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Action Shuffle Alternating Learning for Unsupervised Action Segmentation

Action Shuffle Alternating Learning for Unsupervised Action ...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Jun Todorovic, Sinisa Oregon State Univ Corvallis OR 97331 USA

ISBN: (纸本)9781665445092

This paper addresses unsupervised action segmentation. Prior work captures the frame-level temporal structure of videos by a feature embedding that encodes time locations of frames in the video. We advance prior work with a new self-supervised learning (SSL) of a feature embedding that accounts for both frame- and action-level structure of videos. Our SSL trains an RNN to recognize positive and negative action sequences, and the RNN's hidden layer is taken as our new action-level feature embedding. The positive and negative sequences consist of action segments sampled from videos, where in the former the sampled action segments respect their time ordering in the video, and in the latter they are shuffled. As supervision of actions is not available and our SSL requires access to action segments, we specify an HMM that explicitly models action lengths, and infer a MAP action segmentation with the Viterbi algorithm. The resulting action segmentation is used as pseudo-ground truth for estimating our action-level feature embedding and updating the HMM. We alternate the above steps within the Generalized EM framework, which ensures convergence. Our evaluation on the Breakfast, YouTube Instructions, and 50Salads datasets gives superior results to those of the state of the art.

关键词： Training computer vision Viterbi algorithm Computational modeling Hidden Markov models pattern recognition Videos

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End-to-End Object Detection with Fully Convolutional Network

End-to-End Object Detection with Fully Convolutional Network

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wang, Jianfeng Song, Lin Li, Zeming Sun, Hongbin Sun, Jian Zheng, Nanning Megvii Technol Beijing Peoples R China Xi An Jiao Tong Univ Coll Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9781665445092

Mainstream object detectors based on the fully convolutional network has achieved impressive performance. While most of them still need a hand-designed non-maximum suppression (NMS) post-processing, which impedes fully endto-end training. In this paper, we give the analysis of discarding NMS, where the results reveal that a proper label assignment plays a crucial role. To this end, for fully convolutional detectors, we introduce a Prediction-aware One-To-One (POTO) label assignment for classification to enable end-to-end detection, which obtains comparable performance with NMS. Besides, a simple 3D Max Filtering (3DMF) is proposed to utilize the multi-scale features and improve the discriminability of convolutions in the local region. With these techniques, our end-to-end framework achieves competitive performance against many state-of-the-art detectors with NMS on COCO and CrowdHuman datasets.

关键词： Convolutional codes Training computer vision Three-dimensional displays Filtering Detectors Object detection

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Radar-Camera Pixel Depth Association for Depth Completion

Radar-Camera Pixel Depth Association for Depth Completion

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Long, Yunfei Morris, Daniel Liu, Xiaoming Castro, Marcos Chakravarty, Punarjay Narayanan, Praveen Michigan State Univ E Lansing MI 48824 USA Ford Motor Co Dearborn MI 48121 USA

ISBN: (纸本)9781665445092

While radar and video data can be readily fused at the detection level, fusing them at the pixel level is potentially more beneficial. This is also more challenging in part due to the sparsity of radar, but also because automotive radar beams are much wider than a typical pixel combined with a large baseline between camera and radar, which results in poor association between radar pixels and color pixel. A consequence is that depth completion methods designed for LiDAR and video fare poorly for radar and video. Here we propose a radar-to-pixel association stage which learns a mapping from radar returns to pixels. This mapping also serves to densify radar returns. Using this as a first stage, followed by a more traditional depth completion method, we are able to achieve imageguided depth completion with radar and video. We demonstrate performance superior to camera and radar alone on the nuScenes dataset. Our source code is available at https://***/longyunf/rc-pda.

关键词： Training computer vision Laser radar Radar measurements Image color analysis Radar detection Radar

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Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation

Differentiable SLAM-net: Learning Particle SLAM for Visual N...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Karkus, Peter Cai, Shaojun Hsu, David Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665445092

Simultaneous localization and mapping (SLAM) remains challenging for a number of downstream applications, such as visual robot navigation, because of rapid turns, featureless walls, and poor camera quality. We introduce the Differentiable SLAM Network (SLAM-net) along with a navigation architecture to enable planar robot navigation in previously unseen indoor environments. SLAM-net encodes a particle filter based SLAM algorithm in a differentiable computation graph, and learns task-oriented neural network components by backpropagating through the SLAM algorithm. Because it can optimize all model components jointly for the end-objective, SLAM-net learns to be robust in challenging conditions. We run experiments in the Habitat platform with different real-world RGB and RGB-D datasets. SLAM-net significantly outperforms the widely adapted ORB-SLAM in noisy conditions. Our navigation architecture with SLAMnet improves the state-of-the-art for the Habitat Challenge 2020 PointNav task by a large margin (37% to 64% success).

关键词： Visualization Simultaneous localization and mapping Navigation Robot vision systems Neural networks computer architecture Particle filters

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Spatially Consistent Representation Learning

Spatially Consistent Representation Learning

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Roh, Byungseok Shin, Wuhyun Kim, Ildoo Kim, Sungwoong Kakao Brain Seoul South Korea

ISBN: (纸本)9781665445092

Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. Especially, recent contrastive learning methods have shown impressive performances on downstream image classification tasks. While these contrastive methods mainly focus on generating invariant global representations at the image-level under semantic-preserving transformations, they are prone to overlook spatial consistency of local representations and therefore have a limitation in pretraining for localization tasks such as object detection and instance segmentation. Moreover, aggressively cropped views used in existing contrastive methods can minimize representation distances between the semantically different regions of a single image. In this paper, we propose a spatially consistent representation learning algorithm (SCRL) for multi-object and location-specific tasks. In particular, we devise a novel self-supervised objective that tries to produce coherent spatial representations of a randomly cropped local region according to geometric translations and zooming operations. On various downstream localization tasks with benchmark datasets, the proposed SCRL shows significant performance improvements over the image-level supervised pretraining as well as the state-of-the-art self-supervised learning methods.

关键词： Location awareness Learning systems Image segmentation computer vision Codes Object detection Benchmark testing

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SkeletonNetV2: A Dense Channel Attention Blocks for Skeleton Extraction 18

SkeletonNetV2: A Dense Channel Attention Blocks for Skeleton...

引用

18th ieee/cvf International conference on computer vision (ICCV)

作者： Nathan, Sabari Kansal, Priya Couger Inc Tokyo Japan

ISBN: (纸本)9781665401913

Geometrical analysis of a shape through skeletonization has some of very important high- and low-level application which includes tracking, manipulation, retrieval, representation, registration, recognition, and compression. The task of skeletonization is defined as the generation of the medial axis of the shape while preserving its original topology and geometry. While the earlier approaches are mainly based on extracting the skeleton and then pruning the unwanted branches, the present study proposes a novel convolutional neural network based method to perform this task. The proposed architecture is an encoder-decoder network that leverages the benefits of the coordinated convolutional layer and multi-level supervision to prevent the loss of information between the extracted skeleton and the ground truth. The dense attention block is used as the backbone block in the encoder and decoder block. This architecture is performing better than the state of art on not only skeletonization of image tasks but also skeletonization from the point cloud. This method achieved an F1 score of 0.7961 on the Pixel Skeleton dataset and a Chamfer Distance (CD) score of 1.9561 on the Point skeleton dataset.

关键词： Measurement Geometry Shape Network topology computer architecture Skeleton Topology

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Can audio-visual integration strengthen robustness under multimodal attacks?

Can audio-visual integration strengthen robustness under mul...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Tian, Yapeng Xu, Chenliang Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665445092

In this paper, we propose to make a systematic study on machines' multisensory perception under attacks. We use the audio-visual event recognition task against multimodal adversarial attacks as a proxy to investigate the robustness of audio-visual learning. We attack audio, visual, and both modalities to explore whether audio-visual integration still strengthens perception and how different fusion mechanisms affect the robustness of audio-visual models. For interpreting the multimodal interactions under attacks, we learn a weakly-supervised sound source visual localization model to localize sounding regions in videos. To mitigate multimodal attacks, we propose an audio-visual defense approach based on an audio-visual dissimilarity constraint and external feature memory banks. Extensive experiments demonstrate that audio-visual models are susceptible to multimodal adversarial attacks;audio-visual integration could decrease the model robustness rather than strengthen under multimodal attacks;even a weaklysupervised sound source visual localization model can be successfully fooled;our defense method can improve the invulnerability of audio-visual networks without significantly sacrificing clean model performance. The source code and pre-trained models are released in https://***/YapengTian/AV-Robustness-CVPR21.

关键词： Location awareness Visualization computer vision Systematics Codes Computational modeling Robustness

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Blind Deblurring for Saturated Images

Blind Deblurring for Saturated Images

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chen, Liang Zhang, Jiawei Lin, Songnan Fang, Faming Ren, Jimmy S. East China Normal Univ Proc Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Shanghai Peoples R China SenseTime Res Shanghai Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China

ISBN: (纸本)9781665445092

Blind deblurring has received considerable attention in recent years. However, state-of-the-art methods often fail to process saturated blurry images. The main reason is that pixels around saturated regions are not conforming to the commonly used linear blur model. Pioneer arts suggest excluding these pixels during the deblurring process, which sometimes simultaneously removes the informative edges around saturated regions and results in insufficient information for kernel estimation when large saturated regions exist. To address this problem, we introduce a new blur model to fit both saturated and unsaturated pixels, and all informative pixels can be considered during the deblurring process. Based on our model, we develop an effective maximum a posterior (MAP)-based optimization framework. Quantitative and qualitative evaluations on benchmark datasets and challenging real-world examples show that the proposed method performs favorably against existing methods.

关键词： computer vision Art Image edge detection Estimation Benchmark testing pattern recognition Image restoration

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Intrinsic Image Harmonization

Intrinsic Image Harmonization

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Guo, Zonghui Zheng, Haiyong Jiang, Yufeng Gu, Zhaorui Zheng, Bing Ocean Univ China Underwater Vis Lab Qingdao Shandong Peoples R China Ocean Univ China Sanya Oceanog Inst Qingdao Shandong Peoples R China

ISBN: (纸本)9781665445092

Compositing an image usually inevitably suffers from inharmony problem that is mainly caused by incompatibility of foreground and background from two different images with distinct surfaces and lights, corresponding to material-dependent and light-dependent characteristics, namely, reflectance and illumination intrinsic images, respectively. Therefore, we seek to solve image harmonization via separable harmonization of reflectance and illumination, i.e., intrinsic image harmonization. Our method is based on an autoencoder that disentangles composite image into reflectance and illumination for further separate harmonization. Specifically, we harmonize reflectance through material-consistency penalty, while harmonize illumination by learning and transferring light from background to foreground, moreover, we model patch relations between foreground and background of composite images in an inharmony-free learning way, to adaptively guide our intrinsic image harmonization. Both extensive experiments and ablation studies demonstrate the power of our method as well as the efficacy of each component. We also contribute a new challenging dataset for benchmarking illumination harmonization. Code and dataset are at https://***/zhenglab/IntrinsicHarmony.

关键词： Reflectivity computer vision Adaptation models Codes Lighting Benchmark testing pattern recognition

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POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture

POSEFusion: Pose-guided Selective Fusion for Single-view Hum...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Zhe Yu, Tao Zheng, Zerong Guo, Kaiwen Liu, Yebin Tsinghua Univ Dept Automat Beijing Peoples R China Google Zurich Switzerland

ISBN: (纸本)9781665445092

We propose POse-guided SElective Fusion (POSEFusion), a single-view human volumetric capture method that leverages tracking-based methods and tracking-free inference to achieve high-fidelity and dynamic 3D reconstruction. By contributing a novel reconstruction framework which contains pose-guided keyframe selection and robust implicit surface fusion, our method fully utilizes the advantages of both tracking-based methods and tracking-free inference methods, and finally enables the high-fidelity reconstruction of dynamic surface details even in the invisible regions. We formulate the keyframe selection as a dynamic programming problem to guarantee the temporal continuity of the reconstructed sequence. Moreover, the novel robust implicit surface fusion involves an adaptive blending weight to preserve high-fidelity surface details and an automatic collision handling method to deal with the potential self-collisions. Overall, our method enables high-fidelity and dynamic capture in both visible and invisible regions from a single RGBD camera, and the results and experiments show that our method outperforms state-of-the-art methods.

关键词： Surface reconstruction computer vision Three-dimensional displays Cameras pattern recognition Dynamic programming

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