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共 20951 条记录，以下是51-60 订阅

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Learning Group Activity Features Through Person Attribute Prediction

Learning Group Activity Features Through Person Attribute Pr...

引用

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

作者： Nakatani, Chihiro Kawashima, Hiroaki Ukita, Norimichi Toyota Technol Inst Toyota Japan Univ Hyogo Himeji Hyogo Japan

ISBN: (纸本)9798350353006

This paper proposes Group Activity Feature (GAF) learning in which features of multi-person activity are learned as a compact latent vector. Unlike prior work in which the manual annotation of group activities is required for supervised learning, our method learns the GAF through person attribute prediction without group activity annotations. By learning the whole network in an end-to-end manner so that the GAF is required for predicting the person attributes of people in a group, the GAF is trained as the features of multi-person activity. As a person attribute, we propose to use a person's action class and appearance features because the former is easy to annotate due to its simpleness, and the latter requires no manual annotation. In addition, we introduce a location-guided attribute prediction to disentangle the complex GAF for extracting the features of each target person properly. Various experimental results validate that our method outperforms SOTA methods quantitatively and qualitatively on two public datasets. Visualization of our GAF also demonstrates that our method learns the GAF representing fined-grained group activity classes. Code: https://***/chihina/GAFL-cvpr2024.

关键词： group activity recognition representation learning self-supervised learning

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Token Transformation Matters: Towards Faithful Post-hoc Explanation for vision Transformer

Token Transformation Matters: Towards Faithful Post-hoc Expl...

引用

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

作者： Wu, Junyi Duan, Bin Kang, Weitai Tang, Hao Yan, Yan IIT Dept Comp Sci Chicago IL 60616 USA Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

While Transformers have rapidly gained popularity in various computer vision applications, post-hoc explanations of their internal mechanisms remain largely unexplored. vision Transformers extract visual information by representing image regions as transformed tokens and integrating them via attention weights. However, existing post-hoc explanation methods merely consider these attention weights, neglecting crucial information from the transformed tokens, which fails to accurately illustrate the rationales behind the models' predictions. To incorporate the influence of token transformation into interpretation, we propose TokenTM, a novel post-hoc explanation method that utilizes our introduced measurement of token transformation effects. Specifically, we quantify token transformation effects by measuring changes in token lengths and correlations in their directions pre- and post-transformation. Moreover, we develop initialization and aggregation rules to integrate both attention weights and token transformation effects across all layers, capturing holistic token contributions throughout the model. Experimental results on segmentation and perturbation tests demonstrate the superiority of our proposed TokenTM compared to state-of-the-art vision Transformer explanation methods.

关键词： Explainability Transformer

来源：评论

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RoDLA: Benchmarking the Robustness of Document Layout Analysis Models

RoDLA: Benchmarking the Robustness of Document Layout Analys...

引用

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

作者： Chen, Yufan Zhang, Jiaming Peng, Kunyu Zheng, Junwei Liu, Ruiping Torre, Philip Stiefelhagen, Rainer Karlsruhe Inst Technol Karlsruhe Germany Univ Oxford Oxford England

ISBN: (纸本)9798350353006

Before developing a Document Layout Analysis (DLA) model in real-world applications, conducting comprehensive robustness testing is essential. However, the robustness of DLA models remains underexplored in the literature. To address this, we are the first to introduce a robustness benchmark for DLA models, which includes 450K document images of three datasets. To cover realistic corruptions, we propose a perturbation taxonomy with 12 common document perturbations with 3 severity levels inspired by real-world document processing. Additionally, to better understand document perturbation impacts, we propose two metrics, Mean Perturbation Effect (mPE) for perturbation assessment and Mean Robustness Degradation (mRD) for robustness evaluation. Furthermore, we introduce a self-titled model, i.e., Robust Document Layout Analyzer (RoDLA), which improves attention mechanisms to boost extraction of robust features. Experiments on the proposed benchmarks (PubLayNet-P, DocLayNet-P, and M6Doc-P) demonstrate that RoDLA obtains state-of-the-art mRD scores of 115.7, 135.4, and 150.4, respectively. Compared to previous methods, RoDLA achieves notable improvements in mAP of +3.8%, +7.1% and +12.1%, respectively.

关键词： computer vision and pattern recognition Document Analysis Robustness

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Object recognition as Next Token Prediction

Object Recognition as Next Token Prediction

引用

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

作者： Yue, Kaiyu Chen, Bor-Chun Geiping, Jonas Li, Hengduo Goldstein, Tom Lim, Ser-Nam Meta Menlo Pk CA 94025 USA Univ Maryland College Pk MD 20742 USA ELLIS Inst Tubingen Germany MPI IS Tubingen Tubingen Germany Univ Cent Florida Orlando FL 32816 USA Meta AI Menlo Pk CA USA

ISBN: (纸本)9798350353006

We present an approach to pose object recognition as next token prediction. The idea is to apply a language decoder that auto-regressively predicts the text tokens from image embeddings to form labels. To ground this prediction process in auto-regression, we customize a non-causal attention mask for the decoder, incorporating two key features: modeling tokens from different labels to be independent, and treating image tokens as a prefix. This masking mechanism inspires an efficient method - one-shot sampling - to simultaneously sample tokens of multiple labels in parallel and rank generated labels by their probabilities during inference. To further enhance the efficiency, we propose a simple strategy to construct a compact decoder by simply discarding the intermediate blocks of a pretrained language model. This approach yields a decoder that matches the full model's performance while being notably more efficient. The code is available at ***/kaiyuyue/nxtp.

关键词： Decoding

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vision-and-Language Navigation via Causal Learning

Vision-and-Language Navigation via Causal Learning

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

作者： Wang, Liuyi He, Zongtao Dang, Ronghao Shen, Mengjiao Liu, Chengju Chen, Qijun Tongji Univ Sch Elect & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9798350353006

In the pursuit of robust and generalizable environment perception and language understanding, the ubiquitous challenge of dataset bias continues to plague vision-and-language navigation (VLN) agents, hindering their perfor-mance in unseen environments. This paper introduces the generalized cross-modal causal transformer (GOAT), a pioneering solution rooted in the paradigm of causal inference. By delving into both observable and unobservable confounders within vision, language, and history, we propose the back-door and front-door adjustment causal learning (BACL and FACL) modules to promote unbiased learning by comprehensively mitigating potential spurious correlations. Additionally, to capture global confounder features, we propose a cross-modal feature pooling (CFP) module supervised by contrastive learning, which is also shown to be effective in improving cross-modal representations during pre-training. Extensive experiments across multiple VLN datasets (R2R, REVERIE, RxR, and SOON) under-score the superiority of our proposed method over previous state-of-the-art approaches. Code is available at https://***/CrystalSixone/VLN-GOAT.

关键词： causal learning cross-modal fusion embodied AI vision-and-language vision-and-language navigation

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Making vision Transformers Truly Shift-Equivariant

Making Vision Transformers Truly Shift-Equivariant

引用

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

作者： Rojas-Gomez, Renan A. Lim, Teck-Yian Do, Minh N. Yeh, Raymond A. UIUC Dept Elect Engn Urbana IL 61801 USA UIUC VinUni Illinois Smart Hlth Ctr Urbana IL USA Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

ISBN: (纸本)9798350353013;9798350353006

In the field of computer vision, vision Transformers (ViTs) have emerged as a prominent deep learning architecture. Despite being inspired by Convolutional Neural Networks (CNNs), ViTs are susceptible to small spatial shifts in the input data - they lack shift-equivariance. To address this shortcoming, we introduce novel data-adaptive designs for each of the ViT modules that break shift-equivariance, such as tokenization, self-attention, patch merging, and positional encoding. With our proposed modules, we achieve perfect circular shift-equivariance across four prominent ViT architectures: Swin, SwinV2, CvT, and MViTv2. Additionally, we leverage our design to further enhance consistency under standard shifts. We evaluate our adaptive ViT models on image classification and semantic segmentation tasks. Our models achieve competitive performance across three diverse datasets, showcasing perfect (100%) circular shift consistency while improving standard shift consistency.(1)

关键词： shift equivariance shift invariance vision transformers

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Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matching

Hybrid Functional Maps for Crease-Aware Non-Isometric Shape ...

引用

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

作者： Bastian, Lennart Xie, Yizheng Navab, Nassir Laehner, Zorah Tech Univ Munich Munich Germany Univ Siegen Siegen Germany Univ Bonn Bonn Germany Lamarr Inst Bonn Germany

ISBN: (纸本)9798350353013;9798350353006

Non-isometric shape correspondence remains a fundamental challenge in computer vision. Traditional methods using Laplace-Beltrami operator (LBO) eigenmodes face limitations in characterizing high-frequency extrinsic shape changes like bending and creases. We propose a novel approach of combining the non-orthogonal extrinsic basis of eigenfunctions of the elastic thin-shell hessian with the intrinsic ones of the LBO, creating a hybrid spectral space in which we construct functional maps. To this end, we present a theoretical framework to effectively integrate non-orthogonal basis functions into descriptor- and learning-based functional map methods. Our approach can be incorporated easily into existing functional map pipelines across varying applications and can handle complex deformations beyond isometries. We show extensive evaluations across various supervised and unsupervised settings and demonstrate significant improvements. Notably, our approach achieves up to 15% better mean geodesic error for non-isometric correspondence settings and up to 45% improvement in scenarios with topological noise. Code is available at: https://***/

关键词： computer vision Functional Maps Non-isometric Shape Correspondence Shape Matching Topological Noise

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Classes Are Not Equal: An Empirical Study on Image recognition Fairness

Classes Are Not Equal: An Empirical Study on Image Recogniti...

引用

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

作者： Cui, Jiequan Zhu, Beier Wen, Xin Qi, Xiaojuan Yu, Bei Zhang, Hanwang Nanyang Technol Univ Singapore Singapore Univ Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353006

In this paper, we present an empirical study on image unfairness, i.e., extreme class accuracy disparity on balanced data like ImageNet. We demonstrate that are not equal and unfairness is prevalent for image classification models across various datasets, network and model capacities. Moreover, several intriguing properties of fairness are identified. First, the unfairness lies in problematic representation rather than classifier bias distinguished from long-tailed recognition. Second, with the proposed concept of Model Prediction Bias, investigate the origins of problematic representation training optimization. Our findings reveal that models tend to exhibit greater prediction biases for classes that more challenging to recognize. It means that more other will be confused with harder classes. Then the False (FPs) will dominate the learning in optimization, leading to their poor accuracy. Further, we conclude data augmentation and representation learning algorithms improve overall performance by promoting fairness some degree in image classification.

关键词： Fairness Long-tailed recognition Representation Learning vision-Language Models

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Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory

Accurate Training Data for Occupancy Map Prediction in Autom...

引用

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

作者： Kaelble, Jonas Wirges, Sascha Tatarchenko, Maxim Ilg, Eddy Bosch Ctr Artificial Intelligence Renningen Germany Saarland Univ Saarbrucken Germany

ISBN: (纸本)9798350353013;9798350353006

Automated driving fundamentally requires knowledge about the surrounding geometry of the scene. Modern approaches use only captured images to predict occupancy maps that represent the geometry. Training these approaches requires accurate data that may be acquired with the help of LiDAR scanners. We show that the techniques used for current benchmarks and training datasets to convert LiDAR scans into occupancy grid maps yield very low quality, and subsequently present a novel approach using evidence theory that yields more accurate reconstructions. We demonstrate that these are superior by a large margin, both qualitatively and quantitatively, and that we additionally obtain meaningful uncertainty estimates. When converting the occupancy maps back to depth estimates and comparing them with the raw LiDAR measurements, our method yields a MAE improvement of 30% to 52% on nuScenes and 53% on Waymo over other occupancy ground-truth data. Finally, we use the improved occupancy maps to train a state-of-the-art occupancy prediction method and demonstrate that it improves the MAE by 25% on nuScenes.

关键词： Automated Driving computer vision Occupancy Prediction

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Honeybee: Locality-enhanced Projector for Multimodal LLM

Honeybee: Locality-enhanced Projector for Multimodal LLM

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

作者： Cha, Junbum Kang, Wooyoung Mun, Jonghwan Roh, Byungseok Kakao Brain Seongnam South Korea

ISBN: (纸本)9798350353006

In Multimodal Large Language Models (MLLMs), a visual projector plays a crucial role in bridging pre-trained vision encoders with LLMs, enabling profound visual understanding while harnessing the LLMs' robust capabilities. Despite the importance of the visual projector, it has been relatively less explored. In this study, we first identify two essential projector properties: (i) flexibility in managing the number of visual tokens, crucial for MLLMs' overall efficiency, and (ii) preservation of local context from visual features, vital for spatial understanding. Based on these findings, we propose a novel projector design that is both flexible and locality-enhanced, effectively satisfying the two desirable properties. Additionally, we present comprehensive strategies to effectively utilize multiple and multifaceted instruction datasets. Through extensive experiments, we examine the impact of individual design choices. Finally, our proposed MLLM, Honeybee, remarkably outperforms previous state-of-the-art methods across various benchmarks, including MME, MMBench, SEED-Bench, and LLaVA-Bench, achieving significantly higher efficiency. Code and models are available at https://github. com/kakaobrain/honeybee.

关键词： Multimodal LLM vision-Language

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