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Grounding Counterfactual Explanation of Image Classifiers to Textual Concept Space

Grounding Counterfactual Explanation of Image Classifiers to...

引用

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

作者： Kim, Siwon Oh, Jinoh Lee, Sungjin Yu, Seunghak Doe, Jaeyoung Taghavi, Tara Seoul Natl Univ Data Sci & Artificial Intelligence Lab Seoul South Korea Amazon Alexa AI Seattle WA USA NAVER Search US Seongnam South Korea

ISBN: (纸本)9798350301298

Concept-based explanation aims to provide concise and human-understandable explanations of an image classifier. However, existing concept-based explanation methods typically require a significant amount of manually collected concept-annotated images. This is costly and runs the risk of human biases being involved in the explanation. In this paper, we propose Counterfactual explanation with text-driven concepts (CounTEX), where the concepts are defined only from text by leveraging a pre-trained multimodal joint embedding space without additional concept-annotated datasets. A conceptual counterfactual explanation is generated with text-driven concepts. To utilize the text-driven concepts defined in the joint embedding space to interpret target classifier outcome, we present a novel projection scheme for mapping the two spaces with a simple yet effective implementation. We show that CounTEX generates faithful explanations that provide a semantic understanding of model decision rationale robust to human bias.

关键词： Explainable computer vision

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Explaining Image Classifiers with Multiscale Directional Image Representation

Explaining Image Classifiers with Multiscale Directional Ima...

引用

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

作者： Kolek, Stefan Windesheim, Robert Andrade-Loarca, Hector Kutyniok, Gitta Levie, Ron Ludwig Maximilians Univ Munchen Dept Math Munich Germany Univ Tromso Dept Phys & Technol Tromso Norway Technion Israel Inst Technol Dept Math Haifa Israel

ISBN: (纸本)9798350301298

Image classifiers are known to be difficult to interpret and therefore require explanation methods to understand their decisions. We present ShearletX, a novel mask explanation method for image classifiers based on the shear-let transform - a multiscale directional image representation. Current mask explanation methods are regularized by smoothness constraints that protect against undesirable fine-grained explanation artifacts. However, the smoothness of a mask limits its ability to separate fine-detail patterns, that are relevant for the classifier, from nearby nuisance patterns, that do not affect the classifier. ShearletX solves this problem by avoiding smoothness regularization all together, replacing it by shearlet sparsity constraints. The resulting explanations consist of a few edges, textures, and smooth parts of the original image, that are the most relevant for the decision of the classifier. To support our method, we propose a mathematical definition for explanation artifacts and an information theoretic score to evaluate the quality of mask explanations. We demonstrate the superiority of ShearletX over previous mask based explanation methods using these new metrics, and present exemplary situations where separating fine-detail patterns allows explaining phenomena that were not explainable before.

关键词： Explainable computer vision

来源：评论

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GeneCIS: A Benchmark for General Conditional Image Similarity

GeneCIS: A Benchmark for General Conditional Image Similarit...

引用

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

作者： Vaze, Sagar Carion, Nicolas Misra, Ishan Meta AI FAIR Menlo Pk CA 94025 USA Univ Oxford VGG Oxford England

ISBN: (纸本)9798350301298

We argue that there are many notions of 'similarity' and that models, like humans, should be able to adapt to these dynamically. This contrasts with most representation learning methods, supervised or self-supervised, which learn a fixed embedding function and hence implicitly assume a single notion of similarity. For instance, models trained on ImageNet are biased towards object categories, while a user might prefer the model to focus on colors, textures or specific elements in the scene. In this paper, we propose the GeneCIS ('genesis') benchmark, which measures models' ability to adapt to a range of similarity conditions. Extending prior work, our benchmark is designed for zero-shot evaluation only, and hence considers an open-set of similarity conditions. We find that baselines from powerful CLIP models struggle on GeneCIS and that performance on the benchmark is only weakly correlated with ImageNet accuracy, suggesting that simply scaling existing methods is not fruitful. We further propose a simple, scalable solution based on automatically mining information from existing image-caption datasets. We find our method offers a substantial boost over the baselines on GeneCIS, and further improves zero-shot performance on related image retrieval benchmarks. In fact, though evaluated zero-shot, our model surpasses state-of-the-art supervised models on MIT-States.

关键词： language reasoning vision

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Filtering, Distillation, and Hard Negatives for vision-Language Pre-Training

Filtering, Distillation, and Hard Negatives for Vision-Langu...

引用

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

作者： Radenovic, Filip Dubey, Abhimanyu Kadian, Abhishek Mihaylov, Todor Vandenhende, Simon Patel, Yash Wen, Yi Ramanathan, Vignesh Mahajan, Dhruv Meta AI New York NY 10003 USA Czech Tech Univ Prague Czech Republic

ISBN: (纸本)9798350301298

vision-language models trained with contrastive learning on large-scale noisy data are becoming increasingly popular for zero-shot recognition problems. In this paper we improve the following three aspects of the contrastive pre-training pipeline: dataset noise, model initialization and the training objective. First, we propose a straightforward filtering strategy titled Complexity, Action, and Text-spotting (CAT) that significantly reduces dataset size, while achieving improved performance across zero-shot vision-language tasks. Next, we propose an approach titled Concept Distillation to leverage strong unimodal representations for contrastive training that does not increase training complexity while outperforming prior work. Finally, we modify the traditional contrastive alignment objective, and propose an importance-sampling approach to up-sample the importance of hard-negatives without adding additional complexity. On an extensive zero-shot benchmark of 29 tasks, our Distilled and Hard-negative Training (DiHT) approach improves on 20 tasks compared to the baseline. Furthermore, for few-shot linear probing, we propose a novel approach that bridges the gap between zero-shot and few-shot performance, substantially improving over prior work. Models are available at ***/facebookresearch/diht.

关键词： and reasoning language vision

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

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

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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

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Learning Steerable Function for Efficient Image Resampling

Learning Steerable Function for Efficient Image Resampling

引用

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

作者： Li, Jiacheng Chen, Chang Huang, Wei Lang, Zhiqiang Song, Fenglong Yan, Youliang Xiong, Zhiwei Univ Sci & Technol China Chengdu Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Image resampling is a basic technique that is widely employed in daily applications. Existing deep neural networks (DNNs) have made impressive progress in resampling performance. Yet these methods are still not the perfect substitute for interpolation, due to the issues of efficiency and continuous resampling. In this work, we propose a novel method of Learning Resampling Function (termed LeRF), which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption of interpolation methods. Specifically, LeRF assigns spatially-varying steerable resampling functions to input image pixels and learns to predict the hyper-parameters that determine the orientations of these resampling functions with a neural network. To achieve highly efficient inference, we adopt look-up tables (LUTs) to accelerate the inference of the learned neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures. Extensive experiments show that our method runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly, e.g., up to 3dB PSNR gain over bicubic for x2 upsampling on Manga109.

关键词： Low-level vision

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Comprehensive and Delicate: An Efficient Transformer for Image Restoration

Comprehensive and Delicate: An Efficient Transformer for Ima...

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

作者： Zhao, Haiyu Gou, Yuanbiao Li, Boyun Peng, Dezhong Lv, Jiancheng Peng, Xi Sichuan Univ Coll Comp Sci Chengdu Peoples R China

ISBN: (纸本)9798350301298

vision Transformers have shown promising performance in image restoration, which usually conduct window- or channel-based attention to avoid intensive computations. Although the promising performance has been achieved, they go against the biggest success factor of Transformers to a certain extent by capturing the local instead of global dependency among pixels. In this paper, we propose a novel efficient image restoration Transformer that first captures the superpixel-wise global dependency, and then transfers it into each pixel. Such a coarse-to-fine paradigm is implemented through two neural blocks, i.e., condensed attention neural block (CA) and dual adaptive neural block (DA). In brief, CA employs feature aggregation, attention computation, and feature recovery to efficiently capture the global dependency at the superpixel level. To embrace the pixel-wise global dependency, DA takes a novel dual-way structure to adaptively encapsulate the globality from superpixels into pixels. Thanks to the two neural blocks, our method achieves comparable performance while taking only similar to 6% FLOPs compared with SwinIR.

关键词： Low-level vision

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vision Transformers are Parameter-Efficient Audio-Visual Learners

Vision Transformers are Parameter-Efficient Audio-Visual Lea...

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

作者： Lin, Yan-Bo Sung, Yi-Lin Lei, Jie Bansal, Mohit Bertasius, Gedas UNC Chapel Hill Dept Comp Sci Chapel Hill NC 27514 USA

ISBN: (纸本)9798350301298

vision transformers (ViTs) have achieved impressive results on various computer vision tasks in the last several years. In this work, we study the capability of frozen ViTs, pretrained only on visual data, to generalize to audio-visual data without finetuning any of its original parameters. To do so, we propose a latent audio-visual hybrid (LAVISH) adapter that adapts pretrained ViTs to audio-visual tasks by injecting a small number of trainable parameters into every layer of a frozen ViT. To efficiently fuse visual and audio cues, our LAVISH adapter uses a small set of latent tokens, which form an attention bottleneck, thus, eliminating the quadratic cost of standard cross-attention. Compared to the existing modality-specific audio-visual methods, our approach achieves competitive or even better performance on various audio-visual tasks while using fewer tunable parameters and without relying on costly audio pretraining or external audio encoders. Our code is available at https://***/project_page/LAVISH/

关键词： Multi-modal learning

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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...

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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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Automatic recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor

Automatic Recognition of Food Ingestion Environment from the...

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

作者： Huang, Yuning Hassan, M. A. He, Jiangpeng Higgins, J. McCrory, Megan Eicher-Miller, Heather Thomas, J. Graham Sazonov, Edward Zhu, Fengqing Purdue Univ W Lafayette IN 47907 USA Univ Calif Davis Davis CA 95616 USA Univ Colorado Aurora CO USA Boston Univ Boston MA 02215 USA Brown Univ Providence RI 02912 USA Univ Alabama Tuscaloosa AL USA

ISBN: (纸本)9798350365474

Detecting an ingestion environment is an important aspect of monitoring dietary intake. It provides insightful information for dietary assessment. However, it is a challenging problem where human-based reviewing can be tedious, and algorithm-based review suffers from data imbalance and perceptual aliasing problems. To address these issues, we propose a neural network-based method with a two-stage training framework that tactfully combines fine-tuning and transfer learning techniques. Our method is evaluated on a newly collected dataset called "UA Free Living Study", which uses an egocentric wearable camera, AIM-2 sensor, to simulate food consumption in free-living conditions. The proposed training framework is applied to common neural network backbones, combined with approaches in the general imbalanced classification field. Experimental results on the collected dataset show that our proposed method for automatic ingestion environment recognition successfully addresses the challenging data imbalance problem in the dataset and achieves a promising overall classification accuracy of 96.63%.

关键词： Classification Dietary Assessment Scene recognition Transfer Learning

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