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Doubly Right Object recognition: A Why Prompt for Visual Rationales

Doubly Right Object Recognition: A Why Prompt for Visual Rat...

引用

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

作者： Mao, Chengzhi Teotial, Revant Sundari, Amrutha Menon, Sachit Yang, Junfeng Wang, Xin Vondrick, Carl Columbia Univ New York NY 10027 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9798350301298

Many visual recognition models are evaluated only on their classification accuracy, a metric for which they obtain strong performance. In this paper, we investigate whether computer vision models can also provide correct rationales for their predictions. We propose a "doubly right" object recognition benchmark, where the metric requires the model to simultaneously produce both the right labels as well as the right rationales. We find that state-of-the-art visual models, such as CLIP, often provide incorrect rationales for their categorical predictions. However, by transferring the rationales from language models into visual representations through a tailored dataset, we show that we can learn a "why prompt," which adapts large visual representations to produce correct rationales. Visualizations and empirical experiments show that our prompts significantly improve performance on doubly right object recognition, in addition to zero-shot transfer to unseen tasks and datasets.

关键词： Explainable computer vision

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No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation

No Time to Train: Empowering Non-Parametric Networks for Few...

引用

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

作者： Zhu, Xiangyang Zhang, Renrui He, Bowei Guo, Ziyu Liu, Jiaming Xiao, Han Fu, Chaoyou Dong, Hao Gao, Peng City Univ Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China Peking Univ Beijing Peoples R China Tencent Youtu Lab Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot segmentation methods first pre-train models on 'seen' classes, and then evaluate their generalization performance on 'unseen' classes. However, the prior pre-training stage not only introduces excessive time over-head but also incurs a significant domain gap on 'unseen' classes. To tackle these issues, we propose a Non-parametric Network for few-shot 3D Segmentation, SegNN, and its Parametric variant, Seg-PN. Without training, Seg-NN extracts dense representations by hand-crafted filters and achieves comparable performance to existing parametric models. Due to the elimination of pre-training, SegNN can alleviate the domain gap issue and save a substantial amount of time. Based on Seg-NN, Seg-PN only requires training a lightweight QUEry-Support Transferring (QUEST) module, which enhances the interaction between the support set and query set. Experiments suggest that Seg-PN outperforms previous state-of-the-art method by +4.19% and +7.71% mIoU on S3DIS and ScanNet datasets respectively, while reducing training time by -90%, indicating its effectiveness and efficiency. Code is available here.

关键词： 3D Segmentation 3D vision Few-shot Learning

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Robustness and Adaptation to Hidden Factors of Variation

Robustness and Adaptation to Hidden Factors of Variation

引用

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

作者： Paul, William Burlina, Philippe Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We tackle here a specific, still not widely addressed aspect, of AI robustness, which consists of seeking invariance / insensitivity of model performance to hidden factors of variations in the data. Towards this end, we employ a two step strategy that a) does unsupervised discovery, via generative models, of sensitive factors that cause models to under-perform, and b) intervenes models to make their performance invariant to these sensitive factors' influence. We consider 3 separate interventions for robustness, including: data augmentation, semantic consistency, and adversarial alignment. We evaluate our method using metrics that measure trade offs between invariance (insensitivity) and overall performance (utility) and show the benefits of our method for 3 settings (unsupervised, semi-supervised and generalization).

关键词： Measurement computer vision conferences Semantics Robustness Data models pattern recognition

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FairCLIP: Harnessing Fairness in vision-Language Learning

FairCLIP: Harnessing Fairness in Vision-Language Learning

引用

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

作者： Luol, Yan Shil, Min Khan, Muhammad Osama Afzal, Muhammad Muneeb Huang, Hao Yuan, Shuaihang Tian, Yu Song, Luo Kouhana, Ava Elze, Tobias Fang, Yi Wang, Mengyu Harvard Univ Harvard Ophthalmol AI Lab Cambridge MA 02138 USA NYU Tandon Sch Engn New York NY USA New York Univ Abu Dhabi Multimedia & Visual Comp Lab Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350353006

Fairness is a critical concern in deep learning, especially in healthcare, where these models influence diagnoses and treatment decisions. Although fairness has been investigated in the vision-only domain, the fairness of medical vision-language (VL) models remains unexplored due to the scarcity of medical VL datasets for studying fairness. To bridge this research gap, we introduce the first fair vision-language medical dataset (Harvard-FairVLMed) that provides detailed demographic attributes, ground-truth labels, and clinical notes to facilitate an in-depth examination of fairness within VL foundation models. Using Harvard-FairVLMed, we conduct a comprehensive fairness analysis of two widely-used VL models (CLIP and BLIP2), pre-trained on both natural and medical domains, across four different protected attributes. Our results highlight significant biases in all VL models, with Asian, Male, Non-Hispanic, and Spanish being the preferred subgroups across the protected attributes of race, gender, ethnicity, and language, respectively. In order to alleviate these biases, we propose FairCLIP, an optimal-transport-based approach that achieves a favorable trade-off between performance and fairness by reducing the Sinkhorn distance between the overall sample distribution and the distributions corresponding to each demographic group. As the first VL dataset of its kind, Harvard-FairVLMed holds the potential to catalyze advancements in the development of machine learning models that are both ethically aware and clinically effective. Our dataset and code are available at https://***/datasets/

关键词： AI for Medicine Equitable Deep Learning Fairness Learning Large Language Models Medical AI Ophthalmology Optimal Transport vision-Language Learning

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BEHAVIOR vision Suite: Customizable Dataset Generation via Simulation

BEHAVIOR Vision Suite: Customizable Dataset Generation via S...

引用

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

作者： Ge, Yunhao Tang, Yihe Xu, Jiashu Gokmen, Cem Li, Chengshu Ai, Wensi Martinez, Benjamin Jose Aydin, Arman Anvari, Mona Chakravarthy, Ayush K. Yu, Hong-Xing Wong, Josiah Srivastava, Sanjana Lee, Sharon Zhang, Shengxin Itti, Laurent Li, Yunzhu Martin-Martins, Roberto Liu, Miao Zhang, Pengchuan Zhang, Ruohan Fei-Fei, Li Wu, Jiajun Stanford Univ Stanford CA 94305 USA Univ Southern Calif Los Angeles CA 90007 USA Harvard Univ Cambridge MA 02138 USA Meta GenAI Menlo Pk CA USA Meta FAIR Menlo Pk CA USA Univ Texas Austin Austin TX USA Univ Illinois Urbana IL USA

ISBN: (纸本)9798350353006

The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels, which real-world vision datasets rarely satisfy. While current synthetic data generators offer a promising alternative, particularly for embodied AI tasks, they often fall short for computer vision tasks due to low asset and rendering quality, limited diversity, and unrealistic physical properties. We introduce the BEHAVIOR vision Suite (BVS), a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models, based on the newly developed embodied AI benchmark, BEHAVIOR-1K. BVS supports a large number of adjustable parameters at the scene level (e.g., lighting, object placement), the object level (e.g., joint configuration, attributes such as "filled" and "folded"), and the camera level (e.g., field of view, focal length). Researchers can arbitrarily vary these parameters during data generation to perform controlled experiments. We showcase three example application scenarios: systematically evaluating the robustness of models across different continuous axes of domain shift, evaluating scene understanding models on the same set of images, and training and evaluating simulation-to-real transfer for a novel vision task: unary and binary state prediction. Project website: https://***/

关键词： benchmarking embodied AI simulation synthetic data generation

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Behavioral Analysis of vision-and-Language Navigation Agents

Behavioral Analysis of Vision-and-Language Navigation Agents

引用

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

作者： Yang, Zijiao Majumdar, Arjun Lee, Stefan Oregon State Univ Corvallis OR 97331 USA Georgia Inst Technol Atlanta GA USA

ISBN: (纸本)9798350301298

To be successful, vision-and-Language Navigation (VLN) agents must be able to ground instructions to actions based on their surroundings. In this work, we develop a methodology to study agent behavior on a skill-specific basis - examining how well existing agents ground instructions about stopping, turning, and moving towards specified objects or rooms. Our approach is based on generating skill-specific interventions and measuring changes in agent predictions. We present a detailed case study analyzing the behavior of a recent agent and then compare multiple agents in terms of skill-specific competency scores. This analysis suggests that biases from training have lasting effects on agent behavior and that existing models are able to ground simple referring expressions. Our comparisons between models show that skill-specific scores correlate with improvements in overall VLN task performance.

关键词： language reasoning vision

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Three Pillars improving vision Foundation Model Distillation for Lidar

Three Pillars improving Vision Foundation Model Distillation...

引用

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

作者： Puy, Gilles Gidaris, Spyros Boulch, Alexandre Simeoni, Oriane Sautier, Corentin Perez, Patrick Bursucl, Andrei Marlet, Renaud Valeo ai Paris France Kyutai Paris France Univ Gustave Eiffel CNRS LIGM Ecole Ponts Marne La Vallee France

ISBN: (纸本)9798350353006

Self-supervised image backbones can be used to address complex 2D tasks (e.g., semantic segmentation, object discovery) very efficiently and with little or no downstream supervision. Ideally, 3D backbones for lidar should be able to inherit these properties after distillation of these powerful 2D features. The most recent methods for image-to-lidar distillation on autonomous driving data show promising results, obtained thanks to distillation methods that keep improving. Yet, we still notice a large performance gap when measuring by linear probing the quality of distilled vs fully supervised features. In this work, instead of focusing only on the distillation method, we study the effect of three pillars for distillation: the 3D backbone, the pretrained 2D backbone, and the pretraining 2D+3D dataset. In particular, thanks to our scalable distillation method named ScaLR, we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality. This allows us to significantly reduce the gap between the quality of distilled and fully-supervised 3D features, and to improve the robustness of the pretrained backbones to domain gaps and perturbations. The code is available at https://***/valeoai/ScaLR.

关键词： Semantic Segmentation

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SMM-Conv: Scalar Matrix Multiplication with Zero Packing for Accelerated Convolution

SMM-Conv: Scalar Matrix Multiplication with Zero Packing for...

引用

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

作者： Ofir, Amir Ben-Artzi, Gil Ariel Univ Ariel Israel

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a novel approach for accelerating convolutions during inference for CPU-based architectures. The most common method of computation involves packing the image into the columns of a matrix (im2col) and performing general matrix multiplication (GEMM) with a matrix of weights. This results in two main drawbacks: (a) im2col requires a large memory buffer and can experience inefficient memory access, and (b) while GEMM is highly optimized for scientific matrices multiplications, it is not well suited for convolutions. We propose an approach that takes advantage of scalar-matrix multiplication and reduces memory overhead. Our experiments with commonly used network architectures demonstrate a significant speedup compared to existing indirect methods.

关键词： Deep learning computer vision conferences Memory management Network architecture pattern recognition Kernel

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ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images

ChAda-ViT : Channel Adaptive Attention for Joint Representat...

引用

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

作者： Bourriez, Nicolas Bendidi, Ihab Cohen, Ethan Watkinson, Gabriel Sanchez, Maxime Bollot, Guillaume Genovesio, Auguste Ecole Normale Super PSL Paris France Minos Biosci Paris France Synsight Eviy France

ISBN: (纸本)9798350353006

Unlike color photography images, which are consistently encoded into RGB channels, biological images encompass various modalities, where the type of microscopy and the meaning of each channel varies with each experiment. Importantly, the number of channels can range from one to a dozen and their correlation is often comparatively much lower than RGB, as each of them brings specific information content. This aspect is largely overlooked by methods designed out of the bioimage field, and current solutions mostly focus on intra-channel spatial attention, often ignoring the relationship between channels, yet crucial in most biological applications. Importantly, the variable channel type and count prevent the projection of several experiments to a unified representation for large scale pre-training. In this study, we propose ChAda-ViT, a novel Channel Adaptive vision Transformer architecture employing an Inter-Channel Attention mechanism on images with an arbitrary number, order and type of channels. We also introduce IDR-Cell100k, a bioimage dataset with a rich set of 79 experiments covering 7 microscope modalities, with a multitude of channel types, and counts varying from 1 to 10 per experiment. Our architecture, trained in a self-supervised manner, outperforms existing approaches in several biologically relevant downstream tasks. Additionally, it can be used to bridge the gap for the first time between assays with different microscopes, channel numbers or types by embedding various image and experimental modalities into a unified biological image representation. The latter should facilitate interdisciplinary studies and pave the way for better adoption of deep learning in biological image-based analyses. [GRAPHICS] .

关键词： Channel Adaptive Channel Embeddings Computational Biology computer vision Deep Learning Inter-Channel Attention Long Context Microscopy Imaging Self Supervised Learning Transformers vision Transformers

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MV-TAL: Mulit-view Temporal Action Localization in Naturalistic Driving

MV-TAL: Mulit-view Temporal Action Localization in Naturalis...

引用

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

作者： Li, Wei Chen, Shimin Gu, Jianyang Wang, Ning Chen, Chen Guo, Yandong OPPO Res Inst Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China East China Univ Sci & Technol Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human risky behavior in driving is an important visual recognition problem. In this paper, we propose a multi-view temporal action localization system based on the grayscale video to achieve action recognition in naturalistic driving. Specifically, we adopted SwinTransformer as feature extractor, and a single framework to detect boundary and class at the same time. Also, we improve multiple loss function for explicit constraints of embedded feature distributions. Our proposed framework achieves the overall F1 -score of 0.3154 on A2 dataset.

关键词： Location awareness Measurement Visualization computer vision conferences Aggregates Gray-scale

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