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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11888 条记录，以下是31-40 订阅

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Style Aligned Image Generation via Shared Attention

Style Aligned Image Generation via Shared Attention

引用

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

作者： Hertz, Amir Voynov, Andrey Fruchter, Shlomi Cohen-Or, Daniel Google Res Mountain View CA 94043 USA Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9798350353013;9798350353006

Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields, generating visually compelling outputs from textual prompts. However, controlling these models to ensure consistent style remains challenging, with existing methods necessitating fine-tuning and manual intervention to disentangle content and style. In this paper, we introduce StyleAligned, a novel technique designed to establish style alignment among a series of generated images. By employing minimal 'attention sharing' during the diffusion process, our method maintains style consistency across images within T2I models. This approach allows for the creation of style-consistent images using a reference style through a straightforward inversion operation. Our method's evaluation across diverse styles and text prompts demonstrates high-quality synthesis and fidelity, underscoring its efficacy in achieving consistent style across various inputs.

关键词： computer vision Generative models Machine Learning Style Transfer

来源：评论

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Adapters Strike Back

Adapters Strike Back

引用

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

作者： Steitz, Jan-Martin Roth, Stefan Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany Hessian AI Darmstadt Germany

ISBN: (纸本)9798350353006

Adapters provide an efficient and lightweight mechanism for adapting trained transformer models to a variety of different tasks. However, they have often been found to be outperformed by other adaptation mechanisms including low-rank adaptation. In this paper, we provide an in-depth study of adapters, their internal structure, as well as various implementation choices. We uncover pitfalls for using adapters and suggest a concrete, improved adapter architecture, called Adapter+, that not only outperforms previous adapter implementations but surpasses a number of other, more complex adaptation mechanisms in several challenging settings. Despite this, our suggested adapter is highly robust and, unlike previous work, requires little to no manual intervention when addressing a novel scenario. Adapter+ reaches state-of-the-art average accuracy on the VTAB benchmark, even without a per-task hyperparameter optimization.(1)

关键词： adapters deep learning image classification parameter-efficient transfer learning transfer learning vision transformer

来源：评论

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Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domains

Blur2Blur: Blur Conversion for Unsupervised Image Deblurring...

引用

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

作者： Bang-Dang Pham Phong Tran Anh Tran Cuong Pham Rang Nguyen Minh Hoai VinAI Res Hanoi Vietnam MBZUAI Abu Dhabi U Arab Emirates Posts & Telecommun Inst Tech Hanoi Vietnam Univ Adelaide Adelaide SA Australia

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013;9798350353006

This paper presents an innovative framework designed to train an image deblurring algorithm tailored to a specific camera device. This algorithm works by transforming a blurry input image, which is challenging to deblur, into another blurry image that is more amenable to deblurring. The transformation process, from one blurry state to another, leverages unpaired data consisting of sharp and blurry images captured by the target camera device. Learning this blur-to-blur transformation is inherently simpler than direct blur-to-sharp conversion, as it primarily involves modifying blur patterns rather than the intricate task of reconstructing fine image details. The efficacy of the proposed approach has been demonstrated through comprehensive experiments on various benchmarks, where it significantly outperforms state-of-the-art methods both quantitatively and qualitatively. Our code and data are available at https://***/VinAIResearch/Blur2Blur

关键词： computer vision Codes Benchmark testing Cameras Image restoration pattern recognition Image reconstruction

来源：评论

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Visual Concept Connectome (VCC): Open World Concept Discovery and their Interlayer Connections in Deep Models

Visual Concept Connectome (VCC): Open World Concept Discover...

引用

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

作者： Kowal, Matthew Wildes, Richard P. Derpanis, Konstantinos G. York Univ Toronto ON Canada Samsung AI Ctr Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9798350353006

Understanding what deep network models capture in their learned representations is a fundamental challenge in computer vision. We present a new methodology to understanding such vision models, the Visual Concept Connectome (VCC), which discovers human interpretable concepts and their interlayer connections in a fully unsupervised manner. Our approach simultaneously reveals fine-grained concepts at a layer, connection weightings across all layers and is amendable to global analysis of network structure (e.g. branching pattern of hierarchical concept assemblies). Previous work yielded ways to extract interpretable concepts from single layers and examine their impact on classification, but did not afford multilayer concept analysis across an entire network architecture. Quantitative and qualitative empirical results show the effectiveness of VCCs in the domain of image classification. Also, we leverage VCCs for the application of failure mode debugging to reveal where mistakes arise in deep networks.

关键词： Concept-based interpretability Interpretability Multilayer interpretability Object recognition

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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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AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings

AirPlanes: Accurate Plane Estimation via 3D-Consistent Embed...

引用

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

作者： Watson, Jamie Aleotti, Filippo Sayed, Mohamed Qureshi, Zawar Mac Aodha, Oisin Brostow, Gabriel Firman, Michael Vicente, Sara Niantic San Francisco CA 94111 USA Univ Edinburgh Edinburgh Midlothian Scotland UCL London England

ISBN: (纸本)9798350353013;9798350353006

Extracting planes from a 3D scene is useful for downstream tasks in robotics and augmented reality. In this paper we tackle the problem of estimating the planar surfaces in a scene from posed images. Our first finding is that a surprisingly competitive baseline results from combining popular clustering algorithms with recent improvements in 3D geometry estimation. However, such purely geometric methods are understandably oblivious to plane semantics, which are crucial to discerning distinct planes. To overcome this limitation, we propose a method that predicts multi-view consistent plane embeddings that complement geometry when clustering points into planes. We show through extensive evaluation on the ScanNetV2 dataset that our new method outperforms existing approaches and our strong geometric baseline for the task of plane estimation.

关键词： 3D vision plane estimation

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A Generative Approach for Wikipedia-Scale Visual Entity recognition

A Generative Approach for Wikipedia-Scale Visual Entity Reco...

引用

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

作者： Caron, Mathilde Iscen, Ahmet Fathi, Alireza Schmid, Cordelia Google Res San Francisco CA 94105 USA

ISBN: (纸本)9798350353006

In this paper, we address web-scale visual entity recognition, specifically the task of mapping a given query image to one of the 6 million existing entities in Wikipedia. One way of approaching a problem of such scale is using dual-encoder models (e.g. CLIP), where all the entity names and query images are embedded into a unified space, paving the way for an approximate kNN search. Alternatively, it is also possible to re-purpose a captioning model to directly generate the entity names for a given image. In contrast, we introduce a novel Generative Entity recognition (GER) frame-work, which given an input image learns to auto-regressively decode a semantic and discriminative "code" identifying the target entity. Our experiments demonstrate the efficacy of this GER paradigm, showcasing state-of-the-art performance on the challenging OVEN benchmark. GER surpasses strong captioning, dual-encoder, visual matching and hierarchical classification baselines, affirming its advantage in tackling the complexities of web-scale recognition.

关键词： generative auto-regressive models Image recognition vision-language models

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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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V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs

V*: Guided Visual Search as a Core Mechanism in Multimodal L...

引用

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

作者： Wu, Penghao Xie, Saining Univ Calif San Diego La Jolla CA 92093 USA NYU New York NY USA

ISBN: (纸本)9798350353006

When we look around and perform complex tasks, how we see and selectively process what we see is crucial. However, the lack of this visual search mechanism in current multimodal LLMs (MLLMs) hinders their ability to focus on important visual details, especially when handling high-resolution and visually crowded images. To address this, we introduce V*, an LLM- guided visual search mechanism that employs the world knowledge in LLMs for efficient visual querying. When combined with an MLLM, this mechanism enhances collaborative reasoning, contextual understanding, and precise visual grounding. This integration results in a new MLLM meta-architecture, named Show, sEArch, and TelL (SEAL). We further create V*Bench, a benchmark specifically designed to evaluate MLLMs in their ability to process high-resolution images and focus on visual details. Our study highlights the necessity of incorporating visual search capabilities into multimodal systems. The code is available here.

关键词： multimodal large language model vision and language visual search

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Privacy-Preserving Face recognition Using Trainable Feature Subtraction

Privacy-Preserving Face Recognition Using Trainable Feature ...

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

作者： Mi, Yuxi Zhong, Zhizhou Huang, Yuge Ji, Jiazhen Xu, Jianqing Wang, Jun Wang, Shaoming Ding, Shouhong Zhou, Shuigeng Fudan Univ Shanghai Peoples R China Tencent Youtu Lab Shenzhen Peoples R China Tencent WeChat Pay Lab33 Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

The widespread adoption of face recognition has led to increasing privacy concerns, as unauthorized access to face images can expose sensitive personal information. This paper explores face image protection against viewing and recovery attacks. Inspired by image compression, we propose creating a visually uninformative face image through feature subtraction between an original face and its model-produced regeneration. Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature representation. To enhance privacy, the high-dimensional representation is crafted through random channel shuffling, resulting in randomized recognizable images devoid of attacker-leverageable texture details. We distill our methodologies into a novel privacy-preserving face recognition method, MinusFace. Experiments demonstrate its high recognition accuracy and effective privacy protection. Its code is available at https://***/Tencent/TFace

关键词： face recognition privacy protection

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