GenAI4ELab/papercli-papers huggingface dataset:
CSCL: Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation
Authors: Jiahua Dong; Yang Cong; Gan Sun; Yuyang Liu; Xiaowei Xu
Venue: ECCV 2020 (main)

Abstract: Unsupervised domain adaptation without consuming annotation process for unlabeled target data attracts appealing interests in semantic segmentation. However, 1) existing methods neglect that not all semantic representations across domains are transferable, which cripples domain-wise transfer with untransferable knowledge; 2) they fail to narrow category-wise distribution shift due to category-agnostic feature alignment. To address above challenges, we develop a new Critical Semantic-Consistent Learning (CSCL) model, which mitigates the discrepancy of both domain-wise and category-wise distributions. Specifically, a critical transfer based adversarial framework is designed to highlight transferable domain-wise knowledge while neglecting untransferable knowledge. Transferability-critic guides transferability-quantizer to maximize positive transfer gain under reinforcement learning manner, although negative transfer of untransferable knowledge occurs. Meanwhile, with the help of confidence-guided pseudo labels generator of target samples, a symmetric soft divergence loss is presented to explore inter-class relationships and facilitate category-wise distribution alignment. Experiments on several datasets demonstrate the superiority of our model."
Prototype Mixture Models for Few-shot Semantic Segmentation
Authors: Boyu Yang; Chang Liu; Bohao Li; Jianbin Jiao; Qixiang Ye
Venue: ECCV 2020 (main)

Abstract: Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose. Using a single prototype acquired directly from the support image to segment the query image causes semantic ambiguity. In this paper, we propose prototype mixture models (PMMs), which correlate diverse image regions with multiple prototypes to enforce the prototype-based semantic representation. Estimated by an Expectation-Maximization algorithm, PMMs incorporate rich channel-wised and spatial semantics from limited support images. Utilized as representations as well as classifiers, PMMs fully leverage the semantics to activate objects in the query image while depressing background regions in a duplex manner. Extensive experiments on Pascal VOC and MS-COCO datasets show that PMMs significantly improve upon state-of-the-arts. Particularly, PMMs improve 5-shot segmentation performance on MS-COCO by up to 5.82% with only a moderate cost for model size and inference speed."
Webly Supervised Image Classification with Self-Contained Confidence
Authors: Jingkang Yang; Litong Feng; Weirong Chen; Xiaopeng Yan; Huabin Zheng; Ping Luo; Wayne Zhang
Venue: ECCV 2020 (main)

Abstract: This paper focuses on webly supervised learning (WSL), where datasets are built by crawling samples from the Internet and adopting search queries directly as their web labels. Although WSL benefits from fast and low-cost data expansion, noisy web labels prevent models from reliable predictions. To mitigate this problem, self-labeled supervised loss $\mathcal{L}_s$ is utilized together with webly supervised loss $\mathcal{L}_w$ in recent works. $\mathcal{L}_s$ relies on machine labels predicted by the model itself. Since whether web labels or machine labels being correct is usually on a case-by-case basis for web samples, it is desirable to adjust balance between $\mathcal{L}_s$ and $\mathcal{L}_w$ at a sample-level. Inspired by DNNs' ability on confidence prediction, we introduce self-contained confidence (SCC) by adapting model uncertainty for WSL setting and use it to sample-wisely balance $\mathcal{L}_s$ and $\mathcal{L}_w$. Thus, a simple yet effective WSL framework is proposed. A series of SCC-friendly approaches are investigated, among which our proposed graph-enhanced mixup stands out as the most effective approach to provide high-quality confidence to boost our framework. The proposed WSL framework achieved state-of-the-art results on two large-scale WSL datasets of WebVision-1000 and Food101-N."
Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization
Authors: Haibao Yu; Qi Han; Jianbo Li; Jianping Shi; Guangliang Cheng; Bin Fan
Venue: ECCV 2020 (main)

Abstract: Emergent hardwares can support mixed precision CNN models inference that assign different bitwidths for different layers. Learning to find an optimal mixed precision model that can preserve accuracy and satisfy the specific constraints on model size and computation is extremely challenge due to the difficult in training a mixed precision model and the huge space of all possible bit quantizations. In this paper, we propose a novel soft Barrier Penalty based NAS (BP-NAS) for mixed precision quantization, which ensures all the searched models are inside the valid domain defined by the complexity constraint, thus could return an optimal model under the given constraint by conducting search only one time. The proposed soft Barrier Penalty is differentiable and can impose very large losses to those models outside the valid domain while almost no punishment for models inside the valid domain, thus constraining the search only in the feasible domain. In addition, a differentiable Prob-1 regularizer is proposed to ensure learning with NAS is reasonable. A distribution reshaping training strategy is also used to make training more stable. BP-NAS sets new state of the arts on both classification (Cifar-10, ImageNet) and detection (COCO), surpassing all the efficient mixed precision methods designed manually and automatically. Particularly, BP-NAS achieves higher mAP (up to 2.7% mAP improvement) together with lower bit computation cost compared with the existing best mixed precision model on COCO detection."
Monocular 3D Object Detection via Feature Domain Adaptation
Authors: Xiaoqing Ye; Liang Du; Yifeng Shi; Yingying Li; Xiao Tan; Jianfeng Feng; Errui Ding; Shilei Wen
Venue: ECCV 2020 (main)

Abstract: Monocular 3D object detection is a challenging task due to unreliable depth, resulting in a distinct performance gap between monocular and LiDAR-based approaches. In this paper, we propose a novel domain adaptation based monocular 3D object detection framework named DA-3Ddet, which adapts the feature from unsound image-based pseudo-LiDAR domain to the accurate real LiDAR domain for performance boosting. In order to solve the overlooked problem of inconsistency between the foreground mask of pseudo and real LiDAR caused by inaccurately estimated depth, we also introduce a context-aware foreground segmentation module which helps to involve relevant points for foreground masking. Extensive experiments on KITTI dataset demonstrate that our simple yet effective framework outperforms other state-of-the-arts by a large margin."
Talking-head Generation with Rhythmic Head Motion
Authors: Lele Chen; Guofeng Cui; Celong Liu; Zhong Li; Ziyi Kou; Yi Xu; Chenliang Xu
Venue: ECCV 2020 (main)

Abstract: When people deliver a speech, they naturally move heads, and this rhythmic head motion conveys linguistic information. However, generating a lip-synced video while moving head naturally is challenging. While remarkably successful, existing works either generate still talking-face videos or rely on landmark/video frames as sparse/dense mapping guidance to generate head movements, which leads to unrealistic or uncontrollable video synthesis. To overcome the limitations, we propose a 3D-aware generative network along with a hybrid embedding module and a non-linear composition module. Through modeling the head motion and facial expressions explicitly, manipulating 3D animation carefully, and embedding reference images dynamically, our approach achieves controllable, photorealistic, and temporally coherent talking-head videos with natural head movements. Thoughtful experiments on several standard benchmarks demonstrate that our method achieves significantly better results than the state-of-the-art methods in both quantitative and qualitative comparisons. The code is available on https://github.com/lelechen63/Talking-head-Generation-with-Rhythmic-Head-Motion"
AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation
Authors: Xiaofeng Liu; Tong Che; Yiqun Lu; Chao Yang; Site Li; Jane You
Venue: ECCV 2020 (main)

Abstract: This paper targets on learning-based novel view synthesis from a single or limited 2D images without the pose supervision. In the viewer-centered coordinates, we construct an end-to-end trainable conditional variational framework to disentangle the unsupervisely learned relative-pose/rotation and implicit global 3D representation (shape, texture and the origin of viewer-centered coordinates, etc.). The global appearance of the 3D object is given by several ``appearance-describing"" images taken from any number of viewpoints. Our spatial correlation module extracts a global 3D representation from the appearance-describing images in a permutation invariant manner. Our system can achieve implicitly 3D understanding without explicitly 3D reconstruction. With an unsupervisely learned viewer-centered relative-pose/rotation code, the decoder can hallucinate the novel view continuously by sampling the relative-pose in a prior distribution. In various applications, we demonstrate that our model can achieve comparable or even better results than pose/3D model-supervised learning-based novel view synthesis (NVS) methods with any number of input views."
VPN: Learning Video-Pose Embedding for Activities of Daily Living
Authors: Srijan Das; Saurav Sharma; Rui Dai; François Brémond; Monique Thonnat
Venue: ECCV 2020 (main)

Abstract: In this paper, we focus on the spatio-temporal aspect of recognizing Activities of Daily Living (ADL). ADL have two specific properties (i) subtle spatio-temporal patterns and (ii) similar visual patterns varying with time. Therefore, ADL may look very similar and often necessitate to look at their fine-grained details to distinguish them. Because the recent spatio-temporal 3D ConvNets are too rigid to capture the subtle visual patterns across an action, we propose a novel Video-Pose Network: VPN. The 2 key components of this VPN are a spatial embedding and an attention network. The spatial embedding projects the 3D poses and RGB cues in a common semantic space. This enables the action recognition framework to learn better spatio-temporal features exploiting both modalities. In order to discriminate similar actions, the attention network provides two functionalities - (i) an end-to-end learnable pose backbone exploiting the topology of human body, and (ii) a coupler to provide joint spatio-temporal attention weights across a video. Experiments show that VPN outperforms the state-of-the-art results for action classification on a large scale human activity dataset: NTU-RGB+D 120, its subset NTU-RGB+D 60, a real-world challenging human activity dataset: Toyota Smarthome and a small scale human-object interaction dataset Northwestern UCLA."
Soft Anchor-Point Object Detection
Authors: Chenchen Zhu; Fangyi Chen; Zhiqiang Shen; Marios Savvides
Venue: ECCV 2020 (main)

Abstract: Recently, anchor-free detection methods have been through great progress. The major two families, anchor-point detection and key-point detection, are at opposite edges of the speed-accuracy trade-off, with anchor-point detectors having the speed advantage. In this work, we boost the performance of the anchor-point detector over the key-point counterparts while maintaining the speed advantage. To achieve this, we formulate the detection problem from the anchor point's perspective and identify ineffective training as the main problem. Our key insight is that anchor points should be optimized jointly as a group both within and across feature pyramid levels. We propose a simple yet effective training strategy with soft-weighted anchor points and soft-selected pyramid levels to address the false attention issue within each pyramid level and the feature selection issue across all the pyramid levels, respectively. To evaluate the effectiveness, we train a single-stage anchor-free detector called Soft Anchor-Point Detector (SAPD). Experiments show that our concise SAPD pushes the envelope of speed/accuracy trade-off to a new level, outperforming recent state-of-the-art anchor-free and anchor-based detectors. Without bells and whistles, our best model can achieve a single-model single-scale AP of 47.4% on COCO. "
Beyond Fixed Grid: Learning Geometric Image Representation with a Deformable Grid
Authors: Jun Gao; Zian Wang; Jinchen Xuan; Sanja Fidler
Venue: ECCV 2020 (main)

Abstract: In modern computer vision, images are typically represented as a fixed uniform grid with some stride and processed via a deep convolutional neural network. We argue that deforming the grid to better align with the high-frequency image content is a more effective strategy. We introduce mph{Deformable Grid} (Defgrid), a learnable neural network module that predicts location offsets of vertices of a 2-dimensional triangular grid such that the edges of the deformed grid align with image boundaries. We showcase our Defgrid in a variety of use cases, i.e., by inserting it as a module at various levels of processing. We utilize Defgrid as an end-to-end mph{learnable geometric downsampling} layer that replaces standard pooling methods for reducing feature resolution when feeding images into a deep CNN. We show significantly improved results at the same grid resolution compared to using CNNs on uniform grids for the task of semantic segmentation. We also utilize Defgrid at the output layers for the task of object mask annotation, and show that reasoning about object boundaries on our predicted polygonal grid leads to more accurate results over existing pixel-wise and curve-based approaches. We finally showcase DefGrid as a standalone module for unsupervised image segmentation, showing superior performance over existing superpixel-based approaches. Project website: http://www.cs.toronto.edu/~jungao/def-grid ."
Soft Expert Reward Learning for Vision-and-Language Navigation
Authors: Hu Wang; Qi Wu; Chunhua Shen
Venue: ECCV 2020 (main)

Abstract: Vision-and-Language Navigation (VLN) requires an agent to find a specified spot in an unseen environment by following natural language instructions. Dominant methods based on supervised learning clone expert's behaviours and thus perform better on seen environments, while showing restricted performance on unseen ones. Reinforcement Learning (RL) based models show better generalisation ability but have issues as well, requiring large amount of manual reward engineering is one of which. In this paper, we introduce a Soft Expert Reward Learning (SERL) model to overcome the reward engineering designing and generalisation problems of the VLN task. Our proposed method consists of two complementary components: Soft Expert Distillation (SED) module encourages agents to behave like an expert as much as possible, but in a soft fashion; Self Perceiving (SP) module targets at pushing the agent towards the final destination as fast as possible. Empirically, we evaluate our model on the VLN seen, unseen and test splits and the model outperforms the state-of-the-art methods on most of the evaluation metrics."
Part-aware Prototype Network for Few-shot Semantic Segmentation
Authors: Yongfei Liu; Xiangyi Zhang; Songyang Zhang; Xuming He
Venue: ECCV 2020 (main)

Abstract: Few-shot semantic segmentation aims to learn to segment new object classes with only a few annotated examples, which has a wide range of real-world applications. Most existing methods either focus on the restrictive setting of one-way few-shot segmentation or suffer from incomplete coverage of object regions. In this paper, we propose a novel few-shot semantic segmentation framework based on the prototype representation. Our key idea is to decompose the holistic class representation into a set of part-aware prototypes, capable of capturing diverse and fine-grained object features. In addition, we propose to leverage unlabeled data to enrich our part-aware prototypes, resulting in better modeling of intra-class variations of semantic objects. We develop a novel graph neural network model to generate and enhance the proposed part-aware prototypes based on labeled and unlabeled images. Extensive experimental evaluations on two benchmarks show that our method outperforms the prior art with a sizable margin."
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization
Authors: Shujun Wang; Lequan Yu; Caizi Li; Chi-Wing Fu; Pheng-Ann Heng
Venue: ECCV 2020 (main)

Abstract: The generalization capability of neural networks across domains is crucial for real-world applications. We argue that a generalized object recognition system should well understand the relationships among different images and also the images themselves at the same time. To this end, we present a new domain generalization framework that learns how to generalize across domains simultaneously from extit{extrinsic} relationship supervision and extit{intrinsic} self-supervision for images from multi-source domains. To be specific, we formulate our framework with feature embedding using a multi-task learning paradigm. Besides conducting the common supervised recognition task, we seamlessly integrate a momentum metric learning task and a self-supervised auxiliary task to collectively utilize the extrinsic supervision and intrinsic supervision. Also, we develop an effective momentum metric learning scheme with K-hard negative mining to boost the network to capture image relationship for domain generalization. We demonstrate the effectiveness of our approach on two standard object recognition benchmarks VLCS and PACS, and show that our methods achieve state-of-the-art performance."
Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos
Authors: Mahsa Ehsanpour; Alireza Abedin; Fatemeh Saleh; Javen Shi; Ian Reid; Hamid Rezatofighi
Venue: ECCV 2020 (main)

Abstract: Individuals Action and Sub-group Activities in Videos","The state-of-the art solutions for human activity understanding from a video stream formulate the task as a spatio-temporal problem which requires joint localization of all individuals in the scene and classification of their actions or group activity over time. Who is interacting with whom, e.g. not everyone in a queue is interacting with each other, is often not predicted. There are scenarios where people are best to be split into sub-groups, which we call social groups, and each social group may be engaged in a different social activity. In this paper, we solve the problem of simultaneously grouping people by their social interactions, predicting their individual actions and the social activity of each social group, which we call the social task. Our main contributions are: i) we propose an end-to-end trainable framework for the social task; ii) our proposed method also sets the state-of-the-art results on two widely adopted benchmarks for the traditional group activity recognition task (assuming individuals of the scene form a single group and predicting a single group activity label for the scene); iii) we introduce new annotations on an existing group activity dataset, re-purposing it for the social task."
Whole-Body Human Pose Estimation in the Wild
Authors: Sheng Jin; Lumin Xu; Jin Xu; Can Wang; Wentao Liu; Chen Qian; Wanli Ouyang; Ping Luo
Venue: ECCV 2020 (main)

Abstract: This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet. As existing datasets do not have whole-body annotations, previous methods have to assemble different deep models trained independently on different datasets of the human face, hand, and body, struggling with dataset biases and large model complexity. To fill in this blank, we introduce COCO-WholeBody which extends COCO dataset with whole-body annotations. To our best knowledge, it is the first benchmark that has manual annotations on the entire human body, including 133 dense landmarks with 68 on the face, 42 on hands and 23 on the body and feet. A single-network model, named ZoomNet, is devised to take into account the hierarchical structure of the full human body to solve the scale variation of different body parts of the same person. ZoomNet is able to significantly outperform existing methods on the proposed COCO-WholeBody dataset. Extensive experiments show that COCO-WholeBody not only can be used to train deep models from scratch for whole-body pose estimation but also can serve as a powerful pre-training dataset for many different tasks such as facial landmark detection and hand keypoint estimation. The dataset is publicly available at \url{https://github.com/jin-s13/COCO-WholeBody}."
Relative Pose Estimation of Calibrated Cameras with Known SE(3) Invariants
Authors: Bo Li; Evgeniy Martyushev; Gim Hee Lee
Venue: ECCV 2020 (main)

Abstract: The $\mathrm{SE}(3)$ invariants of a pose include its rotation angle and screw translation. In this paper, we present a complete comprehensive study of the relative pose estimation problem for a calibrated camera constrained by known $\mathrm{SE}(3)$ invariant, which involves 5 minimal problems in total. These problems reduces the minimal number of point pairs for relative pose estimation and improves the estimation efficiency and robustness. The $\mathrm{SE}(3)$ invariant constraints can come from extra sensor measurements or motion assumption. Different from conventional relative pose estimation with extra constraints, no extrinsic calibration is required to transform the constraints to the camera frame. This advantage comes from the invariance of $\mathrm{SE}(3)$ invariants cross different coordinate systems on a rigid body and makes the solvers more convenient and flexible in practical applications. Besides proposing the concept of relative pose estimation constrained by $\mathrm{SE}(3)$ invariants, we present a comprehensive study of existing polynomial formulations for relative pose estimation and discover their relationship. Different formulations are carefully chosen for each proposed problems to achieve best efficiency. Experiments on synthetic and real data shows performance improvement compared to conventional relative pose estimation methods."
Sequential Convolution and Runge-Kutta Residual Architecture for Image Compressed Sensing
Authors: Runkai Zheng; Yinqi Zhang; Daolang Huang; Qingliang Chen
Venue: ECCV 2020 (main)

Abstract: In recent years, Deep Neural Networks (DNN) have empowered Compressed Sensing (CS) substantially and have achieved high reconstruction quality and speed far exceeding traditional CS methods. However, there are still lots of issues to be further explored before it can be practical enough. There are mainly two challenging problems in CS, one is to achieve efficient data sampling, and the other is to reconstruct images with high-quality. To address the two challenges, this paper proposes a novel Runge-Kutta Convolutional Compressed Sensing Network (RK-CCSNet). In the sensing stage, RK-CCSNet applies Sequential Convolutional Module (SCM) to gradually compact measurements through a series of convolution filters. In the reconstruction stage, RK-CCSNet establishes a novel Learned Runge-Kutta Block (LRKB) based on the famous Runge-Kutta methods, reformulating the process of image reconstruction as a discrete dynamical system. Finally, the implementation of RK-CCSNet achieves state-of-the-art performance on influential benchmarks with respect to prestigious baselines, and all the codes are available at https://github.com/rkteddy/RK-CCSNet."