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On the Faithfulness of vision Transformer Explanations

On the Faithfulness of Vision Transformer Explanations

引用

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

作者： Wu, Junyi Kang, Weitai Tang, Hao Hong, Yuan Yan, Yan IIT Dept Comp Sci Chicago IL 60616 USA Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Univ Connecticut Dept Comp Sci Storrs CT USA

ISBN: (纸本)9798350353006

To interpret vision Transformers, post-hoc explanations assign salience scores to input pixels, providing human-understandable heatmaps. However, whether these interpretations reflect true rationales behind the model's output is still underexplored. To address this gap, we study the faithfulness criterion of explanations: the assigned salience scores should represent the influence of the corresponding input pixels on the model's predictions. To evaluate faithfulness, we introduce Salience-guided Faithfulness Coefficient (SaCo), a novel evaluation metric leveraging essential information of salience distribution. Specifically, we con-duct pair-wise comparisons among distinct pixel groups and then aggregate the differences in their salience scores, resulting in a coefficient that indicates the explanation's degree of faithfulness. Our explorations reveal that current metrics struggle to differentiate between advanced explanation methods and Random Attribution, thereby failing to capture the faithfulness property. In contrast, our pro-posed SaCo offers a reliable faithfulness measurement, establishing a robust metric for interpretations. Furthermore, our SaCo demonstrates that the use of gradient and multi-layer aggregation can markedly enhance the faithfulness of attention-based explanation, shedding light on potential paths for advancing vision Transformer explainability.

关键词： Explainability Transformer

来源：评论

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A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives

A Backpack Full of Skills: Egocentric Video Understanding wi...

引用

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

作者： Peirone, Simone Alberto Pistilli, Francesca Alliegro, Antonio Averta, Giuseppe Politecnico Torino Turin Italy Ist Italiano Tecnol Genoa Italy

ISBN: (纸本)9798350353006

Human comprehension of a video stream is naturally broad: in a few instants, we are able to understand what is happening, the relevance and relationship of objects, and forecast what will follow in the near future, everything all at once. We believe that - to effectively transfer such an holistic perception to intelligent machines - an important role is played by learning to correlate concepts and to abstract knowledge coming from different tasks, to synergistically exploit them when learning novel skills. To accomplish this, we look for a unified approach to video understanding which combines shared temporal modelling of human actions with minimal overhead, to support multiple down-stream tasks and enable cooperation when learning novel skills. We then propose EgoPack, a solution that creates a collection of task perspectives that can be carried across downstream tasks and used as a potential source of additional insights, as a backpack of skills that a robot can carry around and use when needed. We demonstrate the effectiveness and efficiency of our approach on four Ego4D benchmarks, outperforming current state-of-the-art methods. Project webpage: ***/EgoPack.

关键词： Egocentric vision Video Understanding

来源：评论

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On the Robustness of Language Guidance for Low-Level vision Tasks: Findings from Depth Estimation

On the Robustness of Language Guidance for Low-Level Vision ...

引用

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

作者： Chatterjee, Agneet Gokhale, Tejas Baral, Chitta Yang, Yezhou Arizona State Univ Tempe AZ 85281 USA Univ Maryland Baltimore Cty Baltimore MD 21228 USA

ISBN: (纸本)9798350353013;9798350353006

Recent advances in monocular depth estimation have been made by incorporating natural language as additional guidance. Although yielding impressive results, the impact of the language prior, particularly in terms of generalization and robustness, remains unexplored. In this paper, we address this gap by quantifying the impact of this prior and introduce methods to benchmark its effectiveness across various settings. We generate "low-level" sentences that convey object-centric, three-dimensional spatial relationships, incorporate them as additional language priors and evaluate their downstream impact on depth estimation. Our key finding is that current language-guided depth estimators perform optimally only with scene-level descriptions and counter-intuitively fare worse with low level descriptions. Despite leveraging additional data, these methods are not robust to directed adversarial attacks and decline in performance with an increase in distribution shift. Finally, to provide a foundation for future research, we identify points of failures and offer insights to better understand these shortcomings. With an increasing number of methods using language for depth estimation, our findings highlight the opportunities and pitfalls that require careful consideration for effective deployment in real-world settings. (1)

关键词： Low-level vision robustness vision and language

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H-ViT: A Hierarchical vision Transformer for Deformable Image Registration

H-ViT: A Hierarchical Vision Transformer for Deformable Imag...

引用

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

作者： Ghahremani, Morteza Khateri, Mohammad Jian, Bailiang Wiestler, Benedikt Adeli, Ehsan Wachinger, Christian Tech Univ Munich Munich Germany Munich Ctr Machine Learning Munich Germany Univ Eastern Finland Espoo Finland Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9798350353006

This paper introduces a novel top-down representation approach for deformable image registration, which estimates the deformation field by capturing various short-and long-range flow features at different scale levels. As a Hierarchical vision Transformer (H-ViT), we propose a dual self-attention and cross-attention mechanism that uses high-level features in the deformation field to represent low-level ones, enabling information streams in the deformation field across all voxel patch embeddings irrespective of their spatial proximity. Since high-level features contain abstract flow patterns, such patterns are expected to effectively contribute to the representation of the deformation field in lower scales. When the self-attention module utilizes within-scale short-range patterns for representation, the cross-attention modules dynamically look for the key tokens across different scales to further interact with the local query voxel patches. Our method shows superior accuracy and visual quality over the state-of-the-art registration methods in five publicly available datasets, highlighting a substantial enhancement in the performance of medical imaging registration. The project link is available at https://***/hvit.

关键词： Deformable Image Registration Hierarchical cross-attention Medical Imaging vision Transformer

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Split to Merge: Unifying Separated Modalities for Unsupervised Domain Adaptation

Split to Merge: Unifying Separated Modalities for Unsupervis...

引用

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

作者： Li, Xinyao Li, Yuke Du, Zhekai Li, Fengling Lu, Ke Li, Jingjing Univ Elect Sci & Technol China Chengdu Peoples R China Boston Coll Chestnut Hill MA 02167 USA Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9798350353006

Large vision-language models (VLMs) like CLIP have demonstrated good zero-shot learning performance in the unsupervised domain adaptation task. Yet, most transfer approaches for VLMs focus on either the language or visual branches, overlooking the nuanced interplay between both modalities. In this work, we introduce a Unified Modality Separation (UniMoS) framework for unsupervised domain adaptation. Leveraging insights from modality gap studies, we craft a nimble modality separation network that distinctly disentangles CLIP's features into language-associated and vision-associated components. Our proposed Modality-Ensemble Training (MET) method fosters the exchange of modality-agnostic information while maintaining modality-specific nuances. We align features across domains using a modality discriminator. Comprehensive evaluations on three benchmarks reveal our approach sets a new state-of-the-art with minimal computational costs. Code: https://***/TL-UESTC/UniMoS.

关键词： deep learning Unsupervised domain adaptation vision-language model

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Proceedings - 2024 ieee Winter conference on Applications of computer vision workshops, WACVW 2024

Proceedings - 2024 IEEE Winter Conference on Applications of...

引用

2024 ieee/CVF Winter conference on Applications of computer vision workshops, WACVW 2024

ISBN: (纸本)9798350370287

The proceedings contain 123 papers. The topics discussed include: the SARFish dataset and challenge;NORPPA: NOvel ringed seal re-identification by pelage pattern aggregation;multiple toddler tracking in indoor videos;challenges in video-based infant action recognition: a critical examination of the state of the art;KABR: in-situ dataset for kenyan animal behavior recognition from drone videos;the hitchhiker's guide to endangered species pose estimation;efficient domain adaptation via generative prior for 3D infant pose estimation;dynamic gaussian splatting from markerless motion capture reconstruct infants movements;neural texture puppeteer: a framework for neural geometry and texture rendering of articulated shapes, enabling re-identification at interactive speed;and DigiDogs: single-view 3D pose estimation of dogs using synthetic training data.

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Sequential Modeling Enables Scalable Learning for Large vision Models

Sequential Modeling Enables Scalable Learning for Large Visi...

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

作者： Bail, Yutong Geng, Xinyang Mangalam, Karttikeya Bar, Amir Yuille, Alan L. Darrell, Trevor Malik, Jitendra Efros, Alexei A. UC Berkeley BAIR Berkeley CA 94720 USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9798350353006

We introduce a novel sequential modeling approach which enables learning a Large vision Model (LVM) without making use of any linguistic data. To do this, we define a common format, "visual sentences", in which we can represent raw images and videos as well as annotated data sources such as semantic segmentations and depth reconstructions with-out needing any meta-knowledge beyond the pixels. Once this wide variety of visual data (comprising 420 billion to-kens) is represented as sequences, the model can be trained to minimize a cross-entropy loss for next token prediction. By training across various scales of model architecture and data diversity, we provide empirical evidence that our models scale effectively. Many different vision tasks can be solved by designing suitable visual prompts at test time.

关键词： pretraining scaling Self-supervised Learning

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Action Scene Graphs for Long-Form Understanding of Egocentric Videos

Action Scene Graphs for Long-Form Understanding of Egocentri...

引用

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

作者： Rodin, Ivan Furnari, Antonino Min, Kyle Tripathi, Subarna Farinella, Giovanni Maria Univ Catania Catania Italy Intel Labs Hillsboro OR USA

ISBN: (纸本)9798350353006

We present Egocentric Action Scene Graphs (EASGs), a new representation for long-form understanding of egocentric videos. EASGs extend standard manually-annotated representations of egocentric videos, such as verb-noun action labels, by providing a temporally evolving graph-based description of the actions performed by the camera wearer, including interacted objects, their relationships, and how actions unfold in time. Through a novel annotation procedure, we extend the Ego4D dataset adding manually labeled Egocentric Action Scene Graphs which offer a rich set of annotations for long-from egocentric video understanding. We hence define the EASG generation task and provide a baseline approach, establishing preliminary benchmarks. Experiments on two downstream tasks, action anticipation and activity summarization, highlight the effectiveness of EASGs for long-form egocentric video understanding. We will release the dataset and code to replicate experiments and annotations 1 1 The code is available at https://***/fpv-iplab/EASG.

关键词： egocentric vision long-form video understanding scene graphs

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Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning

Context-based and Diversity-driven Specificity in Compositio...

引用

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

作者： Li, Yun Liu, Zhe Chen, Hang Yao, Lina CSIROs Data61 Clayton Vic Australia Bytedance Ltd Beijing Peoples R China Snap Inc Santa Monica CA USA

ISBN: (纸本)9798350353006

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object pairs based on a limited set of observed examples. Current CZSL methodologies, despite their advancements, tend to neglect the distinct specificity levels present in attributes. For instance, given images of sliced strawberries, they may fail to prioritize 'Sliced-Strawberry' over a generic 'Red-Strawberry', despite the former being more informative. They also suffer from ballooning search space when shifting from Close-World (CW) to Open-World (OW) CZSL. To address the issues, we introduce the Context-based and Diversity-driven Specificity learning framework for CZSL (CDS-CZSL). Our framework evaluates the specificity of attributes by considering the diversity of objects they apply to and their related context. This novel approach allows for more accurate predictions by emphasizing specific attribute-object pairs and improves composition filtering in OW-CZSL. We conduct experiments in both CW and OW scenarios, and our model achieves state-of-the-art results across three datasets.

关键词： compositional zero-shot learning transfer learning vision language model

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Transductive Zero-Shot and Few-Shot CLIP

Transductive Zero-Shot and Few-Shot CLIP

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

作者： Martin, Segolene Huang, Yunshi Shakeri, Fereshteh Pesquet, Jean-Christophe Ben Ayed, Ismail Univ Paris Saclay CVN Cent Supelec INRIA Paris France ETS Montreal Montreal PQ Canada

ISBN: (纸本)9798350353006

Transductive inference has been widely investigated in few-shot image classification, but completely overlooked in the recent, fast growing literature on adapting vision-langage models like CLIP. This paper addresses the transductive zero-shot and few-shot CLIP classification challenge, in which inference is performed jointly across a mini-batch of unlabeled query samples, rather than treating each instance independently. We initially construct informative vision-text probability features, leading to a classification problem on the unit simplex set. Inspired by Expectation-Maximization (EM), our optimization-based classification objective models the data probability distribution for each class using a Dirichlet law. The minimization problem is then tackled with a novel block Majorization-Minimization algorithm, which simultaneously estimates the distribution parameters and class assignments. Extensive numerical experiments on 11 datasets underscore the benefits and efficacy of our batch inference approach. On zero-shot tasks with test batches of 75 samples, our approach yields near 20% improvement in ImageNet accuracy over CLIP's zero-shot performance. Additionally, we outperform state-of-the-art methods in the few-shot setting. The code is available at: https://***/SegoleneMartin/transductive-CLIP.

关键词： expectation-maximization few-shot text-vision models transductive learning zero-shot

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