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Adversarial Normalization: I Can visualize Everything (ICE)

Adversarial Normalization: I Can visualize Everything (ICE)

引用

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

作者： Choi, Hoyoung Jin, Seungwan Han, Kyungsik Hanyang Univ Seoul South Korea

ISBN: (纸本)9798350301298

vision transformers use [CLS] tokens to predict image classes. Their explainability visualization has been studied using relevant information from [CLS] tokens or focusing on attention scores during self-attention. Such visualization, however, is challenging because of the dependence of the structure of a vision transformer on skip connections and attention operators, the instability of non-linearities in the learning process, and the limited reflection of self-attention scores on relevance. We argue that the output vectors for each input patch token in a vision transformer retain the image information of each patch location, which can facilitate the prediction of an image class. In this paper, we propose ICE (Adversarial Normalization: I Can visualize Everything), a novel method that enables a model to directly predict a class for each patch in an image;thus, advancing the effective visualization of the explainability of a vision transformer. Our method distinguishes background from foreground regions by predicting background classes for patches that do not determine image classes. We used the DeiT-S model, the most representative model employed in studies, on the explainability visualization of vision transformers. On the ImageNet-Segmentation dataset, ICE outperformed all explainability visualization methods for four cases depending on the model size. We also conducted quantitative and qualitative analyses on the tasks of weakly-supervised object localization and unsupervised object discovery. On the CUB200-2011 and PASCALVOC07/12 datasets, ICE achieved comparable performance to the state-of-the-art methods. We incorporated ICE into the encoder of DeiT-S and improved efficiency by 44.01% on the ImageNet dataset over that achieved by the original DeiT-S model. We showed performance on the accuracy and efficiency comparable to EViT, the state-of-the-art pruning model, demonstrating the effectiveness of ICE. The code is available at https://***/Hanyang-HCC-La

关键词： Explainable computer vision

来源：评论

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Are Data-driven Explanations Robust against Out-of-distribution Data?

Are Data-driven Explanations Robust against Out-of-distribut...

引用

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

作者： Li, Tang Qiao, Fenuchun Ma, Mengmeng Peng, Xi Univ Delaware Newark DE 19716 USA

ISBN: (纸本)9798350301298

As black-box models increasingly power high-stakes applications, a variety of data-driven explanation methods have been introduced. Meanwhile, machine learning models are constantly challenged by distributional shifts. A question naturally arises: Are data-driven explanations robust against out-of-distribution data? Our empirical results show that even though predict correctly, the model might still yield unreliable explanations under distributional shifts. How to develop robust explanations against out-of-distribution data? To address this problem, we propose an end-to-end model-agnostic learning framework Distributionally Robust Explanations (DRE). The key idea is, inspired by self-supervised learning, to fully utilizes the inter-distribution information to provide supervisory signals for the learning of explanations without human annotation. Can robust explanations benefit the model's generalization capability? We conduct extensive experiments on a wide range of tasks and data types, including classification and regression on image and scientific tabular data. Our results demonstrate that the proposed method significantly improves the model's performance in terms of explanation and prediction robustness against distributional shifts.

关键词： Explainable computer vision

来源：评论

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Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for vision Decoding

Seeing Beyond the Brain: Conditional Diffusion Model with Sp...

引用

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

作者： Chen, Zijiao Qing, Jiaxin Xiang, Tiange Yue, Wan Lin Zhou, Juan Helen Natl Univ Singapore Singapore Singapore Chinese Univ Hong Kong Hong Kong Peoples R China Stanford Univ Stanford CA USA

ISBN: (纸本)9798350301298

Decoding visual stimuli from brain recordings aims to deepen our understanding of the human visual system and build a solid foundation for bridging human and computer vision through the Brain-computer Interface. However, reconstructing high-quality images with correct semantics from brain recordings is a challenging problem due to the complex underlying representations of brain signals and the scarcity of data annotations. In this work, we present MinD-Vis: Sparse Masked Brain Modeling with Double-Conditioned Latent Diffusion Model for Human vision Decoding. Firstly, we learn an effective self-supervised representation of fMRI data using mask modeling in a large latent space inspired by the sparse coding of information in the primary visual cortex. Then by augmenting a latent diffusion model with double-conditioning, we show that MinD-Vis can reconstruct highly plausible images with semantically matching details from brain recordings using very few paired annotations. We benchmarked our model qualitatively and quantitatively;the experimental results indicate that our method outperformed state-of-the-art in both semantic mapping (100-way semantic classification) and generation quality (FID) by 66% and 41% respectively. An exhaustive ablation study was also conducted to analyze our framework.

关键词： cell microscopy Medical and biological vision

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Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action recognition from Egocentric RGB Videos

Hierarchical Temporal Transformer for 3D Hand Pose Estimatio...

引用

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

作者： Wen, Yilin Pan, Hao Yang, Lei Pan, Jia Komura, Taku Wang, Wenping Univ Hong Kong Hong Kong Peoples R China Microsoft Res Asia Beijing Peoples R China TransGP Hong Kong Peoples R China Texas A&M Univ College Stn TX USA

ISBN: (纸本)9798350301298

Understanding dynamic hand motions and actions from egocentric RGB videos is a fundamental yet challenging task due to self-occlusion and ambiguity. To address occlusion and ambiguity, we develop a transformer-based framework to exploit temporal information for robust estimation. Noticing the different temporal granularity of and the semantic correlation between hand pose estimation and action recognition, we build a network hierarchy with two cascaded transformer encoders, where the first one exploits the short-term temporal cue for hand pose estimation, and the latter aggregates per-frame pose and object information over a longer time span to recognize the action. Our approach achieves competitive results on two first-person hand action benchmarks, namely FPHA and H2O. Extensive ablation studies verify our design choices.

关键词： body gesture Humans: Face movement pose

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SemiCVT: Semi-Supervised Convolutional vision Transformer for Semantic Segmentation

SemiCVT: Semi-Supervised Convolutional Vision Transformer fo...

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

作者： Huang, Huimin Xie, Shiao Lin, Lanfen Tong, Ruofeng Chen, Yen-Wei Li, Yuexiang Wang, Hong Huang, Yawen Zheng, Yefeng Zhejiang Univ Hangzhou Peoples R China Zhejiang Lab Hangzhou Peoples R China Ritsumeikan Univ Kyoto Japan Tencent Jarvis Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Semi-supervised learning improves data efficiency of deep models by leveraging unlabeled samples to alleviate the reliance on a large set of labeled samples. These successes concentrate on the pixel-wise consistency by using convolutional neural networks (CNNs) but fail to address both global learning capability and class-level features for unlabeled data. Recent works raise a new trend that Transformer achieves superior performance on the entire feature map in various tasks. In this paper, we unify the current dominant Mean-Teacher approaches by reconciling intramodel and inter-model properties for semi-supervised segmentation to produce a novel algorithm, SemiCVT, that absorbs the quintessence of CNNs and Transformer in a comprehensive way. Specifically, we first design a parallel CNN-Transformer architecture (CVT) with introducing an intra-model local-global interaction schema (LGI) in Fourier domain for full integration. The inter-model class-wise consistency is further presented to complement the class-level statistics of CNNs and Transformer in a cross-teaching manner. Extensive empirical evidence shows that SemiCVT yields consistent improvements over the state-of-the-art methods in two public benchmarks.

关键词： grouping and shape analysis Segmentation

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STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action recognition

STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Ac...

引用

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

作者： Zhu, Xiaoyu Huang, Po-Yao Liang, Junwei de Melo, Celso M. Hauptmann, Alexander Carnegie Mellon Univ Pittsburgh PA 15213 USA Meta AI FAIR New York NY USA HKUST Guangzhou Guangzhou Peoples R China DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9798350301298

We study the problem of human action recognition using motion capture (MoCap) sequences. Unlike existing techniques that take multiple manual steps to derive standardized skeleton representations as model input, we propose a novel Spatial-Temporal Mesh Transformer (STMT) to directly model the mesh sequences. The model uses a hierarchical transformer with intra-frame off-set attention and inter-frame self-attention. The attention mechanism allows the model to freely attend between any two vertex patches to learn non-local relationships in the spatial-temporal domain. Masked vertex modeling and future frame prediction are used as two self-supervised tasks to fully activate the bi-directional and auto-regressive attention in our hierarchical transformer. The proposed method achieves state-of-the-art performance compared to skeleton-based and point-cloud-based models on common MoCap benchmarks. Code is available at https://github. com/zgzxy001/STMT.

关键词： Video: Action and event understanding

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Beyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imagery

Beyond Appearances: Material Segmentation with Embedded Spec...

引用

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

作者： Perez, Fabian Rueda-Chacon, Hoover Univ Ind Santander Bucaramanga Colombia

ISBN: (纸本)9798350365474

In the realm of computer vision, material segmentation of natural scenes represents a challenge, driven by the complex and diverse appearances of materials. Traditional approaches often rely on RGB images, which can be deceptive given the variability in appearances due to different lighting conditions. Other methods, that employ polarization or spectral imagery, offer a more reliable material differentiation but their cost and accessibility restrict their everyday usage. In this work, we propose a deep learning framework that bridges the gap between high-fidelity material segmentation and the practical constraints of data acquisition. Our approach leverages a training strategy that employs a paired RGBD-spectral data to incorporate spectral information directly within the neural network. This encoding process is facilitated by a Spectral Feature Mapper (SFM) layer, a novel module that embeds unique spectral characteristics into the network, thus enabling the network to infer materials from standard RGB-D images. Once trained, the model allows to conduct material segmentation on widely available devices without the need for direct spectral data input. In addition, we generate the 3D point cloud from the RGB-D image pair, to provide a richer spatial context for scene understanding. Through simulations using available datasets, and real experiments conducted with an iPad Pro, our method demonstrates superior performance in material segmentation compared to other methods. Code is available at: https://***/Factral/ Spectral-material-segmentation

关键词： Convolutional Neural Networks Hyperspectral Images LiDAR Material recognition Material Segmentation Point Clouds RGB-D

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Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning

Gradient-based Uncertainty Attribution for Explainable Bayes...

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

作者： Wang, Hanjing Joshi, Dhiraj Wang, Shiqiang Ji, Qiang Rensselaer Polytech Inst Troy NY 12180 USA IBM Res Armonk NY USA

ISBN: (纸本)9798350301298

Predictions made by deep learning models are prone to data perturbations, adversarial attacks, and out-of-distribution inputs. To build a trusted AI system, it is therefore critical to accurately quantify the prediction uncertainties. While current efforts focus on improving uncertainty quantification accuracy and efficiency, there is a need to identify uncertainty sources and take actions to mitigate their effects on predictions. Therefore, we propose to develop explainable and actionable Bayesian deep learning methods to not only perform accurate uncertainty quantification but also explain the uncertainties, identify their sources, and propose strategies to mitigate the uncertainty impacts. Specifically, we introduce a gradient-based uncertainty attribution method to identify the most problematic regions of the input that contribute to the prediction uncertainty. Compared to existing methods, the proposed UA-Backprop has competitive accuracy, relaxed assumptions, and high efficiency. Moreover, we propose an uncertainty mitigation strategy that leverages the attribution results as attention to further improve the model performance. Both qualitative and quantitative evaluations are conducted to demonstrate the effectiveness of our proposed methods.

关键词： Explainable computer vision

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Decoupling MaxLogit for Out-of-Distribution Detection

Decoupling MaxLogit for Out-of-Distribution Detection

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

作者： Zhang, Zihan Xiang, Xiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China

ISBN: (纸本)9798350301298

In machine learning, it is often observed that standard training outputs anomalously high confidence for both indistribution (ID) and out-of-distribution (OOD) data. Thus, the ability to detect OOD samples is critical to the model deployment. An essential step for OOD detection is post-hoc scoring. MaxLogit is one of the simplest scoring functions which uses the maximum logits as OOD score. To provide a new viewpoint to study the logit-based scoring function, we reformulate the logit into cosine similarity and logit norm and propose to use MaxCosine and MaxNorm. We empirically find that MaxCosine is a core factor in the effectiveness of MaxLogit. And the performance of MaxLogit is encumbered by MaxNorm. To tackle the problem, we propose the Decoupling MaxLogit (DML) for flexibility to balance MaxCosine and MaxNorm. To further embody the core of our method, we extend DML to DML+ based on the new insights that fewer hard samples and compact feature space are the key components to make logit-based methods effective. We demonstrate the effectiveness of our logit-based OOD detection methods on CIFAR-10, CIFAR-100 and ImageNet and establish state-of-the-art performance.

关键词： detection recognition: Categorization retrieval

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ScaleDet: A Scalable Multi-Dataset Object Detector

ScaleDet: A Scalable Multi-Dataset Object Detector

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

作者： Chen, Yanbei Wang, Manchen Mittal, Abhay Xu, Zhenlin Favaro, Paolo Tighe, Joseph Modolo, Davide AWS AI Labs Shanghai Peoples R China

ISBN: (纸本)9798350301298

Multi-dataset training provides a viable solution for exploiting heterogeneous large-scale datasets without extra annotation cost. In this work, we propose a scalable multi-dataset detector (ScaleDet) that can scale up its generalization across datasets when increasing the number of training datasets. Unlike existing multi-dataset learners that mostly rely on manual relabelling efforts or sophisticated optimizations to unify labels across datasets, we introduce a simple yet scalable formulation to derive a unified semantic label space for multi-dataset training. ScaleDet is trained by visual-textual alignment to learn the label assignment with label semantic similarities across datasets. Once trained, ScaleDet can generalize well on any given upstream and downstream datasets with seen and unseen classes. We conduct extensive experiments using LVIS, COCO, Objects365, OpenImages as upstream datasets, and 13 datasets from Object Detection in the Wild (ODinW) as downstream datasets. Our results show that ScaleDet achieves compelling strong model performance with an mAP of 50.7 on LVIS, 58.8 on COCO, 46.8 on Objects365, 76.2 on OpenImages, and 71.8 on ODinW, surpassing state-of-the-art detectors with the same backbone.

关键词： Efficient and scalable vision

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