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Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation

Rethinking the Heatmap Regression for Bottom-up Human Pose E...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Luo, Zhengxiong Wang, Zhicheng Huang, Yan Wang, Liang Tan, Tieniu Zhou, Erjin Megvii Inc Beijing Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China Natl Lab Pattern Recognit NLPR Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Beijing Peoples R China MEGVII Res Beijing Peoples R China

ISBN: (纸本)9781665445092

Heatmap regression has become the most prevalent choice for nowadays human pose estimation methods. The ground-truth heatmaps are usually constructed via covering all skeletal keypoints by 2D gaussian kernels. The standard deviations of these kernels are fixed. However, for bottom-up methods, which need to handle a large variance of human scales and labeling ambiguities, the current practice seems unreasonable. To better cope with these problems, we propose the scale-adaptive heatmap regression (SAHR) method, which can adaptively adjust the standard deviation for each keypoint. In this way, SAHR is more tolerant of various human scales and labeling ambiguities. However, SAHR may aggravate the imbalance between fore-background samples, which potentially hurts the improvement of SAHR. Thus, we further introduce the weight-adaptive heatmap regression (WAHR) to help balance the fore-background samples. Extensive experiments show that SAHR together with WAHR largely improves the accuracy of bottom-up human pose estimation. As a result, we finally outperform the state-of-the-art model by +1.5AP and achieve 72.0AP on COCO test-dev2017, which is comparable with the performances of most top-down methods. Source codes are available at https://***/greatlog/SWAHR-HumanPose.

关键词： Heating systems computer vision Codes Computational modeling Pose estimation pattern recognition Labeling

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Optimization-Inspired Cross-Attention Transformer for Compressive Sensing

Optimization-Inspired Cross-Attention Transformer for Compre...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Song, Jiechong Mou, Chong Wang, Shiqi Ma, Siwei Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

By integrating certain optimization solvers with deep neural networks, deep unfolding network (DUN) with good interpretability and high performance has attracted growing attention in compressive sensing (CS). However, existing DUNs often improve the visual quality at the price of a large number of parameters and have the problem of feature information loss during iteration. In this paper, we propose an Optimization-inspired Cross-attention Transformer (OCT) module as an iterative process, leading to a lightweight OCT-based Unfolding Framework (OCTUF) for image CS. Specifically, we design a novel Dual Cross Attention (Dual-CA) sub-module, which consists of an Inertia-Supplied Cross Attention (ISCA) block and a Projection-Guided Cross Attention (PGCA) block. ISCA block introduces multi-channel inertia forces and increases the memory effect by a cross attention mechanism between adjacent iterations. And, PGCA block achieves an enhanced information interaction, which introduces the inertia force into the gradient descent step through a cross attention block. Extensive CS experiments manifest that our OCTUF achieves superior performance compared to state-of-the-art methods while training lower complexity. Codes are available at https://***/songjiechong/OCTUF.

关键词： Low-level vision

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An Iterative and Cooperative Top-down and Bottom-up Inference Network for Salient Object Detection 32

An Iterative and Cooperative Top-down and Bottom-up Inferenc...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Wenguan Shen, Jianbing Cheng, Ming-Ming Shao, Ling Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Nankai Univ CS Tianjin Peoples R China

ISBN: (纸本)9781728132938

This paper presents a salient object detection method that integrates both top-down and bottom-up saliency inference in an iterative and cooperative manner. The top-down process is used for coarse-to-fine saliency estimation, where high-level saliency is gradually integrated with finer lower-layer features to obtain a fine-grained result. The bottom-up process infers the high-level, but rough saliency through gradually using upper-layer, semantically-richer features. These two processes are alternatively performed, where the bottom-up process uses the fine-grained saliency obtained from the top-down process to yield enhanced high-level saliency estimate, and the top-down process, in turn, is further benefited from the improved high-level information. The network layers in the bottom-up/top-down processes are equipped with recurrent mechanisms for layer-wise, step-by-step optimization. Thus, saliency information is effectively encouraged to flow in a bottom-up, top-down and intra-layer manner. We show that most other saliency models based on fully convolutional networks (FCNs) are essentially variants of our model. Extensive experiments on several famous benchmarks clearly demonstrate the superior performance, good generalization, and powerful learning ability of our proposed saliency inference framework.

关键词： Image and Video Synthesis Low-level vision

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Label-Free Liver Tumor Segmentation

Label-Free Liver Tumor Segmentation

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hu, Qixin Chen, Yixiong Xiao, Junfei Sun, Shuwen Chen, Jieneng Yuille, Alan Zhou, Zongwei Huazhong Univ Sci & Technol Wuhan Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Peoples R China Johns Hopkins Univ Baltimore MD USA Nanjing Med Univ Affiliated Hosp 1 Nanjing Peoples R China

ISBN: (纸本)9798350301298

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors;(II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors-this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to improve the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness.

关键词： cell microscopy Medical and biological vision

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Efficient Two-Stage Detection of Human-Object Interactions with a Novel Unary-Pairwise Transformer

Efficient Two-Stage Detection of Human-Object Interactions w...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Frederic Z. Campbell, Dylan Gould, Stephen Australian Natl Univ Canberra ACT Australia Univ Oxford Oxford England Australian Ctr Robot Vis Brisbane Qld Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent developments in transformer models for visual data have led to significant improvements in recognition and detection tasks. In particular, using learnable queries in place of region proposals has given rise to a new class of one-stage detection models, spearheaded by the Detection Transformer (DETR). Variations on this one-stage approach have since dominated human-object interaction (HOI) detection. However, the success of such one-stage HOI detectors can largely be attributed to the representation power of transformers. We discovered that when equipped with the same transformer, their two-stage counterparts can be more performant and memory-efficient, while taking a fraction of the time to train. In this work, we propose the Unary-Pairwise Transformer, a two-stage detector that exploits unary and pairwise representations for HOIs. We observe that the unary and pairwise parts of our transformer network specialise, with the former preferentially increasing the scores of positive examples and the latter decreasing the scores of negative examples. We evaluate our method on the HICO-DET and V-COCO datasets, and significantly outperform state-of-the-art approaches. At inference time, our model with ResNet50 approaches real-time performance on a single GPU.

关键词： Visualization Memory management Graphics processing units Detectors Transformers Real-time systems pattern recognition

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Transitional Adaptation of Pretrained Models for Visual Storytelling

Transitional Adaptation of Pretrained Models for Visual Stor...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yu, Youngjae Chung, Jiwan Yun, Heeseung Kim, Jongseok Kim, Gunhee Allen Inst AI Seattle WA 98103 USA Seoul Natl Univ Seoul South Korea Violet Seoul South Korea

ISBN: (纸本)9781665445092

Previous models for vision-to-language generation tasks usually pretrain a visual encoder and a language generator in the respective domains and jointly finetune them with the target task. However, this direct transfer practice may suffer from the discord between visual specificity and language fluency since they are often separately trained from large corpora of visual and text data with no common ground. In this work, we claim that a transitional adaptation task is required between pretraining and finetuning to harmonize the visual encoder and the language model for challenging downstream target tasks like visual storytelling. We propose a novel approach named Transitional Adaptation of Pre-trained Model (TAPM) that adapts the multi-modal modules to each other with a simpler alignment task between visual inputs only with no need for text labels. Through extensive experiments, we show that the adaptation step significantly improves the performance of multiple language models for sequential video and image captioning tasks. We achieve new state-of-the-art performance on both language metrics and human evaluation in the multi-sentence description task of LSMDC 2019 [50] and the image storytelling task of VIST [18]. Our experiments reveal that this improvement in caption quality does not depend on the specific choice of language models.

关键词： Measurement Visualization Adaptation models computer vision Computational modeling Generators pattern recognition

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SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints 32

SoPhie: An Attentive GAN for Predicting Paths Compliant to S...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Sadeghian, Amir Kosaraju, Vineet Sadeghian, Ali Hirose, Noriaki Rezatofighi, S. Hamid Savarese, Silvio Stanford Univ Stanford CA 94305 USA Aibee Inc Beijing Peoples R China Univ Florida Gainesville FL 32611 USA Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781728132938

This paper addresses the problem of path prediction for multiple interacting agents in a scene, which is a crucial step for many autonomous platforms such as self-driving cars and social robots. We present SoPhie;an interpretable framework based on Generative Adversarial Network (GAN), which leverages two sources of information, the path history of all the agents in a scene, and the scene context information, using images of the scene. To predict a future path for an agent, both physical and social information must be leveraged. Previous work has not been successful to jointly model physical and social interactions. Our approach blends a social attention mechanism with physical attention that helps the model to learn where to look in a large scene and extract the most salient parts of the image relevant to the path. Whereas, the social attention component aggregates information across the different agent interactions and extracts the most important trajectory information from the surrounding neighbors. SoPhie also takes advantage of GAN to generates more realistic samples and to capture the uncertain nature of the future paths by modeling its distribution. All these mechanisms enable our approach to predict socially and physically plausible paths for the agents and to achieve state-of-the-art performance on several different trajectory forecasting benchmarks.

关键词： Motion and Tracking vision Applications and Systems

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Revisiting the "Video" in Video-Language Understanding

Revisiting the "Video" in Video-Language Understanding

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Buch, Shyamal Eyzaguirre, Cristobal Gaidon, Adrien Wu, Jiajun Li Fei-Fei Niebles, Juan Carlos Stanford Univ Stanford CA 94305 USA Toyota Res Inst Stanford CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

What makes a video task uniquely suited for videos, beyond what can be understood from a single image? Building on recent progress in self-supervised image-language models, we revisit this question in the context of video and language tasks. We propose the atemporal probe (ATP), a new model for video-language analysis which provides a stronger bound on the baseline accuracy of multimodal models constrained by image-level understanding. By applying this model to standard discriminative video and language tasks, such as video question answering and text-to-video retrieval, we characterize the limitations and potential of current video-language benchmarks. We find that understanding of event temporality is often not necessary to achieve strong or state-of-the-art performance, even compared with recent large-scale video-language models and in contexts intended to benchmark deeper video-level understanding. We also demonstrate how ATP can improve both video-language dataset and model design. We describe a technique for leveraging ATP to better disentangle dataset subsets with a higher concentration of temporally challenging data, improving benchmarking efficacy for causal and temporal understanding. Further, we show that effectively integrating ATP into full video-level temporal models can improve efficiency and state-of-the-art accuracy.(1).

关键词： Analytical models Image recognition Buildings Benchmark testing Question answering (information retrieval) pattern recognition Task analysis

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Learning to Exploit Temporal Structure for Biomedical vision-Language Processing

Learning to Exploit Temporal Structure for Biomedical Vision...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bannur, Shruthi Hyland, Stephanie Liu, Qianchu Perez-Garcia, Fernando Ilse, Maximilian Castro, Daniel C. Boecking, Benedikt Sharma, Harshita Bouzid, Kenza Thieme, Anja Schwaighofer, Anton Wetscherek, Maria Lungren, Matthew P. Nori, Aditya Alvarez-Valle, Javier Oktay, Ozan Microsoft Hlth Futures Redmond WA 98052 USA

ISBN: (纸本)9798350301298

Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data.

关键词： cell microscopy Medical and biological vision

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CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning 32

CANet: Class-Agnostic Segmentation Networks with Iterative R...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Chi Lin, Guosheng Liu, Fayao Yao, Rui Shen, Chunhua Nanyang Technol Univ Singapore Singapore Univ Adelaide Adelaide SA Australia China Univ Min & Technol Beijing Peoples R China

ISBN: (纸本)9781728132938

Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets. However, data labeling for pixel-wise segmentation is tedious and costly. Moreover, a trained model can only make predictions within a set of pre-defined classes. In this paper, we present CANet, a class-agnostic segmentation network that performs few-shot segmentation on new classes with only a few annotated images available. Our network consists of a two-branch dense comparison module which performs multi-level feature comparison between the support image and the query image, and an iterative optimization module which iteratively refines the predicted results. Furthermore, we introduce an attention mechanism to effectively fuse information from multiple support examples under the setting of k-shot learning. Experiments on PASCAL VOC 2012 show that our method achieves a mean Intersection-over-Union score of 55.4% for 1-shot segmentation and 57.1% for 5-shot segmentation, outperforming state-of-the-art methods by a large margin of 14.6% and 13.2%, respectively.

关键词： Categorization Grouping and Shape recognition: Detection Retrieval Segmentation

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