mtybilly/Instruct2DS huggingface dataset:
Utilizing Patch-level Category Activation Patterns for Multiple Class Novelty Detection
Authors: Vishal M. Patel Poojan Oza
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: For any recognition system, the ability to identify novel class samples during inference is an important aspect of the system’s robustness. This problem of detecting novel class samples during inference is commonly referred to as Multiple Class Novelty Detection. In this paper, we propose a novel method that makes deep convolutional neural networks robust to novel classes. Specifically, during training one branch performs traditional classification (referred to as global inference), and the other branch provides patch-level information to keep track of the class-specific activation patterns (referred to as local inference). Both global and local branch information are combined to train a novelty detection network, which is used during inference to identify novel classes. We evaluate the proposed method on four datasets (Caltech256, CUB-200, Stanford Dogs and FounderType-200) and show that the proposed method is able to identify novel class samples better compared to the other deep convolutional neural network based methods."
People as Scene Probes
Authors: Brian L. Curless, Steven M. Seitz Yifan Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: By analyzing the motion of people and other objects in a scene, we demonstrate how to infer depth, occlusion, lighting, and shadow information from video taken from a single camera viewpoint. This information is then used to composite new objects into the same scene with a high degree of automation and realism. In particular, when a user places a new object (2D cut-out) in the image, it is automatically rescaled, relit, occluded properly, and casts realistic shadows in the correct direction relative to the sun, and which conform properly to scene geometry. We demonstrate results (best viewed in supplementary video) on a range of scenes and compare to alternative methods for depth estimation and shadow compositing."
Mapping in a Cycle: Sinkhorn Regularized Unsupervised Learning for Point Cloud Shapes
Authors: Wenxi Liu, Zhiming Cui, Nenglun Chen, Wenping Wang Lei Yang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We propose an unsupervised learning framework with the pretext task of finding dense correspondences between point cloud shapes from the same category based on the cycle-consistency formulation. In order to learn discriminative pointwise features from point cloud data, we incorporate in the formulation a regularization term based on Sinkhorn normalization to enhance the learned pointwise mappings to be as bijective as possible. Besides, a random rigid transform of the source shape is introduced to form a triplet cycle to improve the model's robustness against perturbations. Comprehensive experiments demonstrate that the learned pointwise features through our framework benefits various point cloud analysis tasks, e.g. partial shape registration and keypoint transfer. We also show that the learned pointwise features can be leveraged by supervised methods to improve the part segmentation performance with either the full training dataset or just a small portion of it."
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
Authors: Aruni RoyChowdhury, Gopal Sharma, Evangelos Kalogerakis, Liangliang Cao, Erik Learned-Miller, Rui Wang, Subhransu Maji Matheus Gadelha
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years. Both of these problems, however, suffer from relatively small training sets, creating the need for statistically efficient methods to learn 3D shape representations. In this paper, we investigate the use of Approximate Convex Decompositions (ACD) as a self-supervisory signal for label-efficient learning of point cloud representations. We show that using ACD to approximate ground truth segmentation provides excellent self-supervision for learning 3D point cloud representations that are highly effective on downstream tasks. We report improvements over the state-of-the-art for unsupervised representation learning on the ModelNet40 shape classification dataset and significant gains in few-shot part segmentation on the ShapeNetPart dataset. Our source code is publicly available."
TexMesh: Reconstructing Detailed Human Texture and Geometry from RGB-D Video
Authors: Christoph Lassner, Tony Tung, Carsten Stoll, Srinivasa G. Narasimhan, Minh Vo Tiancheng Zhi
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We present TexMesh, a novel approach to reconstruct detailed human meshes with high-resolution full-body texture from RGB-D video. TexMesh enables high quality free-viewpoint rendering of humans. Given the RGB frames, the captured environment map, and the coarse per-frame human mesh from RGB-D tracking, our method reconstructs spatiotemporally consistent and detailed per-frame meshes along with a high-resolution albedo texture. By using the incident illumination we are able to accurately estimate local surface geometry and albedo, which allows us to further use photometric constraints to adapt a synthetically trained model to real-world sequences in a self-supervised manner for detailed surface geometry and high-resolution texture estimation. In practice, we train our models on a short example sequence for self-adaptation and the model runs at interactive framerate afterwards. We validate TexMesh on synthetic and real-world data, and show it outperforms the state of art quantitatively and qualitatively."
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost
Authors: Zizhao Zhang, Guo Yu, Sercan . Arık, Larry S. Davis, Tomas Pfister Mingfei Gao
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Active learning (AL) combines data labeling and model training to minimize the labeling cost by prioritizing the selection of high value data that can best improve model performance. In pool-based active learning, accessible unlabeled data are not used for model training in most conventional methods. Here, we propose to unify unlabeled sample selection and model training towards minimizing labeling cost, and make two contributions towards that end. First, we exploit both labeled and unlabeled data using semi-supervised learning (SSL) to distill information from unlabeled data during the training stage. Second, we propose a consistency-based sample selection metric that is coherent with the training objective such that the selected samples are effective at improving model performance. We conduct extensive experiments on image classification tasks. The experimental results on CIFAR-10, CIFAR-100 and ImageNet demonstrate the superior performance of our proposed method with limited labeled data, compared to the existing methods and the alternative AL and SSL combinations. Additionally, we also study an important yet under-explored problem -- ``When can we start learning-based AL selection?"". We propose a measure that is empirically correlated with the AL target loss and is potentially useful for determining the proper starting point of learning-based AL methods."
Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation
Authors: Xiao Sun, Hongyang Li, Jingdong Wang, Stephen Lin Fangyun Wei
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Instance Segmentation and Pose Estimation","A recent approach for object detection and human pose estimation is to regress bounding boxes or human keypoints from a central point on the object or person. While this center-point regression is simple and efficient, we argue that the image features extracted at a central point contain limited information for predicting distant keypoints or bounding box boundaries, due to object deformation and scale/orientation variation. To facilitate inference, we propose to instead perform regression from a set of points placed at more advantageous positions. This point set is arranged to reflect a good initialization for the given task, such as modes in the training data for pose estimation, which lie closer to the ground truth than the central point and provide more informative features for regression. As the utility of a point set depends on how well its scale, aspect ratio and rotation matches the target, we adopt the anchor box technique of sampling these transformations to generate additional point-set candidates. We apply this proposed framework, called Point-Set Anchors, to object detection, instance segmentation, and human pose estimation. Our results show that this general-purpose approach can achieve performance competitive with state-of-the-art methods for each of these tasks."
Modeling 3D Shapes by Reinforcement Learning
Authors: Tingxiang Fan, Wenping Wang, Matthias Nießner Cheng Lin
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We explore how to enable machines to model 3D shapes like human modelers using deep reinforcement learning (RL). In 3D modeling software like Maya, a modeler usually creates a mesh model in two steps: (1) approximating the shape using a set of primitives; (2) editing the meshes of the primitives to create detailed geometry. Inspired by such artist-based modeling, we propose a two-step neural framework based on RL to learn 3D modeling policies. By taking actions and collecting rewards in an interactive environment, the agents first learn to parse a target shape into primitives and then to edit the geometry. To effectively train the modeling agents, we introduce a novel training algorithm that combines heuristic policy, imitation learning and reinforcement learning. Our experiments show that the agents can learn good policies to produce regular and structure-aware mesh models, which demonstrates the feasibility and effectiveness of the proposed RL framework."
LST-Net: Learning a Convolutional Neural Network with a Learnable Sparse Transform
Authors: Kun Wang, Shuai Li, Xiangchu Feng, Lei Zhang Lida Li
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: The 2D convolutional (Conv2d) layer is the fundamental element to a deep convolutional neural network (CNN). Despite the great success of CNN, the conventional Conv2d is still limited in effectively reducing the spatial and channel-wise redundancy of features. In this paper, we propose to mitigate this issue by learning a CNN with a learnable sparse transform (LST), which converts the input features into a more compact and sparser domain so that the spatial and channel-wise redundancy can be more effectively reduced. The proposed LST can be efficiently implemented with existing CNN modules, such as point-wise and depth-wise separable convolutions, and it is portable to existing CNN architectures for seamless training and inference.We further present a hybrid soft thresholding and ReLU (ST-ReLU) activation scheme, making the trained network, namely LST-Net, more robust to image corruptions at the inference stage. Extensive experiments on CIFAR-10/100, ImageNet, ImageNet-C and Places365-Standard datasets validated that the proposed LST-Net can obtain even higher accuracy than its counterpart networks with fewer parameters and less overhead."
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision
Authors: Ehsan Abbasnedjad, Anton van den Hengel Damien Teney
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: One of the primary challenges limiting the practical application of deep learning is its susceptibility to learning spurious correlations in the data, rather than capturing the data-generating mechanisms of the task of interest. The resulting failure to generalise cannot be addressed by simply using more data from the same distribution. We propose an auxiliary training objective that improves the generalization capabilities of neural networks by leveraging an overlooked supervisory signal found in existing datasets. We demonstrate that pairs of minimally-different examples with different labels, a.k.a counterfactual examples, provide a signal indicative of the underlying causal structure of the task. We show that such pairs can be identified in a number of existing datasets for a range of tasks in vision (visual question answering, multi-label image classification) and natural language processing (sentiment analysis, natural language inference). We propose a training objective that orients the gradient on a model's decision boundary to align with pairwise relations in the input domain. Models trained with this technique demonstrate improved performance on out-of-distribution test sets."
CN: Channel Normalization For Point Cloud Recognition
Authors: Yanan Sun, Shu Liu, Xiaojuan Qi, Jiaya Jia Zetong Yang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: In 3D recognition, to fuse multi-scale structure information, existing methods apply hierarchical frameworks stacked by multiple fusion layers for integrating current relative locations with structure information from the previous level. In this paper, we deeply analyze these point recognition frameworks and present a factor, called difference ratio, to measure the influence of structure information among different levels on the final representation. We discover that structure information in deeper layers is overwhelmed by information in shallower layers in generating the final features, which prevents the model from understanding the point cloud in a global view. Inspired by this observation, we propose a novel channel normalization scheme to balance structure information among different layers and avoid excessive accumulation of shallow information, which benefits the model in exploiting and integrating multilayer structure information. We evaluate our channel normalization in several core 3D recognition tasks including classification, segmentation and detection. Experimental results show that our channel normalization further boosts the performance of state-of-the-art methods effectively."
Rethinking the Defocus Blur Detection Problem and A Real-Time Deep DBD Model
Authors: Junchi Yan Ning Zhang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Defocus blur detection (DBD) is a classical low level vision task. It has recently attracted attention focusing on designing complex convolutional neural networks (CNN) which make full use of both low level features and high level semantic information. The heavy networks used in these methods lead to low processing speed, resulting difficulty in applying to real-time applications. In this work, we propose novel perspectives on the DBD problem and design convenient approach to build a real-time cost-effective DBD model. First, we observe that the semantic information does not always relate to and sometimes mislead the blur detection. We start from the essential characteristics of the DBD problem and propose a data augmentation method accordingly to destroy the semantic information and enforce the model to learn image blur related features rather than the semantic features. A novel self-supervision training objective is proposed to enhance the model training consistency and stability. Second, by rethinking the relationship between defocus blur detection and salience detection, we identify two previously ignored but common scenarios, based on which we design a hard mining strategy to enhance the DBD model. By using the proposed techniques, our model that uses a slightly modified U-Net as backbone, improves the processing speed by more than 3 times and performs competitively against state of the art methods. Ablation study is also conducted to verify the effectiveness of each part of our proposed methods."
AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning
Authors: Liangliang Shi, Junchi Yan, Hongyuan Zha Jianchao Zhu
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: This paper proposes new ways of sample mixing by thinking of the process as generation of barycenter in a metric space for data augmentation. First, we present an optimal-transport-based mixup technique to generate Wasserstein barycenter which works well on images with clean background and is empirically shown complementary to existing mixup methods. Then we generalize mixup to an AutoMix technique by using a learnable network to fit barycenter in a cooperative way between the classifier (a.k.a. discriminator) and generator networks. Experimental results on both multi-class and multi-label prediction tasks show the efficacy of our approach, which is also verified in the presence of unseen categories (open set) and noise."
Scene Text Image Super-resolution in the wild
Authors: Enze Xie, Xuebo Liu, Wenhai Wang, Ding Liang, Chunhua Shen, Xiang Bai Wenjia Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Low-resolution text images are often seen in natural scenes such as documents captured by mobile phones. Recognizing low-resolution text images is challenging because they lose detailed content information, leading to poor recognition accuracy. An intuitive solution is to introduce super-resolution (SR) techniques as pre-processing. However, previous single image super-resolution (SISR) methods are trained on synthetic low resolution images (e.g.Bicubic down-sampling), which is simple and not suitable for real low-resolution text recognition. To this end, we propose a real scene text SR dataset, termed TextZoom. It contains paired real low-resolution and high-resolution images which are captured by cameras with different focal length in the wild. It is more authentic and challenging than synthetic data, as shown in Fig. 1. We argue improving the recognition accuracy is the ultimate goal for SceneText SR. In this purpose, a new Text Super-Resolution Network, termed TSRN, with three novel modules is developed. (1) A sequential residual block is proposed to extract the sequential information of the text images. (2) A boundary-aware loss is designed to sharpen the character boundaries.(3) A central alignment module is proposed to relieve the misalignment problem in TextZoom. Extensive experiments on TextZoom demonstrate that our TSRN largely improves the recognition accuracy by over 13%of CRNN, and by nearly 9.0% of ASTER and MORAN compared to synthetic SR data. Furthermore, our TSRN clearly outperforms 7 state-of-the-art SR methods in boosting the recognition accuracy of LR images in TextZoom. For example, it outperforms LapSRN by over 5% and 8%on the recognition accuracy of ASTER and CRNN. Our results suggest that low-resolution text recognition in the wild is far from being solved, thus more research effort is needed"
Coupling Explicit and Implicit Surface Representations for Generative 3D Modeling
Authors: Matthew Fisher, Noam Aigerman, Vladimir G. Kim Omid Poursaeed
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We propose a novel neural architecture for representing 3D surfaces, which harnesses two complementary shape representations: (i) an explicit representation via an atlas, i.e., embeddings of 2D domains into 3D; (ii) an implicit-function representation, i.e., a scalar function over the 3D volume, with its levels denoting surfaces. We make these two representations synergistic by introducing novel consistency losses that ensure that the surface created from the atlas aligns with the level-set of the implicit function. Our hybrid architecture outputs results which are superior to the output of the two equivalent single-representation networks, yielding smoother explicit surfaces with more accurate normals, and a more accurate implicit occupancy function. Additionally, our surface reconstruction step can directly leverage the explicit atlas-based representation. This process is computationally efficient, and can be directly used by differentiable rasterizers, enabling training our hybrid representation with image-based losses."
Learning Disentangled Representations with Latent Variation Predictability
Authors: Chang Xu, Dacheng Tao Xinqi Zhu
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Latent traversal is a popular approach to visualize the disentangled latent representations. Given a bunch of variations in a single unit of the latent representation, it is expected that there is a change in a single factor of variation of the data while others are fixed. However, this impressive experimental observation is rarely explicitly encoded in the objective function of learning disentangled representations. This paper defines the variation predictability of latent disentangled representations. Given image pairs generated by latent codes varying in a single dimension, this varied dimension could be closely correlated with these image pairs if the representation is well disentangled. Within an adversarial generation process, we encourage variation predictability by maximizing the mutual information between latent variations and corresponding image pairs. We further develop an evaluation metric that does not rely on the ground-truth generative factors to measure the disentanglement of latent representations. The proposed variation predictability is a general constraint that is applicable to the VAE and GAN frameworks for boosting disentanglement of latent representations. Experiments show that the proposed variation predictability correlates well with existing ground-truth-required metrics and the proposed algorithm is effective for disentanglement learning."
Deep Space-Time Video Upsampling Networks
Authors: Younghyun Jo, Seoung Wug Oh, Peter Vajda, Seon Joo Kim Jaeyeon Kang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Video super-resolution (VSR) and frame interpolation (FI) are traditional computer vision problems, and the performance have been improving by incorporating deep learning recently. In this paper, we investigate the problem of jointly upsampling videos both in space and time, which is becoming more important with advances in display systems. One solution for this is to run VSR and FI, one by one, independently. This is highly inefficient as heavy deep neural networks (DNN) are involved in each solution. To this end, we propose an end-to-end DNN framework for the space-time video upsampling by efficiently merging VSR and FI into a joint framework. In our framework, a novel weighting scheme is proposed to fuse input frames effectively without explicit motion compensation for efficient processing of videos. The results show better results both quantitatively and qualitatively, while reducing the computation time (x7 faster) and the number of parameters (30%) compared to baselines. Our source code is available at https://github.com/JaeYeonKang/STVUN-Pytorch."
Large-Scale Few-Shot Learning via Multi-Modal Knowledge Discovery
Authors: Jun Yue, Jianzhuang Liu, Qi Tian, Meng Wang Shuo Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Large-scale few-shot learning aims at identifying hundreds of novel object categories where each category has only a few samples. It is a challenging problem since (1) the identifying process is susceptible to over-fitting with limited samples of an object, and (2) the sample imbalance between a base (known knowledge) category and a novel category is easy to bias the recognition results. To solve these problems, we propose a method based on multi-modal knowledge discovery. First, we use the visual knowledge to help the feature extractors focus on different visual parts. Second, we design a classifier to learn the distribution over all categories. In the second stage, we develop three schemes to minimize the prediction error and balance the training procedure: (1) Hard labels are used to provide precise supervision. (2) Semantic textual knowledge is utilized as weak supervision to find the potential relations between the novel and the base categories. (3) An imbalance control is presented from the data distribution to alleviate the recognition bias towards the base categories. We apply our method on three benchmark datasets, and it achieves state-of-the-art performances in all the experiments."
Fast Video Object Segmentation using the Global Context Module
Authors: Zhuoran Shen, Ying Shan Yu Li
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We developed a real-time, high-quality semi-supervised video object segmentation algorithm. Its accuracy is on par with the most accurate, time-consuming online-learning model, while its speed is similar to the fastest template-matching method with sub-optimal accuracy. The core component of the model is a novel global context module that effectively summarizes and propagates information through the entire video. Compared to previous approaches that only use one frame or a few frames to guide the segmentation of the current frame, the global context module uses all past frames. Unlike the previous state-of-the-art space-time memory network that caches a memory at each spatio-temporal position, the global context module uses a fixed-size feature representation. Therefore, it uses constant memory regardless of the video length and costs substantially less memory and computation. With the novel module, our model achieves top performance on standard benchmarks at a real-time speed."
Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos
Authors: Chen Sun, Arsha Nagrani, Cordelia Schmid Anurag Arnab
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Despite the recent advances in video classification, progress in spatio-temporal action recognition has lagged behind. A major contributing factor has been the prohibitive cost of annotating videos frame-by-frame. In this paper, we present a spatio-temporal action recognition model that is trained with only video-level labels, which are significantly easier to annotate. Our method leverages per-frame person detectors which have been trained on large image datasets within a Multiple Instance Learning framework. We show how we can apply our method in cases where the standard Multiple Instance Learning assumption, that each bag contains at least one instance with the specified label, is invalid using a novel probabilistic variant of MIL where we estimate the uncertainty of each prediction. Furthermore, we report the first weakly-supervised results on the AVA dataset and state-of-the-art results among weakly-supervised methods on UCF101-24."
Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification
Authors: Cordelia Schmid, Julien Mairal Nikita Dvornik
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples. In this work, we propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. First, we obtain a multi-domain representation by training a set of semantically different feature extractors. Then, given a few-shot learning task, we use our multi-domain feature bank to automatically select the most relevant representations. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training, which leads to state-of-the-art results on MetaDataset and improved accuracy on mini-ImageNet."
MessyTable: Instance Association in Multiple Camera Views
Authors: Junzhe Zhang, Daxuan Ren, Cunjun Yu, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Chen Change Loy Zhongang Cai
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We present an interesting and challenging dataset that features a large number of scenes with messy tables captured from multiple camera views. Each scene in this dataset is highly complex, containing multiple object instances that could be identical, stacked and occluded by other instances. The key challenge is to associate all instances given the RGB image of all views. The seemingly simple task surprisingly fails many popular methods or heuristics that we assume good performance in object association. The dataset challenges existing methods in mining subtle appearance differences, reasoning based on contexts, and fusing appearance with geometric cues for establishing an association. We report interesting findings with some popular baselines, and discuss how this dataset could help inspire new problems and catalyse more robust formulations to tackle real-world instance association problems.ootnote{project page: extcolor{magenta}{\url{https://caizhongang.github.io/projects/MessyTable/}}.}"
A Unified Framework for Shot Type Classification Based on Subject Centric Lens
Authors: Jiaze Wang, Linning Xu, Xuekun Jiang, Qingqiu Huang, Bolei Zhou, Dahua Lin Anyi Rao
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: In film making, shot has a profound influence on how the story is delivered and how the audiences are echoed. As different scale and movement types of shots can express different emotions and contents, recognizing shots and their attributes is important to the understanding of movies as well as general videos. Classifying shot type is challenging due to the additional information required beyond the video content, such as the spatial composition of a frame and the video camera movement. To address these issues, we propose a learning framework Subject Guidance Network (SGNet) for shot type recognition. SGNet separates the subject and background of a shot into two streams, serving as maps to guide scale and movement type classification respectively. To facilitate shot type analysis and model evaluations, we build a large-scale dataset MovieShots, which contains 46K shots from 7K movie trailers with annotations of their scale and movement types. Experiments show that our framework is able to recognize these two attributes of shot accurately, outperforming all the previous methods. "
BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues
Authors: Gül Varol, Liliane Momeni, Triantafyllos Afouras, Joon Son Chung, Neil Fox, Andrew Zisserman Samuel Albanie
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Recent progress in fine-grained gesture and action classification, and machine translation, point to the possibility of automated sign language recognition becoming a reality. A key stumbling block in making progress towards this goal is a lack of appropriate training data, stemming from the high complexity of sign annotation and a limited supply of qualified annotators. In this work, we introduce a new scalable approach to data collection for sign recognition in continuous videos. We make use of weakly-aligned subtitles for broadcast footage together with a keyword spotting method to automatically localise sign-instances for a vocabulary of 1,000 signs in 1,000 hours of video. We make the following contributions: (1) We show how to use mouthing cues from signers to obtain high-quality annotations from video data - the result is the BSL-1K dataset, a collection of British Sign Language (BSL) signs of unprecedented scale; (2) We show that we can use BSL-1K to train strong sign recognition models for co-articulated signs in BSL and that these models additionally form excellent pretraining for other sign languages and benchmarks---we exceed the state of the art on both the MSASL and WLASL benchmarks. Finally, (3) we propose new large-scale evaluation sets for the tasks of sign recognition and sign spotting and provide baselines which we hope will serve to stimulate research in this area."
HTML: A Parametric Hand Texture Model for 3D Hand Reconstruction and Personalization
Authors: Jiayi Wang, Franziska Mueller, Florian Bernard, Vladislav Golyanik, Christian Theobalt Neng Qian
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: 3D hand reconstruction from images is a widely-studied problem in computer vision and graphics, and has a particularly high relevance for virtual and augmented reality. Although several 3D hand reconstruction approaches leverage hand models as a strong prior to resolve ambiguities and achieve more robust results, most existing models account only for the hand shape and poses and do not model the texture. To fill this gap, in this work we present HTML, the first parametric texture model of human hands. Our model spans several dimensions of hand appearance variability (e.g. related to gender, ethnicity, or age) and only requires a commodity camera for data acquisition. Experimentally, we demonstrate that our appearance model can be used to tackle a range of challenging problems such as 3D hand reconstruction from a single monocular image. Furthermore, our appearance model can be used to define a neural rendering layer that enables training with a self-supervised photometric loss. We make our model publicly available."
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions
Authors: Jingwei Zhang, Liang Zheng, Yixuan Liu, Yifan Sun, Yali Li, Shengjin Wang Zhongdao Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering. A potential drawback of using pseudo labels is that errors may accumulate and it is challenging to estimate the number of pseudo IDs. We introduce a different unsupervised method that allows us to learn pedestrian embeddings from raw videos, without resorting to pseudo labels. The goal is to construct a self-supervised pretext task that matches the person re-ID objective. Inspired by the mph{data association} concept in multi-object tracking, we propose the extbf{Cyc}le extbf{As}sociation ( extbf{CycAs}) task: after performing data association between a pair of video frames forward and then backward, a pedestrian instance is supposed to be associated to itself. To fulfill this goal, the model must learn a meaningful representation that can well describe correspondences between instances in frame pairs. We adapt the discrete association process to a differentiable form, such that end-to-end training becomes feasible. Experiments are conducted in two aspects: We first compare our method with existing unsupervised re-ID methods on seven benchmarks and demonstrate CycAs' superiority. Then, to further validate the practical value of CycAs in real-world applications, we perform training on self-collected videos and report promising performance on the standard test sets."
Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructions
Authors: Zhe Lin, Jianming Zhang, Handong Zhao, Quan Tran, Xiaogang Wang, Hongsheng Li Xihui Liu
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We propose a novel algorithm, named Open-Edit, which is the first attempt on open-domain image manipulation with open-vocabulary instructions. It is a challenging task considering the large variation of image domains and the lack of training supervision. Our approach takes advantage of the unified visual-semantic embedding space pretrained on a general image-caption dataset, and manipulates the embedded visual features by applying text-guided vector arithmetic on the image feature maps. A structure-preserving image decoder then generates the manipulated images from the manipulated feature maps. We further propose an on-the-fly sample-specific optimization approach with cycle-consistency constraints to regularize the manipulated images and force them to preserve details of the source images. Our approach shows promising results in manipulating open-vocabulary color, texture, and high-level attributes for various scenarios of open-domain images."
Towards Real-Time Multi-Object Tracking
Authors: Liang Zheng, Yixuan Liu, Yali Li, Shengjin Wang Zhongdao Wang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Modern multiple object tracking (MOT) systems usually follow the tracking-by-detection paradigm. It has 1) a detection model for target localization and 2) an appearance embedding model for data association. Having the two models separately executed might lead to efficiency problems, as the running time is simply a sum of the two steps without investigating potential structures that can be shared between them. Existing research efforts on real-time MOT usually focus on the association step, so they are essentially real-time association methods but not real-time MOT system. In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model. Specifically, we incorporate the appearance embedding model into a single-shot detector, such that the model can simultaneously output detections and the corresponding embeddings. We further propose a simple and fast association method that works in conjunction with the joint model. In both components the computation cost is significantly reduced compared with former MOT systems, resulting in a neat and fast baseline for future follow-ups on real-time MOT algorithm design. To our knowledge, this work reports the first (near) real-time MOT system, with a running speed of 22 to 40 FPS depending on the input resolution. Meanwhile, its tracking accuracy is comparable to the state-of-the-art trackers embodying separate detection and embedding (SDE) learning ($64.4\%$ MOTA s $66.1\%$ MOTA on MOT-16 challenge). Code and models will be available at \url{https://github.com/Zhongdao/Towards-Realtime-MOT}."
A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation
Authors: Yunbo Wang, Dapeng Hu, Ran He, Jiashi Feng Jian Liang
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: This work addresses the unsupervised domain adaptation problem, especially in the case of class labels in the target domain being only a subset of those in the source domain. Such a partial transfer setting is realistic but challenging and existing methods always suffer from two key problems, negative transfer and uncertainty propagation. In this paper, we build on domain adversarial learning and propose a novel domain adaptation method BA$^3$US with two new techniques termed Balanced Adversarial Alignment (BAA) and Adaptive Uncertainty Suppression (AUS), respectively. On one hand, negative transfer results in misclassification of target samples to the classes only present in the source domain. To address this issue, BAA pursues the balance between label distributions across domains in a fairly simple manner. Specifically, it randomly leverages a few source samples to augment the smaller target domain during domain alignment so that classes in different domains are symmetric. On the other hand, a source sample would be denoted as uncertain if there is an incorrect class that has a relatively high prediction score, and such uncertainty easily propagates to unlabeled target data around it during alignment, which severely deteriorates adaptation performance. Thus we present AUS that emphasizes uncertain samples and exploits an adaptive weighted complement entropy objective to encourage incorrect classes to have uniform and low prediction scores. Experimental results on multiple benchmarks demonstrate our BA$^3$US surpasses state-of-the-arts for partial domain adaptation tasks. Code is available at \url{https://github.com/tim-learn/BA3US}."
Unsupervised Deep Metric Learning with Transformed Attention Consistency and Contrastive Clustering Loss
Authors: Shichao Kan, Zhihai He Yang Li
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Existing approaches for unsupervised metric learning focus on exploring self-supervision information within the input image itself. We observe that, when analyzing images, human eyes often compare images against each other instead of examining images individually. In addition, they often pay attention to certain keypoints, image regions, or objects which are discriminative between image classes but highly consistent within classes. Even if the image is being transformed, the attention pattern will be consistent. Motivated by this observation, we develop a new approach to unsupervised deep metric learning where the network is learned based on self-supervision information across images instead of within one single image. To characterize the consistent pattern of human attention during image comparisons, we introduce the idea of transformed attention consistency. It assumes that visually similar images, even undergoing different image transforms, should share the same consistent visual attention map. This consistency leads to a pairwise self-supervision loss, allowing us to learn a Siamese deep neural network to encode and compare images against their transformed or matched pairs. To further enhance the inter-class discriminative power of the feature generated by this network, we adapt the concept of triplet loss from supervised metric learning to our unsupervised case and introduce the contrastive clustering loss. Our extensive experimental results on benchmark datasets demonstrate that our proposed method outperforms current state-of-the-art methods for unsupervised metric learning by a large margin."
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos
Authors: Sabarinath Mahadevan, Aljosa Osep, Laura Leal-Taixé, Bastian Leibe Ali Athar
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Existing methods for instance segmentation in videos typically involve multi-stage pipelines that follow the tracking-by detection paradigm and model a video clip as a sequence of images. Multiple networks are used to detect objects in individual frames, and then associate these detections over time. Hence, these methods are often non-end-to-end trainable and highly tailored to specific tasks. In this paper, we propose a different approach that is well-suited to a variety of tasks involving instance segmentation in videos. In particular, we model a video clip as a single 3D spatio-temporal volume, and propose a novel approach that segments and tracks instances across space and time in a single stage. Our problem formulation is centered around the idea of spatio-temporal embeddings which are trained to cluster pixels belonging to a specific object instance over an entire video clip. To this end, we introduce (i) novel mixing functions that enhance the feature representation of spatio-temporal embeddings, and (ii) a single-stage, proposal-free network that can reason about temporal context. Our network is trained end-to-end to learn spatio-temporal embeddings as well as parameters required to cluster these embeddings, thus simplifying inference. Our method achieves state-of-the-art results across multiple datasets and tasks."
Hierarchical Style-based Networks for Motion Synthesis
Authors: Huazhe Xu, Bingbing Ni, Xiaokang Yang, Xiaolong Wang, Trevor Darrell Jingwei Xu
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Generating diverse and natural behaviors is one of the long-standing goals for creating intelligent characters in the animated world. In this paper, we propose an unsupervised method for generating long-range, diverse and plausible behaviors to achieve a specific goal location. Our proposed method learns to model the motion of human by decomposing a long-range generation task in a hierarchical manner. Given the starting and ending states, a memory bank is used to retrieve motion references as source material for short-range clip generation. We first propose to explicitly disentangle the provided motion material into style and content counterparts via bi-linear transformation modelling, where diverse synthesis is achieved by free-form combination of these two components. The short-range clips are then connected to form a long-range motion sequence. Without ground truth annotation, we propose a parameterized bi-directional interpolation scheme to guarantee the physical validity and visual naturalness of generated results. On large-scale skeleton dataset, we show that the proposed method is able to synthesise long-range, diverse and plausible motion, which is also generalizable to unseen motion data during testing. Moreover, we demonstrate the generated sequences are useful as subgoals for actual physical execution in the animated world. Please refer to our project page~ootnote{https://sites.google.com/view/hsnms/home} for more synthesised results."
Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop
Authors: Oliver Boyne, James Charles, Andrew Fitzgibbon, Roberto Cipolla Benjamin Biggs
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: We introduce an automatic, end-to-end method for recovering the 3D pose and shape of dogs from monocular internet images. The large variation in shape between dog breeds, and significant occlusion and low quality of internet images makes this a challenging problem. We learn a richer prior over shapes than previous work, which helps regularize parameter estimation. We demonstrate results on the Stanford Dog Dataset, an ``in-the-wild'' dataset of 20,580 dog images for which we have collected 2D joint and silhouette annotations to split for training and evaluation. In order to capture the large shape variety of dogs, we show that the natural variation in the 2D dataset is enough to learn a detailed 3D prior through expectation maximisation (EM). As a by-product of training, we generate a new parameterised model (including limb scaling) SMBLD which we release alongside the annotation dataset to the research community."
Learning to Count in the Crowd from Limited Labeled Data
Authors: Rajeev Yasarla, Deepak Sam Babu, R. Venkatesh Babu, Vishal M. Patel Vishwanath A. Sindagi
Published in: European Conference on Computer Vision (ECCV), 2020 (Main Conference Track), 2020

Abstract: Recent crowd counting approaches have achieved excellent performance. However, they are essentially based on fully supervised paradigm and require large number of annotated samples. Obtaining annotations is an expensive and labour-intensive process. In this work, we focus on reducing the annotation efforts by learning to count in the crowd from limited number of labeled samples while leveraging a large pool of unlabeled data. Specifically, we propose a Gaussian Process-based iterative learning mechanism that involves estimation of pseudo-ground truth for the unlabeled data, which is then used as supervision for training the network. The proposed method is shown to be effective under the reduced data (semi-supervised) settings for several datasets like ShanghaiTech, UCF-QNRF, WorldExpo, UCSD, etc. Furthermore, we demonstrate that the proposed method can be leveraged to enable the network in learning to count from synthetic dataset while being able to generalize better to real-world datasets (synthetic-to-real transfer). "