mtybilly/Instruct2DS huggingface dataset:
SuperGlue: Learning Feature Matching With Graph Neural Networks
Authors: Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network. We introduce a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly. Compared to traditional, hand-designed heuristics, our technique learns priors over geometric transformations and regularities of the 3D world through end-to-end training from image pairs. SuperGlue outperforms other learned approaches and achieves state-of-the-art results on the task of pose estimation in challenging real-world indoor and outdoor environments. The proposed method performs matching in real-time on a modern GPU and can be readily integrated into modern SfM or SLAM systems. The code and trained weights are publicly available at github.com/magicleap/SuperGluePretrainedNetwork.
Probabilistic Structural Latent Representation for Unsupervised Embedding
Authors: Mang Ye, Jianbing Shen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Unsupervised embedding learning aims at extracting low-dimensional visually meaningful representations from large-scale unlabeled images, which can then be directly used for similarity-based search. This task faces two major challenges: 1) mining positive supervision from highly similar fine-grained classes and 2) generating to unseen testing categories. To tackle these issues, this paper proposes a probabilistic structural latent representation (PSLR), which incorporates an adaptable softmax embedding to approximate the positive concentrated and negative instance separated properties in the graph latent space. It improves the discriminability by enlarging the positive/negative difference without introducing any additional computational cost while maintaining high learning efficiency. To address the limited supervision using data augmentation, a smooth variational reconstruction loss is introduced by modeling the intra-instance variance, which improves the robustness. Extensive experiments demonstrate the superiority of PSLR over state-of-the-art unsupervised methods on both seen and unseen categories with cosine similarity. Code is available at https://github.com/mangye16/PSLR
How Useful Is Self-Supervised Pretraining for Visual Tasks?
Authors: Alejandro Newell, Jia Deng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Recent advances have spurred incredible progress in self-supervised pretraining for vision. We investigate what factors may play a role in the utility of these pretraining methods for practitioners. To do this, we evaluate various self-supervised algorithms across a comprehensive array of synthetic datasets and downstream tasks. We prepare a suite of synthetic data that enables an endless supply of annotated images as well as full control over dataset difficulty. Our experiments offer insights into how the utility of self-supervision changes as the number of available labels grows as well as how the utility changes as a function of the downstream task and the properties of the training data. We also find that linear evaluation does not correlate with finetuning performance. Code and data is available at \href https://www.github.com/princeton-vl/selfstudy  github.com/princeton-vl/selfstudy .
Action Segmentation With Joint Self-Supervised Temporal Domain Adaptation
Authors: Min-Hung Chen, Baopu Li, Yingze Bao, Ghassan AlRegib, Zsolt Kira
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Despite the recent progress of fully-supervised action segmentation techniques, the performance is still not fully satisfactory. One main challenge is the problem of spatiotemporal variations (e.g. different people may perform the same activity in various ways). Therefore, we exploit unlabeled videos to address this problem by reformulating the action segmentation task as a cross-domain problem with domain discrepancy caused by spatio-temporal variations. To reduce the discrepancy, we propose SelfSupervised Temporal Domain Adaptation (SSTDA), which contains two self-supervised auxiliary tasks (binary and sequential domain prediction) to jointly align cross-domain feature spaces embedded with local and global temporal dynamics, achieving better performance than other Domain Adaptation (DA) approaches. On three challenging benchmark datasets (GTEA, 50Salads, and Breakfast), SSTDA outperforms the current state-of-the-art method by large margins (e.g. for the F1@25 score, from 59.6% to 69.1% on Breakfast, from 73.4% to 81.5% on 50Salads, and from 83.6% to 89.1% on GTEA), and requires only 65% of the labeled training data for comparable performance, demonstrating the usefulness of adapting to unlabeled target videos across variations. The source code is available at https://github.com/cmhungsteve/SSTDA.
Regularizing Discriminative Capability of CGANs for Semi-Supervised Generative Learning
Authors: Yi Liu, Guangchang Deng, Xiangping Zeng, Si Wu, Zhiwen Yu, Hau-San Wong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Semi-supervised generative learning aims to learn the underlying class-conditional distribution of partially labeled data. Generative Adversarial Networks (GANs) have led to promising progress in this task. However, it still needs to further explore the issue of imbalance between real labeled data and fake data in the adversarial learning process. To address this issue, we propose a regularization technique based on Random Regional Replacement (R^3-regularization) to facilitate the generative learning process. Specifically, we construct two types of between-class instances: cross-category ones and real-fake ones. These instances could be closer to the decision boundaries and are important for regularizing the classification and discriminative networks in our class-conditional GANs, which we refer to as R^3-CGAN. Better guidance from these two networks makes the generative network produce instances with class-specific information and high fidelity. We experiment with multiple standard benchmarks, and demonstrate that the R^3-regularization can lead to significant improvement in both classification and class-conditional image synthesis.
State-Relabeling Adversarial Active Learning
Authors: Beichen Zhang, Liang Li, Shijie Yang, Shuhui Wang, Zheng-Jun Zha, Qingming Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Active learning is to design label-efficient algorithms by sampling the most representative samples to be labeled by an oracle. In this paper, we propose a state relabeling adversarial active learning model (SRAAL), that leverages both the annotation and the labeled/unlabeled state information for deriving the most informative unlabeled samples. The SRAAL consists of a representation generator and a state discriminator. The generator uses the complementary annotation information with traditional reconstruction information to generate the unified representation of samples, which embeds the semantic into the whole data representation. Then, we design an online uncertainty indicator in the discriminator, which endues unlabeled samples with different importance. As a result, we can select the most informative samples based on the discriminator's predicted state. We also design an algorithm to initialize the labeled pool, which makes subsequent sampling more efficient. The experiments conducted on various datasets show that our model outperforms the previous state-of-art active learning methods and our initially sampling algorithm achieves better performance.
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
Authors: Yawei Li, Shuhang Gu, Christoph Mayer, Luc Van Gool, Radu Timofte
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: In this paper, we analyze two popular network compression techniques, i.e. filter pruning and low-rank decomposition, in a unified sense. By simply changing the way the sparsity regularization is enforced, filter pruning and low-rank decomposition can be derived accordingly. This provides another flexible choice for network compression because the techniques complement each other. For example, in popular network architectures with shortcut connections (e.g. ResNet), filter pruning cannot deal with the last convolutional layer in a ResBlock while the low-rank decomposition methods can. In addition, we propose to compress the whole network jointly instead of in a layer-wise manner. Our approach proves its potential as it compares favorably to the state-of-the-art on several benchmarks. Code is available at https://github.com/ofsoundof/group_sparsity.
P-nets: Deep Polynomial Neural Networks
Authors: Grigorios G. Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Yannis Panagakis, Jiankang Deng, Stefanos Zafeiriou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their building blocks (e.g., residual blocks, rectifiers, sophisticated normalization schemes, to mention but a few). In this paper, we propose \Pi-Nets, a new class of DCNNs. \Pi-Nets are polynomial neural networks, i.e., the output is a high-order polynomial of the input. \Pi-Nets can be implemented using special kind of skip connections and their parameters can be represented via high-order tensors. We empirically demonstrate that \Pi-Nets have better representation power than standard DCNNs and they even produce good results without the use of non-linear activation functions in a large battery of tasks and signals, i.e., images, graphs, and audio. When used in conjunction with activation functions, \Pi-Nets produce state-of-the-art results in challenging tasks, such as image generation. Lastly, our framework elucidates why recent generative models, such as StyleGAN, improve upon their predecessors, e.g., ProGAN.
G3AN: Disentangling Appearance and Motion for Video Generation
Authors: Yaohui Wang, Piotr Bilinski, Francois Bremond, Antitza Dantcheva
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Creating realistic human videos entails the challenge of being able to simultaneously generate both appearance, as well as motion. To tackle this challenge, we introduce G3AN, a novel spatio-temporal generative model, which seeks to capture the distribution of high dimensional video data and to model appearance and motion in disentangled manner. The latter is achieved by decomposing appearance and motion in a three-stream Generator, where the main stream aims to model spatio-temporal consistency, whereas the two auxiliary streams augment the main stream with multi-scale appearance and motion features, respectively. An extensive quantitative and qualitative analysis shows that our model systematically and significantly outperforms state-of-the-art methods on the facial expression datasets MUG and UvA-NEMO, as well as the Weizmann and UCF101 datasets on human action. Additional analysis on the learned latent representations confirms the successful decomposition of appearance and motion.
DeepFaceFlow: In-the-Wild Dense 3D Facial Motion Estimation
Authors: Mohammad Rami Koujan, Anastasios Roussos, Stefanos Zafeiriou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Dense 3D facial motion capture from only monocular in-the-wild pairs of RGB images is a highly challenging problem with numerous applications, ranging from facial expression recognition to facial reenactment. In this work, we propose DeepFaceFlow, a robust, fast, and highly-accurate framework for the dense estimation of 3D non-rigid facial flow between pairs of monocular images. Our DeepFaceFlow framework was trained and tested on two very large-scale facial video datasets, one of them of our own collection and annotation, with the aid of occlusion-aware and 3D-based loss function. We conduct comprehensive experiments probing different aspects of our approach and demonstrating its improved performance against state-of-the-art flow and 3D reconstruction methods. Furthermore, we incorporate our framework in a full-head state-of-the-art facial video synthesis method and demonstrate the ability of our method in better representing and capturing the facial dynamics, resulting in a highly-realistic facial video synthesis. Given registered pairs of images, our framework generates 3D flow maps at 60 fps.
TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model
Authors: Bo Pang, Yizhuo Li, Yifan Zhang, Muchen Li, Cewu Lu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Multi-object tracking is a fundamental vision problem that has been studied for a long time. As deep learning brings excellent performances to object detection algorithms, Tracking by Detection (TBD) has become the mainstream tracking framework. Despite the success of TBD, this two-step method is too complicated to train in an end-to-end manner and induces many challenges as well, such as insufficient exploration of video spatial-temporal information, vulnerability when facing object occlusion, and excessive reliance on detection results. To address these challenges, we propose a concise end-to-end model TubeTK which only needs one step training by introducing the "bounding-tube" to indicate temporal-spatial locations of objects in a short video clip. TubeTK provides a novel direction of multi-object tracking, and we demonstrate its potential to solve the above challenges without bells and whistles. We analyze the performance of TubeTK on several MOT benchmarks and provide empirical evidence to show that TubeTK has the ability to overcome occlusions to some extent without any ancillary technologies like Re-ID. Compared with other methods that adopt private detection results, our one-stage end-to-end model achieves state-of-the-art performances even if it adopts no ready-made detection results. We hope that the proposed TubeTK model can serve as a simple but strong alternative for video-based MOT task. The code and model will be publicly available accompanying this paper.
Few-Shot Open-Set Recognition Using Meta-Learning
Authors: Bo Liu, Hao Kang, Haoxiang Li, Gang Hua, Nuno Vasconcelos
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: The problem of open-set recognition is considered. While previous approaches only consider this problem in the context of large-scale classifier training, we seek a unified solution for this and the low-shot classification setting. It is argued that the classic softmax classifier is a poor solution for open-set recognition, since it tends to overfit on the training classes. Randomization is then proposed as a solution to this problem. This suggests the use of meta-learning techniques, commonly used for few-shot classification, for the solution of open-set recognition. A new oPen sEt mEta LEaRning (PEELER) algorithm is then introduced. This combines the random selection of a set of novel classes per episode, a loss that maximizes the posterior entropy for examples of those classes, and a new metric learning formulation based on the Mahalanobis distance. Experimental results show that PEELER achieves state of the art open set recognition performance for both few-shot and large-scale recognition. On CIFAR and miniImageNet, it achieves substantial gains in seen/unseen class detection AUROC for a given seen-class classification accuracy.
Sequential 3D Human Pose and Shape Estimation From Point Clouds
Authors: Kangkan Wang, Jin Xie, Guofeng Zhang, Lei Liu, Jian Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: This work addresses the problem of 3D human pose and shape estimation from a sequence of point clouds. Existing sequential 3D human shape estimation methods mainly focus on the template model fitting from a sequence of depth images or the parametric model regression from a sequence of RGB images. In this paper, we propose a novel sequential 3D human pose and shape estimation framework from a sequence of point clouds. Specifically, the proposed framework can regress 3D coordinates of mesh vertices at different resolutions from the latent features of point clouds. Based on the estimated 3D coordinates and features at the low resolution, we develop a spatial-temporal mesh attention convolution (MAC) to predict the 3D coordinates of mesh vertices at the high resolution. By assigning specific attentional weights to different neighboring points in the spatial and temporal domains, our spatial-temporal MAC can capture structured spatial and temporal features of point clouds. We further generalize our framework to the real data of human bodies with a weakly supervised fine-tuning method. The experimental results on SURREAL, Human3.6M, DFAUST and the real detailed data demonstrate that the proposed approach can accurately recover the 3D body model sequence from a sequence of point clouds.
Sequential Mastery of Multiple Visual Tasks: Networks Naturally Learn to Learn and Forget to Forget
Authors: Guy Davidson, Michael C. Mozer
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We explore the behavior of a standard convolutional neural net in a continual-learning setting that introduces visual classification tasks sequentially and requires the net to master new tasks while preserving mastery of previously learned tasks. This setting corresponds to that which human learners face as they acquire domain expertise serially, for example, as an individual studies a textbook. Through simulations involving sequences of ten related visual tasks, we find reason for optimism that nets will scale well as they advance from having a single skill to becoming multi-skill domain experts. We observe two key phenomena. First, forward facilitation---the accelerated learning of task n+1 having learned n previous tasks---grows with n. Second, backward interference---the forgetting of the n previous tasks when learning task n+1 ---diminishes with n. Amplifying forward facilitation is the goal of research on metalearning, and attenuating backward interference is the goal of research on catastrophic forgetting. We find that both of these goals are attained simply through broader exposure to a domain.
Siam R-CNN: Visual Tracking by Re-Detection
Authors: Paul Voigtlaender, Jonathon Luiten, Philip H.S. Torr, Bastian Leibe
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We present Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking. We combine this with a novel tracklet-based dynamic programming algorithm, which takes advantage of re-detections of both the first-frame template and previous-frame predictions, to model the full history of both the object to be tracked and potential distractor objects. This enables our approach to make better tracking decisions, as well as to re-detect tracked objects after long occlusion. Finally, we propose a novel hard example mining strategy to improve Siam R-CNN's robustness to similar looking objects. Siam R-CNN achieves the current best performance on ten tracking benchmarks, with especially strong results for long-term tracking. We make our code and models available at www.vision.rwth-aachen.de/page/siamrcnn.
Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks
Authors: Aditya Golatkar, Alessandro Achille, Stefano Soatto
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We explore the problem of selectively forgetting a particular subset of the data used for training a deep neural network. While the effects of the data to be forgotten can be hidden from the output of the network, insights may still be gleaned by probing deep into its weights. We propose a method for "scrubbing" the weights clean of information about a particular set of training data. The method does not require retraining from scratch, nor access to the data originally used for training. Instead, the weights are modified so that any probing function of the weights is indistinguishable from the same function applied to the weights of a network trained without the data to be forgotten. This condition is a generalized and weaker form of Differential Privacy. Exploiting ideas related to the stability of stochastic gradient descent, we introduce an upper-bound on the amount of information remaining in the weights, which can be estimated efficiently even for deep neural networks.
MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks
Authors: Animesh Karnewar, Oliver Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: While Generative Adversarial Networks (GANs) have seen huge successes in image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due to instability during training and sensitivity to hyperparameters. One commonly accepted reason for this instability is that gradients passing from the discriminator to the generator become uninformative when there isn't enough overlap in the supports of the real and fake distributions. In this work, we propose the Multi-Scale Gradient Generative Adversarial Network (MSG-GAN), a simple but effective technique for addressing this by allowing the flow of gradients from the discriminator to the generator at multiple scales. This technique provides a stable approach for high resolution image synthesis, and serves as an alternative to the commonly used progressive growing technique. We show that MSG-GAN converges stably on a variety of image datasets of different sizes, resolutions and domains, as well as different types of loss functions and architectures, all with the same set of fixed hyperparameters. When compared to state-of-the-art GANs, our approach matches or exceeds the performance in most of the cases we tried.
Transferring Cross-Domain Knowledge for Video Sign Language Recognition
Authors: Dongxu Li, Xin Yu, Chenchen Xu, Lars Petersson, Hongdong Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Word-level sign language recognition (WSLR) is a fundamental task in sign language interpretation. It requires models to recognize isolated sign words from videos. However, annotating WSLR data needs expert knowledge, thus limiting WSLR dataset acquisition. On the contrary, there are abundant subtitled sign news videos on the internet. Since these videos have no word-level annotation and exhibit a large domain gap from isolated signs, they cannot be directly used for training WSLR models. We observe that despite the existence of a large domain gap, isolated and news signs share the same visual concepts, such as hand gestures and body movements. Motivated by this observation, we propose a novel method that learns domain-invariant visual concepts and fertilizes WSLR models by transferring knowledge of subtitled news sign to them. To this end, we extract news signs using a base WSLR model, and then design a classifier jointly trained on news and isolated signs to coarsely align these two domain features. In order to learn domain-invariant features within each class and suppress domain-specific features, our method further resorts to an external memory to store the class centroids of the aligned news signs. We then design a temporal attention based on the learnt descriptor to improve recognition performance. Experimental results on standard WSLR datasets show that our method outperforms previous state-of-the-art methods significantly. We also demonstrate the effectiveness of our method on automatically localizing signs from sign news, achieving 28.1 for AP@0.5.
Flow Contrastive Estimation of Energy-Based Models
Authors: Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial value function. This joint training method has the following traits. (1) The update of the energy-based model is based on noise contrastive estimation, with the flow model serving as a strong noise distribution. (2) The update of the flow model approximately minimizes the Jensen-Shannon divergence between the flow model and the data distribution. (3) Unlike generative adversarial networks (GAN) which estimates an implicit probability distribution defined by a generator model, our method estimates two explicit probabilistic distributions on the data. Using the proposed method we demonstrate a significant improvement on the synthesis quality of the flow model, and show the effectiveness of unsupervised feature learning by the learned energy-based model. Furthermore, the proposed training method can be easily adapted to semi-supervised learning. We achieve competitive results to the state-of-the-art semi-supervised learning methods.
Improving the Robustness of Capsule Networks to Image Affine Transformations
Authors: Jindong Gu, Volker Tresp
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Convolutional neural networks (CNNs) achieve translational invariance by using pooling operations. However, the operations do not preserve the spatial relationships in the learned representations. Hence, CNNs cannot extrapolate to various geometric transformations of inputs. Recently, Capsule Networks (CapsNets) have been proposed to tackle this problem. In CapsNets, each entity is represented by a vector and routed to high-level entity representations by a dynamic routing algorithm. CapsNets have been shown to be more robust than CNNs to affine transformations of inputs. However, there is still a huge gap between their performance on transformed inputs compared to untransformed versions. In this work, we first revisit the routing procedure by (un)rolling its forward and backward passes. Our investigation reveals that the routing procedure contributes neither to the generalization ability nor to the affine robustness of the CapsNets. Furthermore, we explore the limitations of capsule transformations and propose affine CapsNets (Aff-CapsNets), which are more robust to affine transformations. On our benchmark task, where models are trained on the MNIST dataset and tested on the AffNIST dataset, our Aff-CapsNets improve the benchmark performance by a large margin (from 79% to 93.21%), without using any routing mechanism.
Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection
Authors: Huajun Zhou, Xiaohua Xie, Jian-Huang Lai, Zixuan Chen, Lingxiao Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Recently, contour information largely improves the performance of saliency detection. However, the discussion on the correlation between saliency and contour remains scarce. In this paper, we first analyze such correlation and then propose an interactive two-stream decoder to explore multiple cues, including saliency, contour and their correlation. Specifically, our decoder consists of two branches, a saliency branch and a contour branch. Each branch is assigned to learn distinctive features for predicting the corresponding map. Meanwhile, the intermediate connections are forced to learn the correlation by interactively transmitting the features from each branch to the other one. In addition, we develop an adaptive contour loss to automatically discriminate hard examples during learning process. Extensive experiments on six benchmarks well demonstrate that our network achieves competitive performance with a fast speed around 50 FPS. Moreover, our VGG-based model only contains 17.08 million parameters, which is significantly smaller than other VGG-based approaches. Code has been made available at: https://github.com/moothes/ITSD-pytorch.
ViewAL: Active Learning With Viewpoint Entropy for Semantic Segmentation
Authors: Yawar Siddiqui, Julien Valentin, Matthias Niessner
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We propose ViewAL, a novel active learning strategy for semantic segmentation that exploits viewpoint consistency in multi-view datasets. Our core idea is that inconsistencies in model predictions across viewpoints provide a very reliable measure of uncertainty and encourage the model to perform well irrespective of the viewpoint under which objects are observed. To incorporate this uncertainty measure, we introduce a new viewpoint entropy formulation, which is the basis of our active learning strategy. In addition, we propose uncertainty computations on a superpixel level, which exploits inherently localized signal in the segmentation task, directly lowering the annotation costs. This combination of viewpoint entropy and the use of superpixels allows to efficiently select samples that are highly informative for improving the network. We demonstrate that our proposed active learning strategy not only yields the best-performing models for the same amount of required labeled data, but also significantly reduces labeling effort. For instance, our method achieves 95% of maximum achievable network performance using only 7%, 17%, and 24% labeled data on SceneNet-RGBD, ScanNet, and Matterport3D, respectively. On these datasets, the best state-of-the-art method achieves the same performance with 14%, 27% and 33% labeled data. Finally, we demonstrate that labeling using superpixels yields the same quality of ground-truth compared to labeling whole images, but requires 25% less time.
A U-Net Based Discriminator for Generative Adversarial Networks
Authors: Edgar Schonfeld, Bernt Schiele, Anna Khoreva
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Among the major remaining challenges for generative adversarial networks (GANs) is the capacity to synthesize globally and locally coherent images with object shapes and textures indistinguishable from real images. To target this issue we propose an alternative U-Net based discriminator architecture, borrowing the insights from the segmentation literature. The proposed U-Net based architecture allows to provide detailed per-pixel feedback to the generator while maintaining the global coherence of synthesized images, by providing the global image feedback as well. Empowered by the per-pixel response of the discriminator, we further propose a per-pixel consistency regularization technique based on the CutMix data augmentation, encouraging the U-Net discriminator to focus more on semantic and structural changes between real and fake images. This improves the U-Net discriminator training, further enhancing the quality of generated samples. The novel discriminator improves over the state of the art in terms of the standard distribution and image quality metrics, enabling the generator to synthesize images with varying structure, appearance and levels of detail, maintaining global and local realism. Compared to the BigGAN baseline, we achieve an average improvement of 2.7 FID points across FFHQ, CelebA, and the proposed COCO-Animals dataset.
Diversified Arbitrary Style Transfer via Deep Feature Perturbation
Authors: Zhizhong Wang, Lei Zhao, Haibo Chen, Lihong Qiu, Qihang Mo, Sihuan Lin, Wei Xing, Dongming Lu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Image style transfer is an underdetermined problem, where a large number of solutions can satisfy the same constraint (the content and style). Although there have been some efforts to improve the diversity of style transfer by introducing an alternative diversity loss, they have restricted generalization, limited diversity and poor scalability. In this paper, we tackle these limitations and propose a simple yet effective method for diversified arbitrary style transfer. The key idea of our method is an operation called deep feature perturbation (DFP), which uses an orthogonal random noise matrix to perturb the deep image feature maps while keeping the original style information unchanged. Our DFP operation can be easily integrated into many existing WCT (whitening and coloring transform)-based methods, and empower them to generate diverse results for arbitrary styles. Experimental results demonstrate that this learning-free and universal method can greatly increase the diversity while maintaining the quality of stylization.
15 Keypoints Is All You Need
Authors: Michael Snower, Asim Kadav, Farley Lai, Hans Peter Graf
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Pose-tracking is an important problem that requires identifying unique human pose-instances and matching them temporally across different frames in a video. However, existing pose-tracking methods are unable to accurately model temporal relationships and require significant computation, often computing the tracks offline. We present an efficient multi-person pose-tracking method, KeyTrack that only relies on keypoint information without using any RGB or optical flow to locate and track human keypoints in real-time. KeyTrack is a top-down approach that learns spatio-temporal pose relationships by modeling the multi-person pose-tracking problem as a novel Pose Entailment task using a Transformer based architecture. Furthermore, KeyTrack uses a novel, parameter-free, keypoint refinement technique that improves the keypoint estimates used by the Transformers. We achieve state-of-the-art results on PoseTrack'17 and PoseTrack'18 benchmarks while using only a fraction of the computation used by most other methods for computing the tracking information.
LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
Authors: Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Ye Wang, Michael Jones, Anoop Cherian, Toshiaki Koike-Akino, Xiaoming Liu, Chen Feng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations nor predict whether landmarks are visible. In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities. We model these as mixed random variables and estimate them using a deep network trained using our proposed Location, Uncertainty, and Visibility Likelihood (LUVLi) loss. In addition, we release an entirely new labeling of a large face alignment dataset with over 19,000 face images in a full range of head poses. Each face is manually labeled with the ground-truth locations of 68 landmarks, with the additional information of whether each landmarks is visible, self-occluded (due to extreme head poses), or externally occluded. Not only does our joint estimation yield accurate estimates of the uncertainty of predicted landmark locations, but it also yields state-of-the-art estimates for the landmark locations themselves on mulitple standard face alignment datasets. Our method's estimates of the uncertainty of predicted landmark locations could be used to automatically identify input images on which face alignment fails, which can be critical for downstream tasks.
Learning to Cartoonize Using White-Box Cartoon Representations
Authors: Xinrui Wang, Jinze Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: This paper presents an approach for image cartoonization. By observing the cartoon painting behavior and consulting artists, we propose to separately identify three white-box representations from images: the surface representation that contains smooth surface of cartoon images, the structure representation that refers to the sparse color-blocks and flatten global content in the celluloid style workflow, and the texture representation that reflects high-frequency texture, contours and details in cartoon images. A Generative Adversarial Network (GAN) framework is used to learn the extracted representations and to cartoonize images. The learning objectives of our method are separately based on each extracted representations, making our framework controllable and adjustable. This enables our approach to meet artists' requirements in different styles and diverse use cases. Qualitative comparisons and quantitative analyses, as well as user studies, have been conducted to validate the effectiveness of this approach, and our method outperforms previous methods in all comparisons. Finally, the ablation study demonstrates the influence of each component in our framework.
PointAugment: An Auto-Augmentation Framework for Point Cloud Classification
Authors: Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We present PointAugment, a new auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network. Different from existing auto-augmentation methods for 2D images, PointAugment is sample-aware and takes an adversarial learning strategy to jointly optimize an augmentor network and a classifier network, such that the augmentor can learn to produce augmented samples that best fit the classifier. Moreover, we formulate a learnable point augmentation function with a shape-wise transformation and a point-wise displacement, and carefully design loss functions to adopt the augmented samples based on the learning progress of the classifier. Extensive experiments also confirm PointAugment's effectiveness and robustness to improve the performance of various networks on shape classification and retrival.
Siamese Box Adaptive Network for Visual Tracking
Authors: Zedu Chen, Bineng Zhong, Guorong Li, Shengping Zhang, Rongrong Ji
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Most of the existing trackers usually rely on either a multi-scale searching scheme or pre-defined anchor boxes to accurately estimate the scale and aspect ratio of a target. Unfortunately, they typically call for tedious and heuristic configurations. To address this issue, we propose a simple yet effective visual tracking framework (named Siamese Box Adaptive Network, SiamBAN) by exploiting the expressive power of the fully convolutional network (FCN). SiamBAN views the visual tracking problem as a parallel classification and regression problem, and thus directly classifies objects and regresses their bounding boxes in a unified FCN. The no-prior box design avoids hyper-parameters associated with the candidate boxes, making SiamBAN more flexible and general. Extensive experiments on visual tracking benchmarks including VOT2018, VOT2019, OTB100, NFS, UAV123, and LaSOT demonstrate that SiamBAN achieves state-of-the-art performance and runs at 40 FPS, confirming its effectiveness and efficiency. The code will be available at https://github.com/hqucv/siamban.
Interpretable and Accurate Fine-grained Recognition via Region Grouping
Authors: Zixuan Huang, Yin Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: We present an interpretable deep model for fine-grained visual recognition. At the core of our method lies the integration of region-based part discovery and attribution within a deep neural network. Our model is trained using image-level object labels, and provides an interpretation of its results via the segmentation of object parts and the identification of their contributions towards classification. To facilitate the learning of object parts without direct supervision, we explore a simple prior of the occurrence of object parts. We demonstrate that this prior, when combined with our region-based part discovery and attribution, leads to an interpretable model that remains highly accurate. Our model is evaluated on major fine-grained recognition datasets, including CUB-200, CelebA and iNaturalist. Our results compares favourably to state-of-the-art methods on classification tasks, and outperforms previous approaches on the localization of object parts.
Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer
Authors: Yerlan Idelbayev, Miguel A. Carreira-Perpinan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Neural net compression can be achieved by approximating each layer's weight matrix by a low-rank matrix. The real difficulty in doing this is not in training the resulting neural net (made up of one low-rank matrix per layer), but in determining what the optimal rank of each layer is--effectively, an architecture search problem with one hyperparameter per layer. We show that, with a suitable formulation, this problem is amenable to a mixed discrete-continuous optimization jointly over the ranks and over the matrix elements, and give a corresponding algorithm. We show that this indeed can select ranks much better than existing approaches, making low-rank compression much more attractive than previously thought. For example, we can make a VGG network faster than a ResNet and with nearly the same classification error.
There and Back Again: Revisiting Backpropagation Saliency Methods
Authors: Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Andrea Vedaldi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Saliency methods seek to explain the predictions of a model by producing an importance map across each input sample. A popular class of such methods is based on backpropagating a signal and analyzing the resulting gradient. Despite much research on such methods, relatively little work has been done to clarify the differences between such methods as well as the desiderata of these techniques. Thus, there is a need for rigorously understanding the relationships between different methods as well as their failure modes. In this work, we conduct a thorough analysis of backpropagation-based saliency methods and propose a single framework under which several such methods can be unified. As a result of our study, we make three additional contributions. First, we use our framework to propose NormGrad, a novel saliency method based on the spatial contribution of gradients of convolutional weights. Second, we combine saliency maps at different layers to test the ability of saliency methods to extract complementary information at different network levels (e.g. trading off spatial resolution and distinctiveness) and we explain why some methods fail at specific layers (e.g., Grad-CAM anywhere besides the last convolutional layer). Third, we introduce a class-sensitivity metric and a meta-learning inspired paradigm applicable to any saliency method for improving sensitivity to the output class being explained.
Learning Meta Face Recognition in Unseen Domains
Authors: Jianzhu Guo, Xiangyu Zhu, Chenxu Zhao, Dong Cao, Zhen Lei, Stan Z. Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: Face recognition systems are usually faced with unseen domains in real-world applications and show unsatisfactory performance due to their poor generalization. For example, a well-trained model on webface data cannot deal with the ID vs. Spot task in surveillance scenario. In this paper, we aim to learn a generalized model that can directly handle new unseen domains without any model updating. To this end, we propose a novel face recognition method via meta-learning named Meta Face Recognition (MFR). MFR synthesizes the source/target domain shift with a meta-optimization objective, which requires the model to learn effective representations not only on synthesized source domains but also on synthesized target domains. Specifically, we build domain-shift batches through a domain-level sampling strategy and get back-propagated gradients/meta-gradients on synthesized source/target domains by optimizing multi-domain distributions. The gradients and meta-gradients are further combined to update the model to improve generalization. Besides, we propose two benchmarks for generalized face recognition evaluation. Experiments on our benchmarks validate the generalization of our method compared to several baselines and other state-of-the-arts. The proposed benchmarks and code will be available at https://github.com/cleardusk/MFR.
MineGAN: Effective Knowledge Transfer From GANs to Target Domains With Few Images
Authors: Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, Joost van de Weijer
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (Main Conference Track), 2020

Abstract: One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for discriminative networks but has received significantly less attention for generative models. Given the often enormous effort required to train GANs, both computationally as well as in the dataset collection, the re-use of pretrained GANs is a desirable objective. We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods such as mode collapse and lack of flexibility. We perform experiments on several complex datasets using various GAN architectures (BigGAN, Progressive GAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs. Our code is available at: https://github.com/yaxingwang/MineGAN.