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ESCNet: Gaze Target Detection with the Understanding of 3D Scenes

ESCNet: Gaze Target Detection with the Understanding of 3D S...

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

作者： Bao, Jun Liu, Buyu Yu, Jun Hangzhou Dianzi Univ Hangzhou Peoples R China NEC Labs Amer Princeton NJ USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper aims to address the single image gaze target detection problem. Conventional methods either focus on 2D visual cues or exploit additional depth information in a very coarse manner. In this work, we propose to explicitly and effectively model 3D geometry under challenging scenario where only 2D annotations are available. We first obtain 3D point clouds of given scene with estimated depth and reference objects. Then we figure out the front-most points in all possible 3D directions of given person. These points are later leveraged in our ESCNet model. Specifically, ESCNet consists of geometry and scene parsing modules. The former produces an initial heatmap inferring the probability that each front-most point has been looking at according to estimated 3D gaze direction. And the latter further explores scene contextual cues to regulate detection results. We validate our idea on two publicly available dataset, GazeFollow and VideoAttentionTarget, and demon-strate the state-of-the-art performance. Our method also beats the human in terms of AUC on GazeFollow. Our code can be found here https://***/bjj9/ESCNet.

关键词： Geometry Point cloud compression Heating systems Solid modeling Visualization Three-dimensional displays Machine vision

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Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation

Refine Myself by Teaching Myself : Feature Refinement via Se...

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

作者： Ji, Mingi Shin, Seungjae Hwang, Seunghyun Park, Gibeom Moon, Il-Chul Korea Adv Inst Sci & Technol KAIST Daejeon South Korea Looko Inc Los Angeles CA USA Summary AI Seoul South Korea

ISBN: (纸本)9781665445092

Knowledge distillation is a method of transferring the knowledge from a pretrained complex teacher model to a student model, so a smaller network can replace a large teacher network at the deployment stage. To reduce the necessity of training a large teacher model, the recent literatures introduced a self-knowledge distillation, which trains a student network progressively to distill its own knowledge without a pretrained teacher network. While Selfknowledge distillation is largely divided into a data augmentation based approach and an auxiliary network based approach, the data augmentation approach looses its local information in the augmentation process, which hinders its applicability to diverse vision tasks, such as semantic segmentation. Moreover, these knowledge distillation approaches do not receive the refined feature maps, which are prevalent in the object detection and semantic segmentation community. This paper proposes a novel self-knowledge distillation method, Feature Refinement via Self-Knowledge Distillation (FRSKD), which utilizes an auxiliary self-teacher network to transfer a refined knowledge for the classifier network. Our proposed method, FRSKD, can utilize both soft label and feature-map distillations for the self-knowledge distillation. Therefore, FRSKD can be applied to classification, and semantic segmentation, which emphasize preserving the local information. We demonstrate the effectiveness of FRSKD by enumerating its performance improvements in diverse tasks and benchmark datasets. The implemented code is available at https://***/MingiJi/FRSKD.

关键词： Knowledge engineering Training computer vision Codes Semantics Neural networks Object detection

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Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning

Surpassing the Human Accuracy: Detecting Gallbladder Cancer ...

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

作者： Basu, Soumen Gupta, Mayank Rana, Pratyaksha Gupta, Pankaj Arora, Chetan Indian Inst Technol Delhi India Postgrad Inst Med Educ & Res Chandigarh India

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We explore the potential of CNN-based models for gallbladder cancer (GBC) detection from ultrasound (USG) images as no prior study is known. USG is the most common diagnostic modality for GB diseases due to its low cost and accessibility. However, USG images are challenging to analyze due to low image quality, noise, and varying viewpoints due to the handheld nature of the sensor. Our exhaustive study of state-of-the-art (soTA) image classification techniques for the problem reveals that they often fail to learn the salient GB region due to the presence of shadows in the USG images. S OTA object detection techniques also achieve low accuracy because of spurious textures due to noise or adjacent organs. We propose GBCNet to tackle the challenges in our problem. GBCNet first extracts the regions of interest (Ross) by detecting the GB (and not the cancer), and then uses a new multi-scale, second-order pooling architecture specializing in classifying GBC. To effectively handle spurious textures, we propose a curriculum inspired by human visual acuity, which reduces the texture biases in GBCNet. Experimental results demonstrate that GBCNet significantly outperforms SOTA CNN models, as well as the expert radiologists. Our technical innovations are generic to other USG image analysis tasks as well. Hence, as a validation, we also show the efficacy of GBCNet in detecting breast cancer from USG images. Project page with source code, trained models, and data is available at https://***/gbcnet.

关键词： Visualization computer vision Technological innovation Ultrasonic imaging Supervised learning computer architecture Object detection

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Likert Scoring with Grade Decoupling for Long-term Action Assessment

Likert Scoring with Grade Decoupling for Long-term Action As...

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

作者： Xu, Angchi Zeng, Ling-An Zheng, Wei-Shi Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Sch Artificial Intelligence Guangzhou Peoples R China Peng Cheng Lab Shenzhen Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Long-term action quality assessment is a task of evaluating how well an action is performed, namely, estimating a quality score from a long video. Intuitively, long-term actions generally involve parts exhibiting different levels of skill, and we call the levels of skill as performance grades. For example, technical highlights and faults may appear in the same long-term action. Hence, the final score should be determined by the comprehensive effect of different grades exhibited in the video. To explore this latent relationship, we design a novel Likert scoring paradigm inspired by the Likert scale in psychometrics, in which we quantify the grades explicitly and generate the final quality score by combining the quantitative values and the corresponding responses estimated from the video, instead of performing direct regression. Moreover, we extract grade-specific features, which will be used to estimate the responses of each grade, through a Transformer decoder architecture with diverse learnable queries. The whole model is named as Grade-decoupling Likert Transformer (GDLT), and we achieve state-of-the-art results on two long-term action assessment datasets.(1)

关键词： computer vision Computational modeling Estimation computer architecture Transformers Feature extraction Decoding

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ODE-inspired Network Design for Single Image Super-Resolution 32

ODE-inspired Network Design for Single Image Super-Resolutio...

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

作者： He, Xiangyu Mo, Zitao Wang, Peisong Liu, Yang Yang, Mingyuan Cheng, Jian Chinese Acad Sci Inst Automat NLPR Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781728132938

Single image super-resolution, as a high dimensional structured prediction problem, aims to characterize fine-grain information given a low-resolution sample. Recent advances in convolutional neural networks are introduced into super-resolution and push forward progress in this field. Current studies have achieved impressive performance by manually designing deep residual neural networks but overly relies on practical experience. In this paper, we propose to adopt an ordinary differential equation (ODE)-inspired design scheme for single image super-resolution, which have brought us a new understanding of ResNet in classification problems. Not only is it interpretable for super-resolution but it provides a reliable guideline on network designs. By casting the numerical schemes in ODE as blueprints, we derive two types of network structures: LF-block and RK-block, which correspond to the Leapfrog method and Runge-Kutta method in numerical ordinary differential equations. We evaluate our models on benchmark datasets, and the results show that our methods surpass the state-of-the-arts while keeping comparable parameters and operations.

关键词： Low-level vision

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Capture, Learning, and Synthesis of 3D Speaking Styles 32

Capture, Learning, and Synthesis of 3D Speaking Styles

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

作者： Cudeiro, Daniel Bolkart, Timo Laidlaw, Cassidy Ranjan, Anurag Black, Michael J. Max Planck Inst Intelligent Syst Tubingen Germany

ISBN: (纸本)9781728132938

Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, models, and standard evaluation metrics. To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers. We then train a neural network on our dataset that factors identity from facial motion. The learned model, VOCA (Voice Operated Character Animation) takes any speech signal as input-even speech in languages other than English-and realistically animates a wide range of adult faces. Conditioning on subject labels during training allows the model to learn a variety of realistic speaking styles. VOCA also provides animator controls to alter speaking style, identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball rotations) during animation. To our knowledge, VOCA is the only realistic 3D facial animation model that is readily applicable to unseen subjects without retargeting. This makes VOCA suitable for tasks like in-game video, virtual reality avatars, or any scenario in which the speaker, speech, or language is not known in advance. We make the dataset and model available for research purposes at http://***.

关键词： And Body Pose Deep Learning Face Gesture vision + Graphics

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The Role of Shape for Domain Generalization on Sparsely-Textured Images

The Role of Shape for Domain Generalization on Sparsely-Text...

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

作者： Nazari, Narges Honarvar Kovashka, Adriana Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

State-of-the-art object recognition methods do not generalize well to unseen domains. Work in domain generalization has attempted to bridge domains by increasing feature compatibility, but has focused on standard, appearance-based representations. We show the potential of shape-based representations to increase domain robustness. We compare two types of shape-based representations: one trains a convolutional network over edge features, and another computes a soft, dense medial axis transform. We show the complementary strengths of these representations for different types of domains, and the effect of the amount of texture that is preserved. We show that our shape-based techniques better leverage data augmentations for domain generalization, and are more effective at texture bias mitigation than shape-inducing augmentations. Finally, we show that when the convolutional network in state-of-the-art domain generalization methods is replaced with one that explicitly captures shape, we obtain improved results.

关键词： Bridges computer vision Shape conferences Transforms Robustness pattern recognition

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Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers

Zero-TPrune: Zero-Shot Token Pruning through Leveraging of t...

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

作者： Wang, Hongjie Dedhia, Bhishma Jha, Niraj K. Princeton Univ Princeton NJ 08540 USA

ISBN: (纸本)9798350353006

Deployment of Transformer models on edge devices is becoming increasingly challenging due to the exponentially growing inference cost that scales quadratically with the number of tokens in the input sequence. Token pruning is an emerging solution to address this challenge due to its ease of deployment on various Transformer backbones. However, most token pruning methods require computationally expensive fine-tuning, which is undesirable in many edge deployment cases. In this work, we propose Zero-TPrune, the first zero-shot method that considers both the importance and similarity of tokens in performing token pruning. It leverages the attention graph of pre-trained Transformer models to produce an importance distribution for tokens via our proposed Weighted Page Rank (WPR) algorithm. This distribution further guides token partitioning for efficient similarity-based pruning. Due to the elimination of the fine-tuning overhead, Zero-TPrune can prune large models at negligible computational cost, switch between different pruning configurations at no computational cost, and perform hyperparameter tuning efficiently. We evaluate the performance of Zero-TPrune on vision tasks by applying it to various vision Transformer backbones and testing them on ImageNet. Without any fine-tuning, Zero-TPrune reduces the FLOPs cost of DeiT-S by 34.7% and improves its throughput by 45.3% with only 0.4% accuracy loss. Compared with state- of-the-art pruning methods that require fine-tuning, Zero-TPrune not only eliminates the need for fine-tuning after pruning but also does so with only 0.1% accuracy loss. Compared with state-of-the-art fine-tuning-free pruning methods, Zero-TPrune reduces accuracy loss by up to 49% with similar FLOPs budgets. Project webpage: https://***/zerotprune.

关键词： attention efficiency token pruning vision transformer zero-shot

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Deep Safe Multi-view Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase

Deep Safe Multi-view Clustering: Reducing the Risk of Cluste...

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

作者： Tang, Huayi Liu, Yong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Multi-view clustering has been shown to boost clustering performance by effectively mining the complementary information from multiple views. However, we observe that learning from data with more views is not guaranteed to achieve better clustering performance than from data with fewer views. To address this issue, we propose a general deep learning based framework that is guaranteed to reduce the risk of performance degradation caused by view increase. Concretely, the model is trained to simultaneously extract complementary information and discard the meaningless noise by automatically selecting features. These two learning procedures are incorporated into one unified framework by the proposed optimization objective. In theory, the empirical clustering risk of the model is no higher than learning from data before the view increase and data of the new increased single view. Also, the expected clustering risk of the model under divergence-based loss is no higher than that with high probability. Comprehensive experiments on benchmark datasets demonstrate the effectiveness and superiority of the proposed framework in achieving safe multi-view clustering.

关键词： Degradation Deep learning computer vision Computational modeling Feature extraction Robustness Data models

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Multi-Source Uncertainty Mining for Deep Unsupervised Saliency Detection

Multi-Source Uncertainty Mining for Deep Unsupervised Salien...

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

作者： Wang, Yifan Zhang, Wenbo Wang, Lijun Liu, Ting Lu, Huchuan Dalian Univ Technol Dalian Peoples R China Peng Cheng Lab Shenzhen Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep learning-based image salient object detection (SOD) heavily relies on large-scale training data with pixel-wise labeling. High-quality labels involve intensive labor and are expensive to acquire. In this paper, we propose a novel multi-source uncertainty mining method to facilitate unsupervised deep learning from multiple noisy labels generated by traditional handcrafted SOD methods. We design an Uncertainty Mining Network (UMNet) which consists of multiple Merge-and-Split (MS) modules to recursively analyze the commonality and difference among multiple noisy labels and infer pixel-wise uncertainty map for each label. Meanwhile, we model the noisy labels using Gibbs distribution and propose a weighted uncertainty loss to jointly train the UMNet with the SOD network. As a consequence, our UMNet can adaptively select reliable labels for SOD network learning. Extensive experiments on benchmark datasets demonstrate that our method not only outperforms existing unsupervised methods, but also is on par with fully-supervised state-of-the-art models.

关键词： Adaptation models Uncertainty computer network reliability Training data Object detection Reliability engineering Noise measurement

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