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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21008 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

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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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Privacy-Preserving Face recognition Using Trainable Feature Subtraction

Privacy-Preserving Face Recognition Using Trainable Feature ...

引用

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

来源：评论

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Question Aware vision Transformer for Multimodal Reasoning

Question Aware Vision Transformer for Multimodal Reasoning

引用

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

作者： Ganz, Roy Kittenplont, Yair Aberdam, Aviad Ben Avraham, Elad Nuriel, Oren Mazor, Shai Litmant, Ron Technion Haifa Israel AWS AI Labs Seattle WA 98019 USA

ISBN: (纸本)9798350353006

vision-Language (VL) models have gained significant research focus, enabling remarkable advances in multimodal reasoning. These architectures typically comprise a vision encoder, a Large Language Model (LLM), and a projection module that aligns visual features with the LLM's representation space. Despite their success, a critical limitation persists: the vision encoding process remains decoupled from user queries, often in the form of image-related questions. Consequently, the resulting visual features may not be optimally attuned to the query-specific elements of the image. To address this, we introduce QA-ViT, a Question Aware vision Transformer approach for multimodal reasoning, which embeds question awareness directly within the vision encoder. This integration results in dynamic visual features focusing on relevant image aspects to the posed question. QA-ViT is model-agnostic and can be incorporated efficiently into any VL architecture. Extensive experiments demonstrate the effectiveness of applying our method to various multimodal architectures, leading to consistent improvement across diverse tasks and showcasing its potential for enhancing visual and scene-text understanding.

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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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Discriminative pattern Calibration Mechanism for Source-Free Domain Adaptation

Discriminative Pattern Calibration Mechanism for Source-Free...

引用

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

作者： Xia, Haifeng Xia, Siyu Ding, Zhengming Southeast Univ Sch Automat Dhaka Bangladesh Tulane Univ Dept Comp Sci New Orleans LA 70118 USA

ISBN: (纸本)9798350353006

Source-free domain adaptation (SFDA) assumes that model adaptation only accesses the well-learned source model and unlabeled target instances for knowledge transfer. However, cross-domain distribution shift easily triggers invalid discriminative semantics from source model on recognizing the target samples. Hence, understanding the specific content of discriminative pattern and adjusting their representation in target domain become the important key to overcome SFDA. To achieve such a vision, this paper proposes a novel explanation paradigm "Discriminative pattern Calibration (DPC)" mechanism on solving SFDA issue. Concretely, DPC first utilizes learning network to infer the discriminative regions on the target images and specifically emphasizes them in feature space to enhance their representation. Moreover, DPC relies on the attention-reversed mixup mechanism to augment more samples and improve the robustness of the classifier. Considerable experimental results and studies suggest that the effectiveness of our DPC in enhancing the performance of existing SFDA baselines.

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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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Semantic Shield: Defending vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment

Semantic Shield: Defending Vision-Language Models Against Ba...

引用

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

作者： Ishmam, Alvi Md Thomas, Christopher Virginia Tech Blacksburg VA 24061 USA

ISBN: (纸本)9798350353006

In recent years there has been enormous interest in vision-language models trained using self-supervised objectives. However, the use of large-scale datasets scraped from the web for training also makes these models vulnerable to potential security threats, such as backdooring and poisoning attacks. In this paper, we propose a method for mitigating such attacks on contrastively trained vision-language models. Our approach leverages external knowledge extracted from a language model to prevent models from learning correlations between image regions which lack strong alignment with external knowledge. We do this by imposing constraints to enforce that attention paid by the model to visual regions is proportional to the alignment of those regions with external knowledge. We conduct extensive experiments using a variety of recent backdooring and poisoning attacks on multiple datasets and architectures. Our results clearly demonstrate that our proposed approach is highly effective at defending against such attacks across multiple settings, while maintaining model utility and without requiring any changes at inference time.

关键词： Adversarial attack and defense vision languge model

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PointInfinity: Resolution-Invariant Point Diffusion Models

PointInfinity: Resolution-Invariant Point Diffusion Models

引用

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

作者： Huang, Zixuan Johnson, Justin Debnath, Shoubhik Rehg, James M. Wu, Chao-Yuan Meta FAIR Menlo Pk CA 94025 USA Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9798350353006

We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size, resolution-invariant latent representation. This enables efficient training with low-resolution point clouds, while allowing high-resolution point clouds to be generated during inference. More importantly, we show that scaling the test-time resolution beyond the training resolution improves the fidelity of generated point clouds and surfaces. We analyze this phenomenon and draw a link to classifier-free guidance commonly used in diffusion models, demonstrating that both allow trading off fidelity and variability during inference. Experiments on CO3D show that PointInfinity can efficiently generate high-resolution point clouds (up to 131k points, 31 more than Point-E) with state-of-the-art quality.

关键词： 3D Diffusion Model 3D Generation 3D reconstruction 3D vision computer vision Deep Learning

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Can Biases in ImageNet Models Explain Generalization?

Can Biases in ImageNet Models Explain Generalization?

引用

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

作者： Gavrikov, Paul Keuper, Janis Offenburg Univ IMLA Offenburg Germany Univ Mannheim Mannheim Germany

ISBN: (纸本)9798350353006

The robust generalization of models to rare, in-distribution (ID) samples drawn from the long tail of the training distribution and to out-of-training-distribution (OOD) samples is one of the major challenges of current deep learning methods. For image classification, this manifests in the existence of adversarial attacks, the performance drops on distorted images, and a lack of generalization to concepts such as sketches. The current understanding of generalization in neural networks is very limited, but some biases that differentiate models from human vision have been identified and might be causing these limitations. Consequently, several attempts with varying success have been made to reduce these biases during training to improve generalization. We take a step back and sanity-check these attempts. Fixing the architecture to the well-established ResNet-50, we perform a large-scale study on 48 ImageNet models obtained via different training methods to understand how and if these biases - including shape bias, spectral biases, and critical bands - interact with generalization. Our extensive study results reveal that contrary to previous findings, these biases are insufficient to accurately predict the generalization of a model holistically. We provide access to all checkpoints and evaluation code at https://***/paulgavrikov/biases_vs_generalization/

关键词： bias computer vision generalization imagenet robustness

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