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

共 52943 条记录，以下是131-140 订阅

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Towards Efficient Audio-Visual Learners via Empowering Pre-trained vision Transformers with Cross-Modal Adaptation

Towards Efficient Audio-Visual Learners via Empowering Pre-t...

引用

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

作者： Wang, Kai Tian, Yapeng Hatzinakos, Dimitrios Univ Toronto Toronto ON Canada Univ Texas Dallas Richardson TX 75083 USA

ISBN: (纸本)9798350365474

In this paper, we explore the cross-modal adaptation of pre-trained vision Transformers (ViTs) for the audio-visual domain by incorporating a limited set of trainable parameters. To this end, we propose a Spatial-Temporal-Global Cross-Modal Adaptation (STG-CMA) to gradually equip the frozen ViTs with the capability for learning audio-visual representation, consisting of the modality-specific temporal adaptation for temporal reasoning of each modality, the cross-modal spatial adaptation for refining the spatial information with the cue from counterpart modality, and the cross-modal global adaptation for global interaction between audio and visual modalities. Our STG-CMA presents a meaningful finding that only leveraging the shared pre-trained image model with inserted lightweight adapters is enough for spatial-temporal modeling and feature interaction of audio-visual modality. Extensive experiments indicate that our STG-CMA achieves state-of-the-art performance on various audio-visual understanding tasks including AVE, AVS, and AVQA while containing significantly reduced tunable parameters. The code is available at https://***/kaiw7/STG-CMA.

关键词： Audio-visual Lenarning Cross-modal Adaptation Pre-trained vision Transformers Reduced Tunanle Parameters Spatial-temporal-global Modeling

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A Theory of Joint Light and Heat Transport for Lambertian Scenes

A Theory of Joint Light and Heat Transport for Lambertian Sc...

引用

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

作者： Ramanagopal, Mani Narayanan, Sriram Sankaranarayanan, Aswin C. Narasimhan, Srinivasa G. Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

We present a novel theory that establishes the relationship between light transport in visible and thermal infrared, and heat transport in solids. We show that heat generated due to light absorption can be estimated by modeling heat transport using a thermal camera. For situations where heat conduction is negligible, we analytically solve the heat transport equation to derive a simple expression relating the change in thermal image intensity to the absorbed light intensity and heat capacity of the material. Next, we prove that intrinsic image decomposition for Lambertian scenes becomes a well-posed problem if one has access to the absorbed light. Our theory generalizes to arbitrary shapes and unstructured illumination. Our theory is based on applying energy conservation principle at each pixel independently. We validate our theory using real-world experiments on diffuse objects made of different materials that exhibit both direct and global components (inter-reflections) of light transport under unknown complex lighting.

关键词： heat transport intrinsic image decomposition light transport physics-based vision

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Dense vision Transformer Compression with Few Samples

Dense Vision Transformer Compression with Few Samples

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

作者： Zhang, Hanxiao Zhou, Yifan Wang, Guo-Hua Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Sch Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350353006

Few-shot model compression aims to compress a large model into a more compact one with only a tiny training set (even without labels). Block-level pruning has recently emerged as a leading technique in achieving high accuracy and low latency in few-shot CNN compression. But, few-shot compression for vision Transformers (ViT) remains largely unexplored, which presents a new challenge. In particular, the issue of sparse compression exists in traditional CNN few-shot methods, which can only produce very few compressed models of different model sizes. This paper proposes a novel framework for few-shot ViT compression named DC-ViT. Instead of dropping the entire block, DC-ViT selectively eliminates the attention module while retaining and reusing portions of the MLP module. DC-ViT enables dense compression, which outputs numerous compressed models that densely populate the range of model complexity. DC-ViT outperforms state-of-the-art few-shot compression methods by a significant margin of 10 percentage points, along with lower latency in the compression of ViT and its variants.

关键词： Few-shot Model Compression Pruning vision Transformer

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Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning

Adaptive Hyper-graph Aggregation for Modality-Agnostic Feder...

引用

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

作者： Qi, Fan Li, Shuai Tianjin Univ Technol Tianjin Peoples R China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353006

In Federated Learning (FL), the issue of statistical data heterogeneity has been a significant challenge to the field's ongoing development. This problem is further exacerbated when clients' data vary in modalities. In response to these issues of statistical heterogeneity and modality incompatibility, we propose the Adaptive Hyper-graph Aggregation framework, a novel solution for Modality-Agnostic Federated Learning. We design a Modular Architecture for Local Model with single modality, setting the stage for efficient intra-modality sharing and inter-modality complementarity. An innovative Global Consensus Prototype Enhancer is crafted to assimilate and broadcast global consensus knowledge within the network. At the core of our approach lies the Adaptive Hyper-graph Learning Strategy, which effectively tackles the inherent challenges of modality incompatibility and statistical heterogeneity within federated learning environments, accomplishing this adaptively even without the server being aware of the clients' modalities. Our approach, tested on three multimodal benchmark datasets, demonstrated strong performance across diverse data distributions, affirming its effectiveness in multimodal federated learning.

关键词： Knowledge engineering computer vision Adaptation models Federated learning Prototypes computer architecture Benchmark testing

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Low-Rank Few-Shot Adaptation of vision-Language Models

Low-Rank Few-Shot Adaptation of Vision-Language Models

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

作者： Zanella, Maxime Ben Ayed, Ismail UCLouvain Louvain Belgium UMons Mons Belgium ETS Montreal Montreal PQ Canada

ISBN: (纸本)9798350365474

Recent progress in the few-shot adaptation of vision-Language Models (VLMs) has further pushed their generalization capabilities, at the expense of just a few labeled samples within the target downstream task. However, this promising, already quite abundant few-shot literature has focused principally on prompt learning and, to a lesser extent, on adapters, overlooking the recent advances in Parameter-Efficient Fine-Tuning (PEFT). Furthermore, existing few-shot learning methods for VLMs often rely on heavy training procedures and/or carefully chosen, taskspecific hyper-parameters, which might impede their applicability. In response, we introduce Low-Rank Adaptation (LoRA) in few-shot learning for VLMs, and show its potential on 11 datasets, in comparison to current state-ofthe-art prompt- and adapter-based approaches. Surprisingly, our simple CLIP-LoRA method exhibits substantial improvements, while reducing the training times and keeping the same hyper-parameters in all the target tasks, i.e., across all the datasets and numbers of shots. Certainly, our surprising results do not dismiss the potential of prompt-learning and adapter-based research. However, we believe that our strong baseline could be used to evaluate progress in these emergent subjects in few-shot VLMs.

关键词： adapter few-shot LoRA PEFT prompt vision-Language

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PIN: Positional Insert Unlocks Object Localisation Abilities in VLMs

PIN: Positional Insert Unlocks Object Localisation Abilities...

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

作者： Dorkenwald, Michael Barazani, Nimrod Snoek, Cees G. M. Asano, Yuki M. Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798350353006

vision-Language Models (VLMs), such as Flamingo and GPT-4V, have shown immense potential by integrating large language models with vision systems. Nevertheless, these models face challenges in the fundamental computer vision task of object localisation, due to their training on multi-modal data containing mostly captions without explicit spatial grounding. While it is possible to construct custom, supervised training pipelines with bounding box annotations that integrate with VLMs, these result in specialized and hard-to-scale models. In this paper, we aim to explore the limits of caption-based VLMs and instead propose to tackle the challenge in a simpler manner by i) keeping the weights of a caption-based VLM frozen and ii) not using any supervised detection data. To this end, we introduce an input-agnostic Positional Insert (PIN), a learnable spatial prompt, containing a minimal set of parameters that are slid inside the frozen VLM, unlocking object localisation capabilities. Our PIN module is trained with a simple next-token prediction task on synthetic data without requiring the introduction of new output heads. Our experiments demonstrate strong zero-shot localisation performances on a variety of images, including Pascal VOC, COCO, LVIS, and diverse images like paintings or cartoons.

关键词： Efficient Adaption of VLMs Foundation Models vision-Language Models Visual Grounding

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FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography

FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model...

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

作者： Yang, Julia Barnett, Alina Jade Donnelly, Jon Kishore, Satvik Fang, Jerry Schwartz, Fides Regina Chen, Chaofan Lo, Joseph Y. Rudin, Cynthia Duke Univ Durham NC 27708 USA Brigham & Womens Hosp 75 Francis St Boston MA 02115 USA Univ Maine Orono ME USA

ISBN: (纸本)9798350365474

Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models are unsuitable and there is a call in these fields to make interpretable models. Recent work in interpretable computer vision provides transparency to these formerly black boxes by utilizing prototypes for case-based explanations, achieving high accuracy in applications including mammography. However, these models struggle with precise feature localization, reasoning on large portions of an image when only a small part is relevant. This paper addresses this gap by proposing a novel multi-scale interpretable deep learning model for mammographic mass margin classification. Our contribution not only offers an interpretable model with reasoning aligned with radiologist practices, but also provides a general architecture for computer vision with user-configurable prototypes from coarse-to fine-grained prototypes.

关键词： breast cancer cancer computer vision deep learning interpretability interpretable machine learning machine learning mammography medical imaging neural networks

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Iterated Learning Improves Compositionality in Large vision-Language Models

Iterated Learning Improves Compositionality in Large Vision-...

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

作者： Zheng, Chenhao Zhang, Jieyu Kembhavi, Aniruddha Krishna, Ranjay Univ Washington Seattle WA 98195 USA Univ Michigan Ann Arbor MI 48109 USA Allen Inst Artificial Intelligence Seattle WA USA

ISBN: (纸本)9798350353006

A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and language pretraining, recent investigations find that most-if not all-our state-of-the-art vision-language models struggle at compositionality. They are unable to distinguish between images of "a girl in white facing a man in black" and "a girl in black facing a man in white". Moreover, prior work suggests that compositionality doesn't arise with scale: larger model sizes or training data don't help. This paper develops a new iterated training algorithm that incentivizes compositionality. We draw on decades of cognitive science research that identifies cultural transmission-the need to teach a new generation-as a necessary inductive prior that incentivizes humans to develop compositional languages. Specifically, we reframe vision-language contrastive learning as the Lewis Signaling Game between a vision agent and a language agent, and operationalize cultural transmission by iteratively resetting one of the agent's weights during training. After every iteration, this training paradigm induces representations that become "easier to learn", a property of compositional languages: e.g. our model trained on CC3M and CC12M improves standard CLIP by 4.7%, 4.0% respectfully in the SugarCrepe benchmark.

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ALGM: Adaptive Local-then-Global Token Merging for Efficient Semantic Segmentation with Plain vision Transformers

ALGM: Adaptive Local-then-Global Token Merging for Efficient...

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

作者： Norouzi, Narges Orlova, Svetlana de Geus, Daan Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350353006

This work presents Adaptive Local-then-Global Merging (ALGM), a token reduction method for semantic segmentation networks that use plain vision Transformers. ALGM merges tokens in two stages: (1) In the first network layer, it merges similar tokens within a small local window and (2) halfway through the network, it merges similar tokens across the entire image. This is motivated by an analysis in which we found that, in those situations, tokens with a high cosine similarity can likely be merged without a drop in segmentation quality. With extensive experiments across multiple datasets and network configurations, we show that ALGM not only significantly improves the throughput by up to 100%, but can also enhance the mean IoU by up to +1.1, thereby achieving a better trade-off between segmentation quality and efficiency than existing methods. Moreover, our approach is adaptive during inference, meaning that the same model can be used for optimal efficiency or accuracy, depending on the application. Code is available at https://***/ALGM.

关键词： Efficient vision Transformers Semantic Segmentation Token Merging

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Streaming Dense Video Captioning

Streaming Dense Video Captioning

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

作者： Zhou, Xingyi Arnab, Anurag Buch, Shyamal Yan, Shen Myers, Austin Xiong, Xuehan Nagrani, Arsha Schmid, Cordelia Google Mountain View CA 94043 USA

ISBN: (纸本)9798350353006

An ideal model for dense video captioning - predicting captions localized temporally in a video - should be able to handle long input videos, predict rich, detailed textual descriptions, and be able to produce outputs before processing the entire video. Current state-of-the-art models, however, process a fixed number of downsampled frames, and make a single full prediction after seeing the whole video. We propose a streaming dense video captioning model that consists of two novel components: First, we propose a new memory module, based on clustering incoming tokens, which can handle arbitrarily long videos as the memory is of a fixed size. Second, we develop a streaming decoding algorithm that enables our model to make predictions before the entire video has been processed. Our model achieves this streaming ability, and significantly improves the state-of-the-art on three dense video captioning benchmarks: ActivityNet, YouCook2 and ViTT. Our code is released at https://***/google-research/scenic.

关键词： captioning long video streaming video video captioning vision and language

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