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S²-MLP: Spatial-Shift MLP Architecture for vision 22

S<SUP>2</SUP>-MLP: Spatial-Shift MLP Architecture for Vision

引用

22nd ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Yu, Tan Li, Xu Cai, Yunfeng Sun, Mingming Li, Ping Baidu Res Cognit Comp Lab 10900 NE 8th St Bellevue Washington DC 98004 USA 10 Xibeiwang East Rd Beijing 100193 Peoples R China

ISBN: (纸本)9781665409155

Recently, visual Transformer (ViT) and its following works abandon the convolution and exploit the self-attention operation, attaining a comparable or even higher accuracy than CNN. More recently, MLP-mixer abandons both the convolution and the self-attention operation, proposing an architecture containing only MLP layers. To achieve crosspatch communications, it devises an additional token-mixing MLP besides the channel-mixing MLP. It achieves promising results when training on an extremely large-scale dataset such as JFT-300M. But it cannot achieve as outstanding performance as its CNN and ViT counterparts when training on medium-scale datasets such as ImageNet- 1K. The performance drop of MLP-mixer motivates us to rethink the token-mixing MLP. We discover that token-mixing operation in MLP-mixer is a variant of depthwise convolution with a global reception field and spatial-specific configuration. In this paper, we propose a novel pure MLP architecture, spatial-shift MLP (S-2 -MLP). Different from MLP-mixer, our S-2-MLP only contains channel-mixing MLP. We devise a spatial-shift operation for achieving the communication between patches. It has a local reception field and is spatial-agnostic. Meanwhile, it is parameter-free and efficient for computation. The proposed S-2 -MLP attains higher recognition accuracy than MLP-mixer when training on ImageNet-1K dataset. Meanwhile, S-2 -MLP accomplishes as excellent performance as ViT on ImageNet-1K dataset with considerably simpler architecture and fewer FLOPs and parameters.

关键词： Training Visualization computer vision Image recognition Convolution computer architecture Transformers

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Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences

Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D ...

引用

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

作者： Mueller, Norman Wong, Yu-Shiang Mitra, Niloy J. Dai, Angela Niessner, Matthias Tech Univ Munich Munich Germany UCL London England Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

Multi-object tracking from RGB-D video sequences is a challenging problem due to the combination of changing viewpoints, motion, and occlusions over time. We observe that having the complete geometry of objects aids in their tracking, and thus propose to jointly infer the complete geometry of objects as well as track them, for rigidly moving objects over time. Our key insight is that inferring the complete geometry of the objects significantly helps in tracking. By hallucinating unseen regions of objects, we can obtain additional correspondences between the same instance, thus providing robust tracking even under strong change of appearance. From a sequence of RGB-D frames, we detect objects in each frame and learn to predict their complete object geometry as well as a dense correspondence mapping into a canonical space. This allows us to derive 6DoF poses for the objects in each frame, along with their correspondence between frames, providing robust object tracking across the RGB-D sequence. Experiments on both synthetic and real-world RGB-D data demonstrate that we achieve state-of-the-art performance on dynamic object tracking. Furthermore, we show that our object completion significantly helps tracking, providing an improvement of 6.5% in mean MOTA.

关键词： Geometry computer vision Three-dimensional displays Video sequences Pose estimation Pipelines Cameras

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Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding

Cycle4Completion: Unpaired Point Cloud Completion using Cycl...

引用

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

作者： Wen, Xin Han, Zhizhong Cao, Yan-Pei Wan, Pengfei Zheng, Wen Liu, Yu-Shen Tsinghua Univ Sch Software BNRist Beijing Peoples R China Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Kuaishou Technol Y Tech Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we present a novel unpaired point cloud completion network, named Cycle4Completion, to infer the complete geometries from a partial 3D object. Previous unpaired completion methods merely focus on the learning of geometric correspondence from incomplete shapes to complete shapes, and ignore the learning in the reverse direction, which makes them suffer from low completion accuracy due to the limited 3D shape understanding ability. To address this problem, we propose two simultaneous cycle transformations between the latent spaces of complete shapes and incomplete ones. Specifically, the first cycle transforms shapes from incomplete domain to complete domain, and then projects them back to the incomplete domain. This process learns the geometric characteristic of complete shapes, and maintains the shape consistency between the complete prediction and the incomplete input. Similarly, the inverse cycle transformation starts from complete domain to incomplete domain, and goes back to complete domain to learn the characteristic of incomplete shapes. We experimentally show that our model with the learned bidirectional geometry correspondence outperforms state-of-the-art unpaired completion methods. Code will be available at https://***/diviswen/Cycle4Completion.

关键词： Geometry computer vision Three-dimensional displays Codes Shape Computational modeling Transforms

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Learning Triadic Belief Dynamics in Nonverbal Communication from Videos

Learning Triadic Belief Dynamics in Nonverbal Communication ...

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

作者： Fan, Lifeng Qiu, Shuwen Zheng, Zilong Gao, Tao Zhu, Song-Chun Zhu, Yixin Univ Calif Los Angeles Ctr Vis Cognit Learning & Auton Los Angeles CA 90024 USA

ISBN: (纸本)9781665445092

Humans possess a unique social cognition capability [43, 20];nonverbal communication can convey rich social information among agents. In contrast, such crucial social characteristics are mostly missing in the existing scene understanding literature. In this paper, we incorporate different nonverbal communication cues (e.g., gaze, human poses, and gestures) to represent, model, learn, and infer agents' mental states from pure visual inputs. Crucially, such a mental representation takes the agent's belief into account so that it represents what the true world state is and infers the beliefs in each agent's mental state, which may differ from the true world states. By aggregating different beliefs and true world states, our model essentially forms "five minds" during the interactions between two agents. This "five minds" model differs from prior works that infer beliefs in an infinite recursion: instead, agents' beliefs are converged into a "common mind" [31, 47]. Based on this representation, we further devise a hierarchical energy-based model that jointly tracks and predicts all five minds. From this new perspective, a social event is interpreted by a series of nonverbal communication and belief dynamics, which transcends the classic keyframe video summary . . In the experiments, we demonstrate that using such a social account provides a better video summary on videos with rich social interactions compared with state-of-the-art keyframe video summary methods.

关键词： Visualization computer vision Three-dimensional displays Graphical models Heuristic algorithms Predictive models Probabilistic logic

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Proximal Splitting Adversarial Attack for Semantic Segmentation

Proximal Splitting Adversarial Attack for Semantic Segmentat...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jérôme Rony Jean-Christophe Pesquet Ismail Ben Ayed ÉTS Montréal Centre de Vision Numérique Université Paris-Saclay CentraleSupélec Inria

Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation. The methods proposed in these works do not accurately solve the adversarial segmentation problem and, therefore, overestimate the size of the perturbations required to fool models. Here, we propose a white-box attack for these models based on a proximal splitting to produce adversarial perturbations with much smaller $\ell_{\infty}$ norms. Our attack can handle large numbers of constraints within a nonconvex minimization framework via an Augmented Lagrangian approach, coupled with adaptive constraint scaling and masking strategies. We demonstrate that our attack significantly outperforms previously proposed ones, as well as classification attacks that we adapted for segmentation, providing a first comprehensive benchmark for this dense task.

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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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Learning to Detect Phone-related Pedestrian Distracted Behaviors with Synthetic Data

Learning to Detect Phone-related Pedestrian Distracted Behav...

引用

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

作者： Hatay, Emre Ma, Jin Sun, Huiming Fang, Jianwu Gao, Zhiqiang Yu, Hongkai Cleveland State Univ Cleveland OH 44115 USA Changan Univ Xian Peoples R China

ISBN: (纸本)9781665448994

Due to the popularity and mobility of smart phones, phone-related pedestrian distracted behaviors, e.g., Texting, Game Playing, and Phone calls, have caused many traffic fatalities and accidents. As an advanced driver-assistance or autonomous-driving system, computer vision could be used to automatically detect distractions from cameras installed on the vehicle for useful safety intervention. The state-of-the-art method models this problem as a standard supervised learning method with a two-branch Convolutional Neural Network (CNN) followed by a voting on all image frames. In contrast, this paper proposes a new synthetic dataset named SYN-PPDB (448 synchronized video pairs of 53,760 computer game images) for this research problem and models it as a transfer learning problem from synthetic data to real data. A new deep learning model embedded with spatial-temporal feature learning and pose-aware transfer learning is proposed. Experimental results show that we could improve the state-of-the-art overall recognition accuracy from 84.27% to 96.67%.

关键词： computer vision Computational modeling Transfer learning Supervised learning Games pattern recognition Synchronization

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BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation

BGT-Net: Bidirectional GRU Transformer Network for Scene Gra...

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

作者： Dhingra, Naina Ritter, Florian Kunz, Andreas Swiss Fed Inst Technol Innovat Ctr Virtual Real Zurich Switzerland

ISBN: (纸本)9781665448994

Scene graphs are nodes and edges consisting of objects and object-object relationships, respectively. Scene graph generation (SGG) aims to identify the objects and their relationships. We propose a bidirectional GRU (BiGRU) transformer network (BGT-Net) for the scene graph generation for images. This model implements novel object-object communication to enhance the object information using a BiGRU layer. Thus, the information of all objects in the image is available for the other objects, which can be leveraged later in the object prediction step. This object information is used in a transformer encoder to predict the object class as well as to create object-specific edge information via the use of another transformer encoder. To handle the dataset bias induced by the long-tailed relationship distribution, softening with a log-softmax function and adding a bias adaptation term to regulate the bias for every relation prediction individually showed to be an effective approach. We conducted an elaborate study on experiments and ablations using open-source datasets, i.e., Visual Genome, Open-Images, and Visual Relationship Detection datasets, demonstrating the effectiveness of the proposed model over state of the art.

关键词： Visualization computer vision Image edge detection conferences Genomics pattern recognition Softening

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Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans

Domain adaptation, Explainability & Fairness in AI for Medic...

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

作者： Dimitrios Kollias Anastasios Arsenos Stefanos Kollias Queen Mary University of London UK National Technical University of Athens Greece National Infrastructures for Research and Technology Greece

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The paper presents the DEF-AI-MIA COV19D Competition, which is organized in the framework of the ’Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (DEF-AI-MIA)’ Workshop of the 2024 computer vision and pattern recognition (cvpr) conference. The Competition is the 4th in the series, following the first three Competitions held in the framework of ICCV 2021, ECCV 2022 and ICASSP 2023 International conferences respectively. It includes two Challenges on: i) Covid-19 Detection and ii) Covid-19 Domain Adaptation. The Competition use data from COV19-CT-DB database, which is described in the paper and includes a large number of chest CT scan series. Each chest CT scan series consists of a sequence of 2-D CT slices, the number of which is between 50 and 700. Training, validation and test datasets have been extracted from COV19-CT-DB and provided to the participants in both Challenges. The paper presents the baseline models used in the Challenges and the performance which was obtained respectively, together with the best corresponding performances of the methods submitted and evaluated in the Challenges.

关键词： COVID-19 Training computer vision Image analysis Databases Computed tomography conferences

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Self-attention based Text Knowledge Mining for Text Detection

Self-attention based Text Knowledge Mining for Text Detectio...

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

作者： Wan, Qi Ji, Haoqin Shen, Linlin Shenzhen Univ Comp Vis Inst Sch Comp Sci & Software Engn Nanhai Ave 3688 Shenzhen Peoples R China Shenzhen Univ Shenzhen Inst Artificial Intelligence & Robot Soc Nanhai Ave 3688 Shenzhen Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Nanhai Ave 3688 Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Pre-trained models play an important role in deep learning based text detectors. However, most methods ignore the gap between natural images and scene text images and directly apply ImageNet for pre-training. To address such a problem, some of them firstly pre-train the model using a large amount of synthetic data and then fine-tune it on target datasets, which is task-specific and has limited generalization capability. In this paper, we focus on providing general pre-trained models for text detectors. Considering the importance of exploring text contents for text detection, we propose STKM (Self-attention based Text Knowledge Mining), which consists of a CNN Encoder and a Self-attention Decoder, to learn general prior knowledge for text detection from SynthText. Given only image level text labels, Self-attention Decoder directly decodes features extracted from CNN Encoder to texts without requirement of detection, which guides the CNN backbone to explicitly learn discriminative semantic representations ignored by previous approaches. After that, the text knowledge learned by the backbone can be transferred to various text detectors to significantly improve their detection performance (e.g., 5.89% higher F-measure for EAST on ICDAR15 dataset) without bells and whistles. Pre-trained model is available at: http.s/***/CVI -SZU/STKM

关键词： Knowledge engineering Deep learning computer vision Semantics Detectors Feature extraction Data models

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