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Dual Memory Networks: A Versatile Adaptation Approach for vision-Language Models

Dual Memory Networks: A Versatile Adaptation Approach for Vi...

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

作者： Zhang, Yabin Zhu, Wenjie Tang, Hui Ma, Zhiyuan Zhou, Kaiyang Zh, Lei HKPolyU Hong Kong Peoples R China OPPO Hong Kong Peoples R China HKUST Hong Kong Peoples R China HKBU Hong Kong Peoples R China

ISBN: (纸本)9798350353006

With the emergence of pre-trained vision-language models like CLIP, how to adapt them to various downstream classification tasks has garnered significant attention in recent research. The adaptation strategies can be typically categorized into three paradigms: zero-shot adaptation, few-shot adaptation, and the recently-proposed training-free few-shot adaptation. Most existing approaches are tailored for a specific setting and can only cater to one or two of these paradigms. In this paper, we introduce a versatile adaptation approach that can effectively work under all three settings. Specifically, we propose the dual memory networks that comprise dynamic and static memory components. The static memory caches training data knowledge, enabling training-free few-shot adaptation, while the dynamic memory preserves historical test features online during the testing process, allowing for the exploration of additional data insights beyond the training set. This novel capability enhances model performance in the few-shot setting and enables model usability in the absence of training data. The two memory networks employ the same flexible memory interactive strategy, which can operate in a training-free mode and can be further enhanced by incorporating learnable projection layers. Our approach is tested across 11 datasets under the three task settings. Remarkably, in the zero-shot scenario, it outperforms existing methods by over 3% and even shows superior results against methods utilizing external training data. Additionally, our method exhibits robust performance against natural distribution shifts. Codes are available at https://***/YBZh/DMN.

关键词： dual memory networks versatile adaptation vision-language models

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SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection

SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object D...

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

作者： Liu, Mingxuan Hayes, Tyler L. Ricci, Elisa Csurka, Gabriela Volpi, Riccardo Univ Trento Trento Italy NAVER LABS Europe Meylan France Fdn Bruno Kessler Trento Italy

ISBN: (纸本)9798350353006

Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task, empowering users to freely define their class vocabularies of interest during inference. However, our initial investigation indicates that existing OvOD detectors exhibit significant variability when dealing with vocabularies across various semantic granularities, posing a concern for real-world deployment. To this end, we introduce Semantic Hierarchy Nexus (SHiNe), a novel classifier that uses semantic knowledge from class hierarchies. It runs offline in three steps: i) it retrieves relevant super-/sub-categories from a hierarchy for each target class;ii) it integrates these categories into hierarchy-aware sentences;iii) it fuses these sentence embeddings to generate the nexus classifier vector. Our evaluation on various detection benchmarks demonstrates that SHiNe enhances robustness across diverse vocabulary granularities, achieving up to +31.9% mAP50 with ground truth hierarchies, while retaining improvements using hierarchies generated by large language models. Moreover, when applied to open-vocabulary classification on ImageNet-1k, SHiNe improves the CLIP zero-shot baseline by +2.8% accuracy. SHiNe is training-free and can be seamlessly integrated with any off-the-shelf OvOD detector, without incurring additional computational overhead during inference. The code is open source.

关键词： Object Detection Open-vocabulary vision-Language

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HALLUSIONBENCH: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large vision-Language Models

HALLUSIONBENCH: An Advanced Diagnostic Suite for Entangled L...

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

作者： Guan, Tianrui Liu, Fuxiao Wu, Xiyang Xian, Ruiqi Li, Zongxia Liu, Xiaoyu Wang, Xijun Chen, Lichang Huang, Furong Yacoob, Yaser Manocha, Dinesh Zhou, Tianyi Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350353006

We introduce "HALLUSIONBENCH(1)," a comprehensive benchmark designed for the evaluation of image-context reasoning. This benchmark presents significant challenges to advanced large visual-language models (LVLMs), such as GPT-4V(ision), Gemini Pro vision, Claude 3, and LLaVA-1.5, by emphasizing nuanced understanding and interpretation of visual data. The benchmark comprises 346 images paired with 1129 questions, all meticulously crafted by human experts. We introduce a novel structure for these visual questions designed to establish control groups. This structure enables us to conduct a quantitative analysis of the models' response tendencies, logical consistency, and various failure modes. In our evaluation on HALLUSIONBENCH, we benchmarked 15 different models, highlighting a 31.42% question-pair accuracy achieved by the state-of-the-art GPT-4V. Notably, all other evaluated models achieve accuracy below 16%. Moreover, our analysis not only highlights the observed failure modes, including language hallucination and visual illusion but also deepens an understanding of these pitfalls. Our comprehensive case studies within HALLUSIONBENCH shed light on the challenges of hallucination and illusion in LVLMs. Based on these insights, we suggest potential pathways for their future improvement. The benchmark and codebase can be accessed at https://***/tianyi-lab/HallusionBench.

关键词： Evaluation Dataset Hallucination vision language model VLM Evaluation

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NTIRE 2024 Challenge on Night Photography Rendering

NTIRE 2024 Challenge on Night Photography Rendering

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

作者： Ershov, Egor Panshin, Artyom Karasev, Oleg Korchagin, Sergey Lev, Shepelev Startsev, Alexandr Vladimirov, Daniil Zaychenkova, Ekaterina Banic, Nikola Iarchuk, Dmitrii R. Efimova, Maria Timofte, Radu Terekhin, Arseniy Kharkevich Inst Inst Informat Transmiss Problems Moscow Russia Artifitial Intelligence Res Inst AIRI Moscow Russia Gideon Bros Osijek Croatia Swiss Fed Inst Technol Zurich Zurich Switzerland Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9798350365474

This paper presents a review of the NTIRE 2024 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year's competition, the challenge images were collected with a mobile phone and the speed of algorithms was also measured alongside the quality of their output. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. There were 2 nominations: quality and efficiency. Top 5 solutions in terms of output quality were sorted by evaluation time (see Fig. 1). The top ranking participants' solutions effectively represent the state-of-the-art in nighttime photography rendering. More results can be found at https://***.

关键词： Photography

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GeoChat : Grounded Large vision-Language Model for Remote Sensing

GeoChat : Grounded Large Vision-Language Model for Remote Se...

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

作者： Kuckreja, Kartik Danish, Muhammad Sohail Naseer, Muzammal Das, Abhijit Khan, Salman Khan, Fahad Shahbaz Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Birla Inst Technol & Sci Hyderabad India Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Recent advancements in Large vision-Language Models (VLMs) have shown great promise in natural image domains, allowing users to hold a dialogue about given visual content. However, such general-domain VLMs perform poorly for Remote Sensing (RS) scenarios, leading to inaccurate or fabricated information when presented with RS domain-specific queries. Such a behavior emerges due to the unique challenges introduced by RS imagery. For example, to handle high-resolution RS imagery with diverse scale changes across categories and many small objects, region-level reasoning is necessary alongside holistic scene interpretation. Furthermore, the lack of domain-specific multimodal instruction following data as well as strong backbone models for RS make it hard for the models to align their behavior with user queries. To address these limitations, we propose GeoChat - the first versatile remote sensing VLM that offers multitask conversational capabilities with high-resolution RS images. Specifically, GeoChat can not only answer image-level queries but also accepts region inputs to hold region-specific dialogue. Furthermore, it can visually ground objects in its responses by referring to their spatial coordinates. To address the lack of domain-specific datasets, we generate a novel RS multi-modal instruction-following dataset by extending image-text pairs from existing diverse RS datasets. We establish a comprehensive benchmark for RS multitask conversations and compare with a number of baseline methods. GeoChat demonstrates robust zero-shot performance on various RS tasks, e.g., image and region captioning, visual question answering, scene classification, visually grounded conversations and referring detection. Our code is available here.

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Using Language-Aligned Gesture Embeddings for Understanding Gestures Accompanying Math Terms

Using Language-Aligned Gesture Embeddings for Understanding ...

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

作者： Maidment, Tristan Patel, Purav J. Walker, Erin Kovashka, Adriana Univ Pittsburgh Intelligent Syst Program Pittsburgh PA 15260 USA Univ Pittsburgh Comp Sci Pittsburgh PA 15260 USA Univ Pittsburgh Learning Res & Dev Ctr Pittsburgh PA 15260 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350365474

In this paper, we introduce an approach for recognizing and classifying gestures that accompany mathematical terms, in a new collection we name the "GAMT" dataset. Our method uses language as a means of providing context to classify gestures. Specifically, we use a CLIP-style framework to construct a shared embedding space for gestures and language, experimenting with various methods for encoding gestures within this space. We evaluate our method on our new dataset containing a wide array of gestures associated with mathematical terms. The shared embedding space leads to a substantial improvement in gesture classification. Furthermore, we identify an efficient model that excelled at classifying gestures from our unique dataset, thus contributing to the further development of gesture recognition in diverse interaction scenarios.

关键词： action clip cnn comunication dataset embeddings gesture hci language-aligned multimodal non-verbal recognition semantic tcn transformers

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Text-image Alignment for Diffusion-based Perception

Text-image Alignment for Diffusion-based Perception

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

作者： Kondapanenil, Neehar Marksl, Markus Knott, Manuel Guimaraes, Rogerio Perona, Pietro CALTECH Pasadena CA 91125 USA Swiss Data Sci Ctr Empa ETH Zurich Zurich Switzerland

ISBN: (纸本)9798350353006

Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However, the best way to harness the perceptual knowledge of these generative models for visual tasks is still an open question. Specifically, it is unclear how to use the prompting interface when applying diffusion backbones to vision tasks. We find that automatically generated captions can improve text-image alignment and significantly enhance a model's cross-attention maps, leading to better perceptual performance. Our approach improves upon the current state-of-the-art (SOTA) in diffusion-based semantic segmentation on ADE20K and the current overall SOTA for depth estimation on NYUv2. Furthermore, our method generalizes to the cross-domain setting. We use model personalization and caption modifications to align our model to the target domain and find improvements over unaligned baselines. Our cross-domain object detection model, trained on Pascal VOC, achieves SOTA results on Watercolor2K. Our cross-domain segmentation method, trained on Cityscapes, achieves SOTA results on Dark Zurich-val and Nighttime Driving. Project page: ***/TADP/Code page: ***/damaggu/TADP

关键词： ADE20K Alignment Cityscapes Cross-Attention Cross-Domain Depth Estimation Diffusion Domain Shift Generative Models Model Personalization NYUv2 Object Detection Segmentation vision-Language Models

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Classifier Guided Cluster Density Reduction for Dataset Selection

Classifier Guided Cluster Density Reduction for Dataset Sele...

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

作者： Chang, Cheng Long, Keyu Li, Zijian Rai, Himanshu Layer 6 AI Toronto ON Canada

ISBN: (纸本)9798350365474

In this paper, we address the challenge of selecting an optimal dataset from a source pool with annotations to enhance performance on a target dataset derived from a different source. This is important in scenarios where it is hard to afford on-the-fly dataset annotation and is also the theme of the second Visual Data Understanding (VDU) Challenge. Our solution, the Classifier Guided Cluster Density Reduction (CCDR) framework, operates in two stages. Initially, we employ a filtering technique to identify images that align with the target dataset's distribution. Subsequently, we implement a graph-based cluster density reduction method, steered by a classifier that approximates the distance between the target distribution and source distribution. This classifier is trained to distinguish between images that resemble the target dataset and those that do not, facilitating the pruning process shown in Figure 1. Our approach maintains a balance between selecting pertinent images that match the target distribution and eliminating redundant ones that do not contribute to the enhancement of the detection model. We demonstrate the superiority of our method over various baselines in object detection tasks, particularly in optimizing the training set distribution on the region100 dataset. We have released our code here: https://***/ himsR/DataCVChallenge-2024/tree/main

关键词： computer vision Data Search deep learning domain Transfer

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View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network

View-decoupled Transformer for Person Re-identification unde...

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

作者： Zhang, Quan Wang, Lei Patel, Vishal M. Xie, Xiaohua Lai, Jianhuang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Pazhou Lab HuangPu Guangzhou Guangdong Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD USA

ISBN: (纸本)9798350353006

Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. However, as a more practical scenario, aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID, the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features, namely hierarchical subtractive separation and orthogonal loss, where the former separates these two features inside the VDT, and the latter constrains these two to be independent. In addition, we contribute a large-scale AGPReID dataset called CARGO, consisting of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID, keeping the same magnitude of computational complexity. Our project is available at https://***/LinlyAC/VDT-AGPReID.

关键词： image recognition person re-identification transformer

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Semantic-Aware Multi-Label Adversarial Attacks

Semantic-Aware Multi-Label Adversarial Attacks

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

作者： Mahmood, Hassan El Hamifar, Ehsan Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

Despite its importance, generating attacks for multi-label learning (MLL) models has received much less attention compared to multi-class recognition. Attacking an MLL model by optimizing a loss on the target set of labels has often the undesired consequence of changing the predictions for other labels. On the other hand, adding a loss on the remaining labels to keep them fixed leads to highly negatively correlated gradient directions, reducing the attack effectiveness. In this paper, we develop a framework for crafting effective and semantic-aware adversarial attacks for MLL. First, to obtain an attack that leads to semantically consistent predictions across all labels, we find a minimal superset of the target labels, referred to as consistent target set. To do so, we develop an efficient search algorithm over a knowledge graph, which encodes label dependencies. Next, we propose an optimization that searches for an attack that modifies the predictions of labels in the consistent target set while ensuring other labels will not get affected. This leads to an efficient algorithm that projects the gradient of the consistent target set loss onto the orthogonal direction of the gradient of the loss on other labels. Our framework can generate attacks on different target set sizes and for MLL with thousands of labels (as in OpenImages). Finally, by extensive experiments on three datasets and several MLL models, we show that our method generates both successful and semantically consistent attacks.(1)

关键词： Adversarial Attacks Adversarial Robustness Multi-label Attacks Multi-label recognition Semantic Attacks

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