pwc-archive/papers-with-abstracts huggingface dataset:
A Spatial RNN Codec for End-to-End Image Compression
Authors: Chaoyi Lin,  Jiabao Yao,  Fangdong Chen,  Li Wang
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Image Compression, MS-SSIM, SSIM

Recently, deep learning has been explored as a promising direction for image compression. Removing the spatial redundancy of the image is crucial for image compression and most learning based methods focus on removing the redundancy between adjacent pixels. Intuitively, to explore larger pixel range beyond adjacent pixel is beneficial for removing the redundancy. In this paper, we propose a fast yet effective method for end-to-end image compression by incorporating a novel spatial recurrent neural network. Block based LSTM is utilized to remove the redundant information between adjacent pixels and blocks. Besides, the proposed method is a potential efficient system that parallel computation on individual blocks is possible. Experimental results demonstrate that the proposed model outperforms state-of-the-art traditional image compression standards and learning based image compression models in terms of both PSNR and MS-SSIM metrics. It provides a 26.73% bits-reduction than High Efficiency Video Coding (HEVC), which is the current official state-of-the-art video codec.
Gait Recognition via Semi-supervised Disentangled Representation Learning to Identity and Covariate Features
Authors: Xiang Li,  Yasushi Makihara,  Chi Xu,  Yasushi Yagi,  Mingwu Ren
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Disentanglement, Gait Recognition, Representation Learning, Triplet

Existing gait recognition approaches typically focus on learning identity features that are invariant to covariates (e.g., the carrying status, clothing, walking speed, and viewing angle) and seldom involve learning features from the covariate aspect, which may lead to failure modes when variations due to the covariate overwhelm those due to the identity. We therefore propose a method of gait recognition via disentangled representation learning that considers both identity and covariate features. Specifically, we first encode an input gait template to get the disentangled identity and covariate features, and then decode the features to simultaneously reconstruct the input gait template and the canonical version of the same subject with no covariates in a semi-supervised manner to ensure successful disentanglement. We finally feed the disentangled identity features into a contrastive/triplet loss function for a verification/identification task. Moreover, we find that new gait templates can be synthesized by transferring the covariate feature from one subject to another. Experimental results on three publicly available gait data sets demonstrate the effectiveness of the proposed method compared with other state-of-the-art methods.
Interactive Image Segmentation With First Click Attention
Authors: Zheng Lin,  Zhao Zhang,  Lin-Zhuo Chen,  Ming-Ming Cheng,  Shao-Ping Lu
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Image Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

In the task of interactive image segmentation, users initially click one point to segment the main body of the target object and then provide more points on mislabeled regions iteratively for a precise segmentation. Existing methods treat all interaction points indiscriminately, ignoring the difference between the first click and the remaining ones. In this paper, we demonstrate the critical role of the first click about providing the location and main body information of the target object. A deep framework, named First Click Attention Network (FCA-Net), is proposed to make better use of the first click. In this network, the interactive segmentation result can be much improved with the following benefits: focus invariance, location guidance, and error-tolerant ability. We then put forward a click-based loss function and a structural integrity strategy for better segmentation effect. The visualized segmentation results and sufficient experiments on five datasets demonstrate the importance of the first click and the superiority of our FCA-Net.
Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians
Authors: Jialian Wu,  Chunluan Zhou,  Ming Yang,  Qian Zhang,  Yuan Li,  Junsong Yuan
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Pedestrian Detection

State-of-the-art pedestrian detectors have performed promisingly on non-occluded pedestrians, yet they are still confronted by heavy occlusions. Although many previous works have attempted to alleviate the pedestrian occlusion issue, most of them rest on still images. In this paper, we exploit the local temporal context of pedestrians in videos and propose a tube feature aggregation network (TFAN) aiming at enhancing pedestrian detectors against severe occlusions. Specifically, for an occluded pedestrian in the current frame, we iteratively search for its relevant counterparts along temporal axis to form a tube. Then, features from the tube are aggregated according to an adaptive weight to enhance the feature representations of the occluded pedestrian. Furthermore, we devise a temporally discriminative embedding module (TDEM) and a part-based relation module (PRM), respectively, which adapts our approach to better handle tube drifting and heavy occlusions. Extensive experiments are conducted on three datasets, Caltech, NightOwls and KAIST, showing that our proposed method is significantly effective for heavily occluded pedestrian detection. Moreover, we achieve the state-of-the-art performance on the Caltech and NightOwls datasets.
Connect-and-Slice: An Hybrid Approach for Reconstructing 3D Objects
Authors: Hao Fang,  Florent Lafarge
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6

Converting point clouds generated by Laser scanning, multiview stereo imagery or depth cameras into compact polygon meshes is a challenging problem in vision. Existing methods are either robust to imperfect data or scalable, but rarely both. In this paper, we address this issue with an hybrid method that successively connects and slices planes detected from 3D data. The core idea consists in constructing an efficient and compact partitioning data structure. The later is i) spatially-adaptive in the sense that a plane slices a restricted number of relevant planes only, and ii) composed of components with different structural meaning resulting from a preliminary analysis of the plane connectivity. Our experiments on a variety of objects and sensors show the versatility of our approach as well as its competitiveness with respect to existing methods.
Attentive Weights Generation for Few Shot Learning via Information Maximization
Authors: Yiluan Guo,  Ngai-Man Cheung
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Classification, Few-Shot Image Classification, Few-Shot Learning, General Classification, image-classification, Image Classification, Meta-Learning

Few shot image classification aims at learning a classifier from limited labeled data. Generating the classification weights has been applied in many meta-learning methods for few shot image classification due to its simplicity and effectiveness. In this work, we present Attentive Weights Generation for few shot learning via Information Maximization (AWGIM), which introduces two novel contributions: i) Mutual information maximization between generated weights and data within the task; this enables the generated weights to retain information of the task and the specific query sample. ii) Self-attention and cross-attention paths to encode the context of the task and individual queries. Both two contributions are shown to be very effective in extensive experiments. Overall, AWGIM is competitive with state-of-the-art. Code is available at https://github.com/Yiluan/AWGIM.
Assessing Eye Aesthetics for Automatic Multi-Reference Eye In-Painting
Authors: Bo Yan,  Qing Lin,  Weimin Tan,  Shili Zhou
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6

With the wide use of artistic images, aesthetic quality assessment has been widely concerned. How to integrate aesthetics into image editing is still a problem worthy of discussion. In this paper, aesthetic assessment is introduced into eye in-painting task for the first time. We construct an eye aesthetic dataset, and train the eye aesthetic assessment network on this basis. Then we propose a novel eye aesthetic and face semantic guided multi-reference eye inpainting GAN approach (AesGAN), which automatically selects the best reference under the guidance of eye aesthetics. A new aesthetic loss has also been introduced into the network to learn the eye aesthetic features and generate highquality eyes. We prove the effectiveness of eye aesthetic assessment in our experiments, which may inspire more applications of aesthetics assessment. Both qualitative and quantitative experimental results show that the proposed AesGAN can produce more natural and visually attractive eyes compared with state-of-the-art methods.
PuppeteerGAN: Arbitrary Portrait Animation With Semantic-Aware Appearance Transformation
Authors: Zhuo Chen,  Chaoyue Wang,  Bo Yuan,  Dacheng Tao
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Portrait Animation, Segmentation, Semantic Segmentation

Portrait animation, which aims to animate a still portrait to life using poses extracted from target frames, is an important technique for many real-world entertainment applications. Although recent works have achieved highly realistic results on synthesizing or controlling human head images, the puppeteering of arbitrary portraits is still confronted by the following challenges: 1) identity/personality mismatch; 2) training data/domain limitations; and 3) low-efficiency in training/fine-tuning. In this paper, we devised a novel two-stage framework called PuppeteerGAN for solving these challenges. Specifically, we first learn identity-preserved semantic segmentation animation which executes pose retargeting between any portraits. As a general representation, the semantic segmentation results could be adapted to different datasets, environmental conditions or appearance domains. Furthermore, the synthesized semantic segmentation is filled with the appearance of the source portrait. To this end, an appearance transformation network is presented to produce fidelity output by jointly considering the wrapping of semantic features and conditional generation. After training, the two networks can directly perform end-to-end inference on unseen subjects without any retraining or fine-tuning. Extensive experiments on cross-identity/domain/resolution situations demonstrate the superiority of the proposed PuppetterGAN over existing portrait animation methods in both generation quality and inference speed.
Texture and Shape Biased Two-Stream Networks for Clothing Classification and Attribute Recognition
Authors: Yuwei Zhang,  Peng Zhang,  Chun Yuan,  Zhi Wang
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Attribute, Classification, General Classification

Clothes category classification and attribute recognition have achieved distinguished success with the development of deep learning. People have found that landmark detection plays a positive role in these tasks. However, little research is committed to analyzing these tasks from the perspective of clothing attributes. In our work, we explore the usefulness of landmarks and find that landmarks can assist in extracting shape features; and using landmarks for joint learning can increase classification and recognition accuracy effectively. We also find that texture features have an impelling effect on these tasks and that the pre-trained ImageNet model has good performance in extracting texture features. To this end, we propose to use two streams to enhance the extraction of shape and texture, respectively. In particular, this paper proposes a simple implementation, Texture and Shape biased Fashion Networks (TS-FashionNet). Comprehensive and rich experiments demonstrate our discoveries and the effectiveness of our model. We improve the top-3 classification accuracy by 0.83% and improve the top-3 attribute recognition recall rate by 1.39% compared to the state-of-the-art models.
BFBox: Searching Face-Appropriate Backbone and Feature Pyramid Network for Face Detector
Authors: Yang Liu,  Xu Tang
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Face Detection, image-classification, Image Classification, object-detection, Object Detection

Popular backbones designed on image classification have demonstrated their considerable compatibility on the task of general object detection. However, the same phenomenon does not appear on the face detection. This is largely due to the average scale of ground-truth in the WiderFace dataset is far smaller than that of generic objects in theCOCO one. To resolve this, the success of Neural Archi-tecture Search (NAS) inspires us to search face-appropriate backbone and featrue pyramid network (FPN) architecture.Firstly, we design the search space for backbone and FPN by comparing performance of feature maps with different backbones and excellent FPN architectures on the face detection. Second, we propose a FPN-attention module to joint search the architecture of backbone and FPN. Finally,we conduct comprehensive experiments on popular bench-marks, including Wider Face, FDDB, AFW and PASCALFace, display the superiority of our proposed method.
End-to-End Camera Calibration for Broadcast Videos
Authors: Long Sha,  Jennifer Hobbs,  Panna Felsen,  Xinyu Wei,  Patrick Lucey,  Sujoy Ganguly
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Camera Calibration, Camera Pose Estimation, Pose Estimation

The increasing number of vision-based tracking systems deployed in production have necessitated fast, robust camera calibration. In the domain of sport, the majority of current work focuses on sports where lines and intersections are easy to extract, and appearance is relatively consistent across venues. However, for more challenging sports like basketball, those techniques are not sufficient. In this paper, we propose an end-to-end approach for single moving camera calibration across challenging scenarios in sports. Our method contains three key modules: 1) area-based court segmentation, 2) camera pose estimation with embedded templates, 3) homography prediction via a spatial transform network (STN). All three modules are connected, enabling end-to-end training. We evaluate our method on a new college basketball dataset and demonstrate state of the art performance in variable and dynamic environments. We also validate our method on the World Cup 2014 dataset to show its competitive performance against the state-of-the-art methods. Lastly, we show that our method is two orders of magnitude faster than the previous state of the art on both datasets.
Attention-Guided Hierarchical Structure Aggregation for Image Matting
Authors: Yu Qiao,  Yuhao Liu,  Xin Yang,  Dongsheng Zhou,  Mingliang Xu,  Qiang Zhang,  Xiaopeng Wei
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Image Matting, SSIM

Existing deep learning based matting algorithms primarily resort to high-level semantic features to improve the overall structure of alpha mattes. However, we argue that advanced semantics extracted from CNNs contribute unequally for alpha perception and we are supposed to reconcile advanced semantic information with low-level appearance cues to refine the foreground details. In this paper, we propose an end-to-end Hierarchical Attention Matting Network (HAttMatting), which can predict the better structure of alpha mattes from single RGB images without additional input. Specifically, we employ spatial and channel-wise attention to integrate appearance cues and pyramidal features in a novel fashion. This blended attention mechanism can perceive alpha mattes from refined boundaries and adaptive semantics. We also introduce a hybrid loss function fusing Structural SIMilarity (SSIM), Mean Square Error (MSE) and Adversarial loss to guide the network to further improve the overall foreground structure. Besides, we construct a large-scale image matting dataset comprised of 59,600 training images and 1000 test images (total 646 distinct foreground alpha mattes), which can further improve the robustness of our hierarchical structure aggregation model. Extensive experiments demonstrate that the proposed HAttMatting can capture sophisticated foreground structure and achieve state-of-the-art performance with single RGB images as input.
Prior Guided GAN Based Semantic Inpainting
Authors: Avisek Lahiri,  Arnav Kumar Jain,  Sanskar Agrawal,  Pabitra Mitra,  Prabir Kumar Biswas
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6

Contemporary deep learning based semantic inpainting can be approached from two directions. First, and the more explored, approach is to train an offline deep regression network over the masked pixels with an additional refinement by adversarial training. This approach requires a single feed-forward pass for inpainting at inference. Another promising, yet unexplored approach is to first train a generative model to map a latent prior distribution to natural image manifold and during inference time search for the best-matching prior to reconstruct the signal. The primary aversion towards the latter genre is due to its inference time iterative optimization and difficulty to scale to higher resolution. In this paper, going against the general trend, we focus on the second paradigm of inpainting and address both of its mentioned problems. Most importantly, we learn a data driven parametric network to directly predict a matching prior for a given masked image. This converts an iterative paradigm to a single feed forward inference pipeline with around 800X speedup. We also regularize our network with structural prior (computed from the masked image itself) which helps in better preservation of pose and size of the object to be inpainted. Moreover, to extend our model for sequence reconstruction, we propose a recurrent net based grouped latent prior learning. Finally, we leverage recent advancements in high resolution GAN training to scale our inpainting network to 256X256. Experiments (spanning across resolutions from 64X64 to 256X256) conducted on SVHN, Standford Cars, CelebA, CelebA-HQ and ImageNet image datasets, and FaceForensics video datasets reveal that we consistently improve upon contemporary benchmarks from both schools of approaches.
Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer Labels
Authors: Xun Xu,  Gim Hee Lee
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Point Cloud Segmentation, Segmentation, Weakly Supervised 3D Point Cloud Segmentation

Point cloud analysis has received much attention recently; and segmentation is one of the most important tasks. The success of existing approaches is attributed to deep network design and large amount of labelled training data, where the latter is assumed to be always available. However, obtaining 3d point cloud segmentation labels is often very costly in practice. In this work, we propose a weakly supervised point cloud segmentation approach which requires only a tiny fraction of points to be labelled in the training stage. This is made possible by learning gradient approximation and exploitation of additional spatial and color smoothness constraints. Experiments are done on three public datasets with different degrees of weak supervision. In particular, our proposed method can produce results that are close to and sometimes even better than its fully supervised counterpart with 10X fewer labels.
Learning Selective Self-Mutual Attention for RGB-D Saliency Detection
Authors: Nian Liu,  Ni Zhang,  Junwei Han
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Decoder, RGB-D Salient Object Detection, Saliency Detection

Saliency detection on RGB-D images is receiving more and more research interests recently. Previous models adopt the early fusion or the result fusion scheme to fuse the input RGB and depth data or their saliency maps, which incur the problem of distribution gap or information loss. Some other models use the feature fusion scheme but are limited by the linear feature fusion methods. In this paper, we propose to fuse attention learned in both modalities. Inspired by the Non-local model, we integrate the self-attention and each other's attention to propagate long-range contextual dependencies, thus incorporating multi-modal information to learn attention and propagate contexts more accurately. Considering the reliability of the other modality's attention, we further propose a selection attention to weight the newly added attention term. We embed the proposed attention module in a two-stream CNN for RGB-D saliency detection. Furthermore, we also propose a residual fusion module to fuse the depth decoder features into the RGB stream. Experimental results on seven benchmark datasets demonstrate the effectiveness of the proposed model components and our final saliency model. Our code and saliency maps are available at https://github.com/nnizhang/S2MA.
Estimating Low-Rank Region Likelihood Maps
Authors: Gabriela Csurka,  Zoltan Kato,  Andor Juhasz,  Martin Humenberger
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6

Low-rank regions capture geometrically meaningful structures in an image which encompass typical local features such as edges, corners and all kinds of regular, symmetric, often repetitive patterns, that are commonly found in man-made environment. While such patterns are challenging current state-of-the-art feature correspondence methods, the recovered homography of a low-rank texture readily provides 3D structure with respect to a 3D plane, without any prior knowledge of the visual information on that plane. However, the automatic and efficient detection of the broad class of low-rank regions is unsolved. Herein, we propose a novel self-supervised low-rank region detection deep network that predicts a low-rank likelihood map from an image. The evaluation of our method on real-world datasets shows not only that it reliably predicts low-rank regions in the image similarly to our baseline method, but thanks to the data augmentations used in the training phase it generalizes well to difficult cases (e.g. day/night lighting, low contrast, underexposure) where the baseline prediction fails.
Hierarchical Graph Attention Network for Visual Relationship Detection
Authors: Li Mi,  Zhenzhong Chen
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Feature Correlation, Graph Attention, Object, Relationship Detection, Triplet, Visual Relationship Detection

Visual Relationship Detection (VRD) aims to describe the relationship between two objects by providing a structural triplet shown as . Existing graph-based methods mainly represent the relationships by an object-level graph, which ignores to model the triplet-level dependencies. In this work, a Hierarchical Graph Attention Network (HGAT) is proposed to capture the dependencies on both object-level and triplet-level. Object-level graph aims to capture the interactions between objects, while the triplet-level graph models the dependencies among relation triplets. In addition, prior knowledge and attention mechanism are introduced to fix the redundant or missing edges on graphs that are constructed according to spatial correlation. With these approaches, nodes are allowed to attend over their spatial and semantic neighborhoods' features based on the visual or semantic feature correlation. Experimental results on the well-known VG and VRD datasets demonstrate that our model significantly outperforms the state-of-the-art methods.