volume. The combined volume can be next aggregated by 2D convolutions which are faster and require less memory than 3D convolutions. Secondly, we propose an attention-based spatial residual module to generate attention-aware residual features. The attention mechanism is applied to provide intuitive spatial evidence about inaccurate regions with the help of error maps at multiple scales and thus improve the residual learning efficiency. Extensive experiments on the Scene Flow and KITTI datasets show that EDNet outperforms the previous 3D CNN based works and achieves state-of-the-art performance with significantly faster speed and less memory consumption.
Unsupervised Visual Representation Learning by Tracking Patches in Video
Authors: Guangting Wang, Yizhou Zhou, Chong Luo, Wenxuan Xie, Wenjun Zeng, Zhiwei Xiong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Inspired by the fact that human eyes continue to develop tracking ability in early and middle childhood, we propose to use tracking as a proxy task for a computer vision system to learn the visual representations. Modelled on the Catch game played by the children, we design a Catch-the-Patch (CtP) game for a 3D-CNN model to learn visual representations that would help with video-related tasks. In the proposed pretraining framework, we cut an image patch from a given video and let it scale and move according to a pre-set trajectory. The proxy task is to estimate the position and size of the image patch in a sequence of video frames, given only the target bounding box in the first frame. We discover that using multiple image patches simultaneously brings clear benefits. We further increase the difficulty of the game by randomly making patches invisible. Extensive experiments on mainstream benchmarks demonstrate the superior performance of CtP against other video pretraining methods. In addition, CtP-pretrained features are less sensitive to domain gaps than those trained by a supervised action recognition task. When both trained on Kinetics-400, we are pleasantly surprised to find that CtP-pretrained representation achieves much higher action classification accuracy than its fully supervised counterpart on Something-Something dataset.
Wasserstein Contrastive Representation Distillation
Authors: Liqun Chen, Dong Wang, Zhe Gan, Jingjing Liu, Ricardo Henao, Lawrence Carin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compact than the former. Existing work, e.g., using Kullback-Leibler divergence for distillation, may fail to capture important structural knowledge in the teacher network and often lacks the ability for feature generalization, particularly in situations when teacher and student are built to address different classification tasks. We propose Wasserstein Contrastive Representation Distillation (WCoRD), which leverages both primal and dual forms of Wasserstein distance for KD. The dual form is used for global knowledge transfer, yielding a contrastive learning objective that maximizes the lower bound of mutual information between the teacher and the student networks. The primal form is used for local contrastive knowledge transfer within a mini-batch, effectively matching the distributions of features between the teacher and the student networks. Experiments demonstrate that the proposed WCoRD method outperforms state-of-the-art approaches on privileged information distillation, model compression and cross-modal transfer.
FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains
Authors: Jia Li, Zhaoyang Li, Jie Cao, Xingguang Song, Ran He
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In this work, we propose a novel two-stage framework named FaceInpainter to implement controllable Identity-Guided Face Inpainting (IGFI) under heterogeneous domains. Concretely, by explicitly disentangling foreground and background of the target face, the first stage focuses on adaptive face fitting to the fixed background via a Styled Face Inpainting Network (SFI-Net), with 3D priors and texture code of the target, as well as identity factor of the source face. It is challenging to deal with the inconsistency between the new identity of the source and the original background of the target, concerning the face shape and appearance on the fused boundary. The second stage consists of a Joint Refinement Network (JR-Net) to refine the swapped face. It leverages AdaIN considering identity and multi-scale texture codes, for feature transformation of the decoded face from SFI-Net with facial occlusions. We adopt the contextual loss to implicitly preserve the attributes, encouraging face deformation and fewer texture distortions. Experimental results demonstrate that our approach handles high-quality identity adaptation to heterogeneous domains, exhibiting the competitive performance compared with state-of-the-art methods concerning both attribute and identity fidelity.
How Robust Are Randomized Smoothing Based Defenses to Data Poisoning?
Authors: Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Predictions of certifiably robust classifiers remain constant in a neighborhood of a point, making them resilient to test-time attacks with a guarantee. In this work, we present a previously unrecognized threat to robust machine learning models that highlights the importance of training-data quality in achieving high certified adversarial robustness. Specifically, we propose a novel bilevel optimization-based data poisoning attack that degrades the robustness guarantees of certifiably robust classifiers. Unlike other poisoning attacks that reduce the accuracy of the poisoned models on a small set of target points, our attack reduces the average certified radius (ACR) of an entire target class in the dataset. Moreover, our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods such as Gaussian data augmentation [??], MACER [??], and SmoothAdv [??] that achieve high certified adversarial robustness. To make the attack harder to detect, we use clean-label poisoning points with imperceptible distortions. The effectiveness of the proposed method is evaluated by poisoning MNIST and CIFAR10 datasets and training deep neural networks using previously mentioned training methods and certifying the robustness with randomized smoothing. The ACR of the target class, for models trained on generated poison data, can be reduced by more than 30%. Moreover, the poisoned data is transferable to models trained with different training methods and models with different architectures.
Deep Learning in Latent Space for Video Prediction and Compression
Authors: Bowen Liu, Yu Chen, Shiyu Liu, Hun-Seok Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Learning-based video compression has achieved substantial progress during recent years. The most influential approaches adopt deep neural networks (DNNs) to remove spatial and temporal redundancies by finding the appropriate lower-dimensional representations of frames in the video. We propose a novel DNN based framework that predicts and compresses video sequences in the latent vector space. The proposed method first learns the efficient lower-dimensional latent space representation of each video frame and then performs inter-frame prediction in that latent domain. The proposed latent domain compression of individual frames is obtained by a deep autoencoder trained with a generative adversarial network (GAN). To exploit the temporal correlation within the video frame sequence, we employ a convolutional long short-term memory (ConvLSTM) network to predict the latent vector representation of the future frame. We demonstrate our method with two applications; video compression and abnormal event detection that share the identical latent frame prediction network. The proposed method exhibits superior or competitive performance compared to the state-of-the-art algorithms specifically designed for either video compression or anomaly detection.
PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization
Authors: Guangming Wang, Xinrui Wu, Zhe Liu, Hesheng Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using hierarchical embedding mask optimization is proposed in this paper. In this model, the Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task is built to refine the estimated pose in a coarse-to-fine approach hierarchically. An attentive cost volume is built to associate two point clouds and obtain embedding motion patterns. Then, a novel trainable embedding mask is proposed to weigh the local motion patterns of all points to regress the overall pose and filter outlier points. The estimated current pose is used to warp the first point cloud to bridge the distance to the second point cloud, and then the cost volume of the residual motion is built. At the same time, the embedding mask is optimized hierarchically from coarse to fine to obtain more accurate filtering information for pose refinement. The trainable pose warp-refinement process is iteratively used to make the pose estimation more robust for outliers. The superior performance and effectiveness of our LiDAR odometry model are demonstrated on KITTI odometry dataset. Our method outperforms all recent learning-based methods and outperforms the geometry-based approach, LOAM with mapping optimization, on most sequences of KITTI odometry dataset. Our source codes will be released on https://github.com/IRMVLab/PWCLONet.
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-Supervised Continual Learning
Authors: Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, Jun Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications. In this work, we consider semi-supervised continual learning (SSCL) that incrementally learns from partially labeled data. Observing that existing continual learning methods lack the ability to continually exploit the unlabeled data, we propose deep Online Replay with Discriminator Consistency (ORDisCo) to interdependently learn a classifier with a conditional generative adversarial network (GAN), which continually passes the learned data distribution to the classifier. In particular, ORDisCo replays data sampled from the conditional generator to the classifier in an online manner, exploiting unlabeled data in a time- and storage-efficient way. Further, to explicitly overcome the catastrophic forgetting of unlabeled data, we selectively stabilize parameters of the discriminator that are important for discriminating the pairs of old unlabeled data and their pseudo-labels predicted by the classifier. We extensively evaluate ORDisCo on various semi-supervised learning benchmark datasets for SSCL, and show that ORDisCo achieves significant performance improvement on SVHN, CIFAR10 and Tiny-ImageNet, compared to strong baselines.
Dynamic Region-Aware Convolution
Authors: Jin Chen, Xijun Wang, Zichao Guo, Xiangyu Zhang, Jian Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We propose a new convolution called Dynamic Region-Aware Convolution (DRConv), which can automatically assign multiple filters to corresponding spatial regions where features have similar representation. In this way, DRConv outperforms standard convolution in modeling semantic variations. Standard convolutional layer can increase the number of filers to extract more visual elements but results in high computational cost. More gracefully, our DRConv transfers the increasing channel-wise filters to spatial dimension with learnable instructor, which not only improve representation ability of convolution, but also maintains computational cost and the translation-invariance as standard convolution dose. DRConv is an effective and elegant method for handling complex and variable spatial information distribution. It can substitute standard convolution in any existing networks for its plug-and-play property, especially to power convolution layers in efficient networks. We evaluate DRConv on a wide range of models (MobileNet series, ShuffleNetV2, etc.) and tasks (Classification, Face Recognition, Detection and Segmentation). On ImageNet classification, DRConv-based ShuffleNetV2-0.5x achieves state-of-the-art performance of 67.1% at 46M multiply-adds level with 6.3% relative improvement.
Explore Image Deblurring via Encoded Blur Kernel Space
Authors: Phong Tran, Anh Tuan Tran, Quynh Phung, Minh Hoai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space. Assuming the encoded kernel space is close enough to in-the-wild blur operators, we propose an alternating optimization algorithm for blind image deblurring. It approximates an unseen blur operator by a kernel in the encoded space and searches for the corresponding sharp image. Unlike recent deep-learning-based methods, our system can handle unseen blur kernel, while avoiding using complicated handcrafted priors on the blur operator often found in classical methods. Due to the method's design, the encoded kernel space is fully differentiable, thus can be easily adopted in deep neural network models. Moreover, our method can be used for blur synthesis by transferring existing blur operators from a given dataset into a new domain. Finally, we provide experimental results to confirm the effectiveness of the proposed method.
BCNet: Searching for Network Width With Bilaterally Coupled Network
Authors: Xiu Su, Shan You, Fei Wang, Chen Qian, Changshui Zhang, Chang Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constraints. To fulfill the searching, a one-shot supernet is usually leveraged to efficiently evaluate the performance \wrt different network widths. However, current methods mainly follow a unilaterally augmented (UA) principle for the evaluation of each width, which induces the training unfairness of channels in supernet. In this paper, we introduce a new supernet called Bilaterally Coupled Network (BCNet) to address this issue. In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately. Besides, we leverage a stochastic complementary strategy for training the BCNet, and propose a prior initial population sampling method to boost the performance of the evolutionary search. Extensive experiments on benchmark CIFAR-10 and ImageNet datasets indicate that our method can achieve state-of-the-art or competing performance over other baseline methods. Moreover, our method turns out to further boost the performance of NAS models by refining their network widths. For example, with the same FLOPs budget, our obtained EfficientNet-B0 achieves 77.36% Top-1 accuracy on ImageNet dataset, surpassing the performance of original setting by 0.48%.
Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias
Authors: Yunhan Zhao, Shu Kong, Charless Fowlkes
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Monocular depth predictors are typically trained on large-scale training sets which are naturally biased w.r.t the distribution of camera poses. As a result, trained predictors fail to make reliable depth predictions for testing examples captured under uncommon camera poses. To address this issue, we propose two novel techniques that exploit the camera pose during training and prediction. First, we introduce a simple perspective-aware data augmentation that synthesizes new training examples with more diverse views by perturbing the existing ones in a geometrically consistent manner. Second, we propose a conditional model that exploits the per-image camera pose as prior knowledge by encoding it as a part of the input. We show that jointly applying the two methods improves depth prediction on images captured under uncommon and even never-before-seen camera poses. We show that our methods improve performance when applied to a range of different predictor architectures. Lastly, we show that explicitly encoding the camera pose distribution improves the generalization performance of a synthetically trained depth predictor when evaluated on real images.
Lipstick Ain't Enough: Beyond Color Matching for In-the-Wild Makeup Transfer
Authors: Thao Nguyen, Anh Tuan Tran, Minh Hoai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Makeup transfer is the task of applying on a source face the makeup style from a reference image. Real-life makeups are diverse and wild, which cover not only color-changing but also patterns, such as stickers, blushes, and jewelries. However, existing works overlooked the latter components and confined makeup transfer to color manipulation, focusing only on light makeup styles. In this work, we propose a holistic makeup transfer framework that can handle all the mentioned makeup components. It consists of an improved color transfer branch and a novel pattern transfer branch to learn all makeup properties, including color, shape, texture, and location. To train and evaluate such a system, we also introduce new makeup datasets for real and synthetic extreme makeup. Experimental results show that our framework achieves the state of the art performance on both light and extreme makeup styles.
Generative Interventions for Causal Learning
Authors: Chengzhi Mao, Augustine Cha, Amogh Gupta, Hao Wang, Junfeng Yang, Carl Vondrick
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts. Discriminative models often learn naturally occurring spurious correlations, which cause them to fail on images outside of the training distribution. In this paper, we show that we can steer generative models to manufacture interventions on features caused by confounding factors. Experiments, visualizations, and theoretical results show this method learns robust representations more consistent with the underlying causal relationships. Our approach improves performance on multiple datasets demanding out-of-distribution generalization, and we demonstrate state-of-the-art performance generalizing from ImageNet to ObjectNet dataset.
Graph Stacked Hourglass Networks for 3D Human Pose Estimation
Authors: Tianhan Xu, Wataru Takano
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In this paper, we propose a novel graph convolutional network architecture, Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The proposed architecture consists of repeated encoder-decoder, in which graph-structured features are processed across three different scales of human skeletal representations. This multi-scale architecture enables the model to learn both local and global feature representations, which are critical for 3D human pose estimation. We also introduce a multi-level feature learning approach using different-depth intermediate features and show the performance improvements that result from exploiting multi-scale, multi-level feature representations. Extensive experiments are conducted to validate our approach, and the results show that our model outperforms the state-of-the-art.
Adaptive Aggregation Networks for Class-Incremental Learning
Authors: Yaoyao Liu, Bernt Schiele, Qianru Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase. An inherent problem in CIL is the stability-plasticity dilemma between the learning of old and new classes, i.e., high-plasticity models easily forget old classes, but high-stability models are weak to learn new classes. We alleviate this issue by proposing a novel network architecture called Adaptive Aggregation Networks (AANets), in which we explicitly build two types of residual blocks at each residual level (taking ResNet as the baseline architecture): a stable block and a plastic block. We aggregate the output feature maps from these two blocks and then feed the results to the next-level blocks. We adapt the aggregation weights in order to balance these two types of blocks, i.e., to balance stability and plasticity, dynamically. We conduct extensive experiments on three CIL benchmarks: CIFAR-100, ImageNet-Subset, and ImageNet, and show that many existing CIL methods can be straightforwardly incorporated into the architecture of AANets to boost their performances.
VS-Net: Voting With Segmentation for Visual Localization
Authors: Zhaoyang Huang, Han Zhou, Yijin Li, Bangbang Yang, Yan Xu, Xiaowei Zhou, Hujun Bao, Guofeng Zhang, Hongsheng Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Visual localization is of great importance in robotics and computer vision. Recently, scene coordinate regression based methods have shown good performance in visual localization in small static scenes. However, it still estimates camera poses from many inferior scene coordinates. To address this problem, we propose a novel visual localization framework that establishes 2D-to-3D correspondences between the query image and the 3D map with a series of learnable scene-specific landmarks. In the landmark generation stage, the 3D surfaces of the target scene are over-segmented into mosaic patches whose centers are regarded as the scene-specific landmarks. To robustly and accurately recover the scene-specific landmarks, we propose the Voting with Segmentation Network (VS-Net) to segment the pixels into different landmark patches with a segmentation branch and estimate the landmark locations within each patch with a landmark location voting branch. Since the number of landmarks in a scene may reach up to 5000, training a segmentation network with such a large number of classes is both computation and memory costly for the commonly used cross-entropy loss. We propose a novel prototype-based triplet loss with hard negative mining, which is able to train semantic segmentation networks with a large number of labels efficiently. Our proposed VS-Net is extensively tested on multiple public benchmarks and can outperform state-of-the-art visual localization methods. Code and models are available at https://github.com/zju3dv/VS-Net.