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Contrasting intra-modal and ranking cross-modal hard negatives to enhance visio-linguistic compositional understanding

Contrasting intra-modal and ranking cross-modal hard negativ...

引用

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

作者： Zhang, Le Awal, Rabiul Agrawal, Aishwarya Mila Quebec AI Inst Montreal PQ Canada Univ Montreal Montreal PQ Canada Canada CIFAR AI Chair Montreal PQ Canada

ISBN: (纸本)9798350353006

vision-Language Models (VLMs), such as CLIP, exhibit strong image-text comprehension abilities, facilitating advances in several downstream tasks such as zero-shot image classification, image-text retrieval, and text-to-image generation. However, the compositional reasoning abilities of existing VLMs remains subpar. The root of this limitation lies in the inadequate alignment between the images and captions in the pretraining datasets. Additionally, the current contrastive learning objective fails to focus on fine-grained grounding components like relations, actions, and attributes, resulting in "bag-of-words" representations. We introduce a simple and effective method to improve compositional reasoning in VLMs. Our method better leverages available datasets by refining and expanding the standard image-text contrastive learning framework. Our approach does not require specific annotations and does not incur extra parameters. When integrated with CLIP, our technique yields notable improvement over state-of-the-art baselines across five vision-language compositional benchmarks.(1)

关键词： compositional understanding contrastive learning vision-language models

来源：评论

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Learning Correlation Structures for vision Transformers

Learning Correlation Structures for Vision Transformers

引用

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

作者： Kim, Manjin Seo, Paul Hongsuck Schmid, Cordelia Cho, Minsu POSTECH Pohang South Korea Korea Univ Seoul South Korea Google Res Mountain View CA USA

ISBN: (纸本)9798350353006

We introduce a new attention mechanism, dubbed structural self-attention (StructSA), that leverages rich correlation patterns naturally emerging in key-query interactions of attention. StructSA generates attention maps by recognizing space-time structures of key-query correlations via convolution and uses them to dynamically aggregate local contexts of value features. This effectively leverages rich structural patterns in images and videos such as scene layouts, object motion, and inter-object relations. Using StructSA as a main building block, we develop the structural vision transformer (StructViT) and evaluate its effectiveness on both image and video classification tasks, achieving state-of-the-art results on ImageNet-1K, Kinetics-400, Something-Something V1 & V2, Diving-48, and FineGym.

关键词： correlation modeling image classification self-attention video classification vision Transformers visual representation learning

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VLP: vision Language Planning for Autonomous Driving

VLP: Vision Language Planning for Autonomous Driving

引用

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

作者： Pan, Chenbin Yaman, Burhaneddin Nesti, Tommaso Mallik, Abhirup Allievi, Alessandro G. Velipasalar, Senem Rene, Liu Syracuse Univ Syracuse NY USA Bosch Res North Amer & Bosch Ctr Artificial Intel Sunnyvale CA 94085 USA

ISBN: (纸本)9798350353006

Autonomous driving is a complex and challenging task that aims at safe motion planning through scene understanding and reasoning. While vision-only autonomous driving methods have recently achieved notable performance, through enhanced scene understanding, several key issues, including lack of reasoning, low generalization performance and long-tail scenarios, still need to be addressed. In this paper, we present VLP, a novel vision-Language-Planning framework that exploits language models to bridge the gap between linguistic understanding and autonomous driving. VLP enhances autonomous driving systems by strengthening both the source memory foundation and the self-driving car's contextual understanding. VLP achieves state-of-the-art end-to-end planning performance on the challenging NuScenes dataset by achieving 35.9% and 60.5% reduction in terms of average L2 error and collision rates, respectively, compared to the previous best method. Moreover, VLP shows improved performance in challenging long-tail scenarios and strong generalization capabilities when faced with new urban environments.

关键词： Visual languages

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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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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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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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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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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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De-Diffusion Makes Text a Strong Cross-Modal Interface

De-Diffusion Makes Text a Strong Cross-Modal Interface

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

作者： Wei, Chen Liu, Chenxi Qi, Siyuan Zhang, Zhishuai Yuille, Alan Yu, Jiahui Google DeepMind London England Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9798350353006

We demonstrate text as a strong cross-modal interface. Rather than relying on deep embeddings to connect image and language as the interface representation, our approach represents an image as text, from which we enjoy the interpretability and flexibility inherent to natural language. We employ an autoencoder that uses a pre-trained text-to-image diffusion model for decoding. The encoder is trained to transform an input image into text, which is then fed into the fixed text- to-image diffusion decoder to reconstruct the original input - a process we term De- Diffusion. Experiments validate both the precision and comprehensiveness of De-Diffusion text representing images, such that it can be readily ingested by off-the-shelf text-to-image tools and LLMs for diverse multi-modal tasks. For example, a single De-Diffusion model can generalize to provide transferable prompts for different text-to-image tools, and also achieves a new state of the art on open-ended vision-language tasks by simply prompting large language models with few-shot examples. Project page: ***.

关键词： Diffusion Generative Model vision and Language

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PEEKABOO: Interactive Video Generation via Masked-Diffusion

PEEKABOO: Interactive Video Generation via Masked-Diffusion

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

作者： Jain, Yash Nasery, Anshul Vineet, Vibhav Behl, Harkirat Microsoft Redmond WA 98052 USA Univ Washington Seattle WA USA

ISBN: (纸本)9798350353013;9798350353006

Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However, a significant limitation is their inability to offer interactive control to users, a feature that promises to open up unprecedented applications and creativity. In this work, we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control. We present PEEKABOO, a novel masked attention module, which seamlessly integrates with current video generation models offering control without the need for additional training or inference overhead. To facilitate future research, we also introduce a comprehensive benchmark for interactive video generation. This benchmark offers a standardized framework for the community to assess the efficacy of emerging interactive video generation models. Our extensive qualitative and quantitative assessments reveal that PEEKABOO achieves up to a 3.8x improvement in mIoU over baseline models, all while maintaining the same latency. Code and benchmark are available on the webpage.

关键词： computer vision diffusion interactive text to video video generation

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