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ChaLearn LAP Large Scale Signer Independent Isolated Sign Language recognition Challenge: Design, Results and Future Research

ChaLearn LAP Large Scale Signer Independent Isolated Sign La...

引用

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

作者： Sincan, Ozge Mercanoglu Jacques Junior, Julio C. S. Escalera, Sergio Keles, Hacer Yalim Ankara Univ Ankara Turkey Comp Vis Ctr Barcelona Spain Univ Barcelona Barcelona Spain

ISBN: (纸本)9781665448994

The performances of Sign Language recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories.

关键词： computer vision Tracking Assistive technology Computational modeling Transfer learning Semantics Gesture recognition

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LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network

LCCNet: LiDAR and Camera Self-Calibration using Cost Volume ...

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

作者： Lv, Xudong Wang, Boya Dou, Ziwen Ye, Dong Wang, Shuo Harbin Inst Technol Sch Instrumentat Sci & Engn Harbin Peoples R China

ISBN: (纸本)9781665448994

Multi-sensor fusion is for enhancing environment perception and 3D reconstruction in self-driving and robot navigation. Calibration between sensors is the precondition of effective multi-sensor fusion. Laborious manual works and complex environment settings exist in old-fashioned calibration techniques for Light Detection and Ranging (LiDAR) and camera. We propose an online LiDAR-Camera Self-calibration Network (LCCNet), different from the previous CNN-based methods. LCCNet can be trained end-to-end and predict the extrinsic parameters in real-time. In the LCCNet, we exploit the cost volume layer to express the correlation between the RGB image features and the depth image projected from point clouds. Besides using the smooth L1-Loss of the predicted extrinsic calibration parameters as a supervised signal, an additional self-supervised signal, point cloud distance loss, is applied during training. Instead of directly regressing the extrinsic parameters, we predict the decalibrated deviation from initial calibration to the ground truth. The calibration error decreases further with iterative refinement and the temporal filtering approach in the inference stage. The execution time of the calibration process is 24ms for each iteration on a single GPU. LCCNet achieves a mean absolute calibration error of 0.297cm in translation and 0.017. in rotation with miscalibration magnitudes of up to +/- 1.5m and +/- 20. on the KITTI-odometry dataset, which is better than the state-of-the-art CNN-based calibration methods. The code will be publicly available at https://***/LvXudong-HIT/LCCNet

关键词： Training Three-dimensional displays Laser radar Robot vision systems Sensor fusion Cameras Feature extraction

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Diverse Branch Block: Building a Convolution as an Inception-like Unit

Diverse Branch Block: Building a Convolution as an Inception...

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

作者： Ding, Xiaohan Zhang, Xiangyu Han, Jungong Ding, Guiguang Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China MEGVII Technol Beijing Peoples R China Aberystwyth Univ Comp Sci Dept Aberystwyth SY23 3FL Dyfed Wales MEGVII Beijing Peoples R China

ISBN: (纸本)9781665445092

We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single convolution by combining diverse branches of different scales and complexities to enrich the feature space, including sequences of convolutions, multi-scale convolutions, and average pooling. After training, a DBB can be equivalently converted into a single conv layer for deployment. Unlike the advancements of novel ConvNet architectures, DBB complicates the training-time microstructure while maintaining the macro architecture, so that it can be used as a drop-in replacement for regular conv layers of any architecture. In this way, the model can be trained to reach a higher level of performance and then transformed into the original inference-time structure for inference. DBB improves ConvNets on image classification (up to 1.9% higher top-1 accuracy on ImageNet), object detection and semantic segmentation. The PyTorch code and models are released at https:// ***/ DingXiaoH/DiverseBranchBlock.

关键词： Training Image segmentation Costs Convolution Semantics computer architecture Object detection

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Learning Calibrated Medical Image Segmentation via Multi-rater Agreement Modeling

Learning Calibrated Medical Image Segmentation via Multi-rat...

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

作者： Ji, Wei Yu, Shuang Wu, Junde Ma, Kai Bian, Cheng Bi, Qi Li, Jingjing Liu, Hanruo Cheng, Li Zheng, Yefeng Tencent Jarvis Lab Shenzhen Peoples R China Univ Alberta Edmonton AB Canada Capital Med Univ Beijing Tongren Hosp Beijing Peoples R China

ISBN: (纸本)9781665445092

In medical image analysis, it is typical to collect multiple annotations, each from a different clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated. Meanwhile, from the computer vision practitioner viewpoint, it has been a common practice to adopt the ground-truth labels obtained via either the majorityvote or simply one annotation from a preferred rater. This process, however, tends to overlook the rich information of agreement or disagreement ingrained in the raw multirater annotations. To address this issue, we propose to explicitly model the multi-rater (dis-)agreement, dubbed MRNet, which has two main contributions. First, an expertise-aware inferring module or EIM is devised to embed the expertise level of individual raters as prior knowledge, to form high-level semantic features. Second, our approach is capable of reconstructing multi-rater gradings from coarse predictions, with the multi-rater (dis-)agreement cues being further exploited to improve the segmentation performance. To our knowledge, our work is the first in producing calibrated predictions under different expertise levels for medical image segmentation. Extensive empirical experiments are conducted across five medical segmentation tasks of diverse imaging modalities. In these experiments, superior performance of our MRNet is observed comparing to the state-of-the-arts, indicating the effectiveness and applicability of our MRNet toward a wide range of medical segmentation tasks.

关键词： Image segmentation computer vision Image analysis Annotations Semantics Predictive models pattern recognition

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ReMOVE: A Reference-free Metric for Object Erasure

ReMOVE: A Reference-free Metric for Object Erasure

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ieee computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Aditya Chandrasekar Goirik Chakrabarty Jai Bardhan Ramya Hebbalaguppe Prathosh AP TCS Research IISc Bangalore IIT Delhi

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce ReMOVE, a novel reference-free metric for assessing object erasure efficacy in diffusion-based image editing models post-generation. Unlike existing measures such as LPIPS and CLIPScore, ReMOVE addresses the challenge of evaluating inpainting without a reference image, common in practical scenarios. It effectively distinguishes between object removal and replacement. This is a key issue in diffusion models due to stochastic nature of image generation. Traditional metrics fail to align with the intuitive definition of inpainting, which aims for (1) seamless object removal within masked regions (2) while preserving the background continuity. ReMOVE not only correlates with state-of-the-art metrics and aligns with human perception but also captures the nuanced aspects of the inpainting process, providing a finer-grained evaluation of the generated outputs.

关键词： Measurement computer vision Image synthesis conferences Stochastic processes Diffusion models pattern recognition

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UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

UAV-Human: A Large Benchmark for Human Behavior Understandin...

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

作者： Li, Tianjiao Liu, Jun Zhang, Wei Ni, Yun Wang, Wenqian Li, Zhiheng Singapore Univ Technol & Design Informat Syst Technol & Design Singapore Singapore Shandong Univ Sch Control Sci & Engn Jinan Shandong Peoples R China

ISBN: (纸本)9781665445092

Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large, challenging, and comprehensive benchmarks for the development and evaluation of UAV-based models. However, existing benchmarks have limitations in terms of the amount of captured data, types of data modalities, categories of provided tasks, and diversities of subjects and environments. Here we propose a new benchmark - UAV-Human - for human behavior understanding with UAVs, which contains 67,428 multi-modal video sequences and 119 subjects for action recognition, 22,476 frames for pose estimation, 41,290 frames and 1,144 identities for person re-identification, and 22,263 frames for attribute recognition. Our dataset was collected by a flying UAV in multiple urban and rural districts in both daytime and night-time over three months, hence covering extensive diversities w.r.t subjects, backgrounds, illuminations, weathers, occlusions, camera motions, and UAV flying attitudes. Such a comprehensive and challenging benchmark shall be able to promote the research of UAV-based human behavior understanding, including action recognition, pose estimation, re-identification, and attribute recognition. Furthermore, we propose a fisheye-based action recognition method that mitigates the distortions in fisheye videos via learning unbounded transformations guided by flat RGB videos. Experiments show the efficacy of our method on the UAV-Human dataset.

关键词： computer vision Pose estimation Video sequences Lighting Benchmark testing Distortion Unmanned aerial vehicles

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MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions

MotionRNN: A Flexible Model for Video Prediction with Spacet...

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

作者： Wu, Haixu Yao, Zhiyu Wang, Jianmin Long, Mingsheng Tsinghua Univ Sch Software BNRist Beijing Peoples R China

ISBN: (纸本)9781665445092

This paper tackles video prediction from a new dimension of predicting spacetime-varying motions that are incessantly changing across both space and time. Prior methods mainly capture the temporal state transitions but overlook the complex spatiotemporal variations of the motion itself, making them difficult to adapt to ever-changing motions. We observe that physical world motions can be decomposed into transient variation and motion trend, while the latter can be regarded as the accumulation of previous motions. Thus, simultaneously capturing the transient variation and the motion trend is the key to make spacetime-varying motions more predictable. Based on these observations, we propose the MotionRNN framework, which can capture the complex variations within motions and adapt to spacetime-varying scenarios. MotionRNN has two main contributions. The first is that we design the MotionGRU unit, which can model the transient variation and motion trend in a unified way. The second is that we apply the MotionGRU to RNN-based predictive models and indicate a new flexible video prediction architecture with a Motion Highway, which can significantly improve the ability to predict changeable motions and avoid motion vanishing for stacked multiple-layer predictive models. With high flexibility, this framework can adapt to a series of models for deterministic spatiotemporal prediction. Our MotionRNN can yield significant improvements on three challenging benchmarks for video prediction with spacetime-varying motions.

关键词： Road transportation Adaptation models computer vision computer architecture Predictive models Market research Spatiotemporal phenomena

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Phenology Alignment Network: A Novel Framework for Cross-Regional Time Series Crop Classification

Phenology Alignment Network: A Novel Framework for Cross-Reg...

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

作者： Wang, Ziqiao Zhang, Hongyan He, Wei Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China RIKEN Ctr Adv Intelligence Project AIP Tokyo 1030027 Japan

ISBN: (纸本)9781665448994

Timely and accurate crop type classification plays an essential role in the study of agricultural application. However, large area or cross-regional crop classification confronts huge challenges owing to dramatic phenology discrepancy among training and test regions. In this work, we propose a novel framework to address these challenges based on deep recurrent network and unsupervised domain adaptation (DA). Specifically, we firstly propose a Temporal Spatial Network (TSNet) for pixelwise crop classification, which contains stacked RNN and self-attention module to adaptively extract multi-level features from crop samples under various planting conditions. To deal with the cross-regional challenge, an unsupervised DA-based framework named Phenology Alignment Network (PAN) is proposed. PAN consists of two branches of two identical TSNet pre-trained on source domain;one branch takes source samples while the other takes target samples as input. Through aligning the hierarchical deep features extracted from two branches, the discrepancy between two regions is decreased and the pre-trained model is adapted to the target domain without using target label information. As another contribution, a time series dataset based on Sentinel-2 was annotated containing winter crop samples collected on three study sites of China. Cross-regional experiments demonstrate that TSNet shows comparable accuracy to state-of-the-art methods, and PAN further improves the overall accuracy by 5.62%, and macro average F1 score by 0.094 unsupervisedly.

关键词： Training computer vision Adaptation models conferences Time series analysis Feature extraction Agriculture

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Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration

Know Your Surroundings: Panoramic Multi-Object Tracking by M...

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

作者： He, Yuhang Yu, Wentao Han, Jie Wei, Xing Hong, Xiaopeng Gong, Yihong Xi An Jiao Tong Univ Coll Artificial Intelligence Xian Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian Peoples R China Peng Cheng Lab Res Ctr Artificial Intelligence Shenzhen Peoples R China

ISBN: (纸本)9781665448994

In this paper, we focus on the multi-object tracking (MOT) problem of automatic driving and robot navigation. Most existing MOT methods track multiple objects using a singular RGB camera, which are prone to camera field-of-view and suffer tracking failures in complex scenarios due to background clutters and poor light conditions. To meet these challenges, we propose a MultiModality PAnoramic multi-object Tracking framework (MMPAT), which takes both 2D panorama images and 3D point clouds as input and then infers target trajectories using the multimodality data. The proposed method contains four major modules, a panorama image detection module, a multimodality data fusion module, a data association module and a trajectory inference model. We evaluate the proposed method on the JRDB dataset, where the MMPAT achieves the top performance in both the detection and tracking tasks and significantly outperforms state-of-the-art methods by a large margin (15.7 and 8.5 improvement in terms of AP and MOTA, respectively).

关键词： Three-dimensional displays Target tracking Navigation Robot vision systems Object detection Cameras Data models

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ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring

ARVo: Learning All-Range Volumetric Correspondence for Video...

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

作者： Li, Dongxu Xu, Chenchen Zhang, Kaihao Yu, Xin Zhong, Yiran Ren, Wenqi Suominen, Hanna Li, Hongdong Australian Natl Univ Canberra ACT Australia DATA61 CSIRO Sydney NSW Australia UTS Sydney NSW Australia Chinese Acad Sci IIE Beijing Peoples R China Univ Turku Turku Finland

ISBN: (纸本)9781665445092

Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions. In order to utilize neighboring sharp patches, typical methods rely mainly on homography or optical flows to spatially align neighboring blurry frames. However, such explicit approaches are less effective in the presence of fast motions with large pixel displacements. In this work, we propose a novel implicit method to learn spatial correspondence among blurry frames in the feature space. To construct distant pixel correspondences, our model builds a correlation volume pyramid among all the pixel-pairs between neighboring frames. To enhance the features of the reference frame, we design a correlative aggregation module that maximizes the pixel-pair correlations with its neighbors based on the volume pyramid. Finally, we feed the aggregated features into a reconstruction module to obtain the restored frame. We design a generative adversarial paradigm to optimize the model progressively. Our proposed method is evaluated on the widely-adopted DVD dataset, along with a newly collected High-Frame-Rate (1000 fps) Dataset for Video Deblurring (HFR-DVD). Quantitative and qualitative experiments show that our model performs favorably on both datasets against previous state-of-the-art methods, confirming the benefit of modeling all-range spatial correspondence for video deblurring.

关键词： Training Solid modeling computer vision Correlation DVD Computational modeling Cameras

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