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检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020"

共 11281 条记录，以下是161-170 订阅

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LLaFS: When Large Language Models Meet Few-Shot Segmentation

LLaFS: When Large Language Models Meet Few-Shot Segmentation

引用

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

作者： Zhu, Lanyun Chen, Tianrun Ji, Deyi Ye, Jieping Liu, Jun Singapore Univ Technol & Design Singapore Singapore Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350353013;9798350353006

This paper proposes LLaFS, the first attempt to leverage large language models (LLMs) in few-shot segmentation. In contrast to the conventional few-shot segmentation methods that only rely on the limited and biased information from the annotated support images, LLaFS leverages the vast prior knowledge gained by LLM as an effective supplement and directly uses the LLM to segment images in a few-shot manner. To enable the text-based LLM to handle image-related tasks, we carefully design an input instruction that allows the LLM to produce segmentation results represented as polygons, and propose a region-attribute table to simulate the human visual mechanism and provide multi-modal guidance. We also synthesize pseudo samples and use curriculum learning for pretraining to augment data and achieve better optimization. LLaFS achieves state-of-the-art results on multiple datasets, showing the potential of using LLMs for few-shot computer vision tasks.

关键词： Few-shot segmentation Large vision-language models

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BIOCLIP: A vision Foundation Model for the Tree of Life

BIOCLIP: A Vision Foundation Model for the Tree of Life

引用

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

作者： Stevens, Samuel Wu, Jiaman Thompson, Matthew J. Campolongo, Elizabeth G. Song, Chan Hee Carlyle, David Edward Dong, Li Dahdul, Wasila M. Stewart, Charles Berger-Wolf, Tanya Chao, Wei-Lun Su, Yu Ohio State Univ Columbus OH 43210 USA Microsoft Res Mountain View CA USA Univ Calif Irvine Irvine CA USA Rensselaer Polytech Inst Troy NY USA

ISBN: (纸本)9798350353006

Images of the natural world, collected by a variety of cameras, from drones to individual phones, are increasingly abundant sources of biological information. There is an explosion of computational methods and tools, particularly computer vision, for extracting biologically relevant information from images for science and conservation. Yet most of these are bespoke approaches designed for a specific task and are not easily adaptable or extendable to new questions, contexts, and datasets. A vision model for general organismal biology questions on images is of timely need. To approach this, we curate and release TREEOFLIFE-10M, the largest and most diverse ML-ready dataset of biology images. We then develop BIOCLIP, a foundation model for the tree of life, leveraging the unique properties of biology captured by TREEOFLIFE-10M, namely the abundance and variety of images of plants, animals, and fungi, together with the availability of rich structured biological knowledge. We rigorously benchmark our approach on diverse fine-grained biology classification tasks and find that BIOCLIP consistently and substantially outperforms existing baselines (by 16% to 17% absolute). Intrinsic evaluation reveals that BIOCLIP has learned a hierarchical representation conforming to the tree of life, shedding light on its strong generalizability.(1)

关键词： computer vision evolutionary biology & ecology imageomics machine learning

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Building vision-Language Models on Solid Foundations with Masked Distillation

Building Vision-Language Models on Solid Foundations with Ma...

引用

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

作者： Sameni, Sepehr Kafle, Kushal Tan, Hao Jenni, Simon Univ Bern Bern Switzerland Adobe Res San Jose CA USA

ISBN: (纸本)9798350353006

Recent advancements in vision-Language Models (VLMs) have marked a significant leap in bridging the gap between computer vision and natural language processing. However, traditional VLMs, trained through contrastive learning on limited and noisy image-text pairs, often lack the spatial and linguistic understanding to generalize well to dense vision tasks or less common languages. Our approach, Solid Foundation CLIP (SF-CLIP), circumvents this issue by implicitly building on the solid visual and language understanding of foundational models trained on vast amounts of unimodal data. SF-CLIP integrates contrastive image-text pretraining with a masked knowledge distillation from large foundational text and vision models. This methodology guides our VLM in developing robust text and image representations. As a result, SF-CLIP shows exceptional zero-shot classification accuracy and enhanced image and text retrieval capabilities, setting a new state of the art for ViT-B/16 trained on YFCC15M and CC12M. Moreover, the dense per-patch supervision enhances our zero-shot and linear probe performance in semantic segmentation tasks. A remarkable aspect of our model is its multilingual proficiency, evidenced by strong retrieval results in multiple languages despite being trained predominantly on English data. We achieve all of these improvements without sacrificing the training efficiency through our selective application of masked distillation and the inheritance of teacher word embeddings.

关键词： CLIP Distillation LLM Multilingual Multimodal Representation Learning

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Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models

Generative Rendering: Controllable 4D-Guided Video Generatio...

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

作者： Cai, Shengqu Ceylan, Duygu Gadelha, Matheus Huang, Chun-Hao Paul Wang, Tuanfeng Yang Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA Adobe Res San Francisco CA USA

ISBN: (纸本)9798350353013;9798350353006

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious manual process, which can be automated by emerging text-to-video diffusion models. Despite great promise, video diffusion models are difficult to control, hindering a user to apply their own creativity rather than amplifying it. To address this challenge, we present a novel approach that combines the controllability of dynamic 3D meshes with the expressivity and editability of emerging diffusion models. For this purpose, our approach takes an animated, low-fidelity rendered mesh as input and injects the ground truth correspondence information obtained from the dynamic mesh into various stages of a pre-trained text-to-image generation model to output high-quality and temporally consistent frames. We demonstrate our approach on various examples where motion can be obtained by animating rigged assets or changing the camera path. Project page: ***/generative_rendering.

关键词： Animation computer Graphics computer vision Generative Models Video Generation Video Synthesis

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Flow-Guided Online Stereo Rectification for Wide Baseline Stereo

Flow-Guided Online Stereo Rectification for Wide Baseline St...

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

作者： Kumar, Anush Mannan, Fahim Jafari, Omid Hosseini Li, Shile Heider, Felix Torc Robot Blacksburg VA 24060 USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350353006

Stereo rectification is widely considered "solved" due to the abundance of traditional approaches to perform rectification. However, autonomous vehicles and robots in-the-wild require constant re-calibration due to exposure to various environmental factors, including vibration, and structural stress, when cameras are arranged in a wide-baseline configuration. Conventional rectification methods fail in these challenging scenarios: especially for larger vehicles, such as autonomous freight trucks and semi-trucks, the resulting incorrect rectification severely affects the quality of downstream tasks that use stereo/multi-view data. To tackle these challenges, we propose an online rectification approach that operates at real-time rates while achieving high accuracy. We propose a novel learning-based online calibration approach that utilizes stereo correlation volumes built from a feature representation obtained from cross-image attention. Our model is trained to minimize vertical optical flow as proxy rectification constraint, and predicts the relative rotation between the stereo pair. The method is real-time and even outperforms conventional methods used for offline calibration, and substantially improves downstream stereo depth, post-rectification. We release two public datasets (https://***/online-stereo-recification/), a synthetic and experimental wide baseline dataset, to foster further research.

关键词： Autonomous Driving Camera Pose Estimation computer vision Rectification Datasets Stereo Rectification Wide Baseline Stereo

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Fair-VPT: Fair Visual Prompt Tuning for Image Classification

Fair-VPT: Fair Visual Prompt Tuning for Image Classification

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

作者： Park, Sungho Byun, Hy Eran Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350353006

Despite the remarkable success of vision Transformers (ViT) across diverse fields in computer vision, they have a clear drawback of expensive adaption cost for downstream tasks due to the increased scale. To address this, Visual Prompt Tuning (VPT) incorporates learnable parameters in the input space of ViT. While freezing the ViT backbone and tuning only the prompts, it exhibits superior performances to full fine-tuning. However, despite the outstanding advantage, we point out that VPT may lead to serious unfairness in downstream classification. Initially, we investigate the causes of unfairness in VPT, identifying the biasedly pre-trained ViT as a principal factor. Motivated by this observation, we propose a Fair Visual Prompt Tuning (Fair-VPT) which removes biased information in the pre-trained ViT while adapting it to downstream classification tasks. To this end, we categorize prompts into "cleaner prompts" and "target prompts". Based on this, we encode the class token in two different ways by either masking or not masking the target prompts in the self-attention process. These encoded tokens are trained with distinct objective functions, resulting in the inclusion of different information in the target and cleaner prompts. Moreover, we introduce a disentanglement loss based on contrastive learning to further decorrelate them. In experiments across diverse benchmarks, the proposed method demonstrates the most superior performance in terms of balanced classification accuracy and fairness.

关键词： FAI Fair-VPT Fairness Large vision Model vision Transformer Visual Prompt Tuning ViT VPT

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Action-slot: Visual Action-centric Representations for Multi-label Atomic Activity recognition in Traffic Scenes

Action-slot: Visual Action-centric Representations for Multi...

引用

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

作者： Kung, Chi-Hsi Lu, Shu-Wei Tsai, Yi-Hsuan Chen, Yi-Ting Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan Google Mountain View CA USA

ISBN: (纸本)9798350353006

In this paper, we study multi-label atomic activity recognition. Despite the notable progress in action recognition, it is still challenging to recognize atomic activities due to a deficiency in holistic understanding of both multiple road users' motions and their contextual information. In this paper, we introduce Action-slot, a slot attention-based approach that learns visual action-centric representations, capturing both motion and contextual information. Our key idea is to design action slots that are capable of paying attention to regions where atomic activities occur, without the need for explicit perception guidance. To further enhance slot attention, we introduce a background slot that competes with action slots, aiding the training process in avoiding unnecessary focus on background regions devoid of activities. Yet, the imbalanced class distribution in the existing dataset hampers the assessment of rare activities. To address the limitation, we collect a synthetic dataset called TACO, which is four times larger than OATS and features a balanced distribution of atomic activities. To validate the effectiveness of our method, we conduct comprehensive experiments and ablation studies against various action recognition baselines. We also show that the performance of multi-label atomic activity recognition on real-world datasets can be improved by pretraining representations on TACO. Our source code, dataset, and visualization videos are available at https://***/Action-slot/

关键词： Action recognition Autonomous Driving Slot Attention Traffic Activity Video Understanding

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TIGER: Time-Varying Denoising Model for 3D Point Cloud Generation with Diffusion Process

TIGER: Time-Varying Denoising Model for 3D Point Cloud Gener...

引用

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

作者： Ren, Zhiyuan Kim, Minchul Liu, Feng Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9798350353006

Recently, diffusion models have emerged as a new powerful generative method for 3D point cloud generation tasks. However, few works study the effect of the architecture of the diffusion model in the 3D point cloud, resorting to the typical UNet model developed for 2D images. Inspired by the wide adoption of Transformers, we study the complementary role of convolution (from UNet) and attention (from Transformers). We discover that their respective importance change according to the timestep in the diffusion process. At early stage, attention has an out-sized influence because Transformers are found to generate the overall shape more quickly, and at later stages when adding fine detail, convolution starts having a larger impact on the generated point cloud's local surface quality. In light of this observation, we propose a time-varying two-stream denoising model combined with convolution layers and transformer blocks. We generate an optimizable mask from each timestep to reweigh global and local features, obtaining time-varying fused features. Experimentally, we demonstrate that our proposed method quantitatively outperforms other state-of-the-art methods regarding visual quality and diversity. Code is avaiable https://***/Zhiyuan-R/Tiger-Diffusion.

关键词： 3D vision Diffusion Model Generative Model Point Cloud ShapeNet

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H-ViT: A Hierarchical vision Transformer for Deformable Image Registration

H-ViT: A Hierarchical Vision Transformer for Deformable Imag...

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

作者： Ghahremani, Morteza Khateri, Mohammad Jian, Bailiang Wiestler, Benedikt Adeli, Ehsan Wachinger, Christian Tech Univ Munich Munich Germany Munich Ctr Machine Learning Munich Germany Univ Eastern Finland Espoo Finland Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9798350353006

This paper introduces a novel top-down representation approach for deformable image registration, which estimates the deformation field by capturing various short-and long-range flow features at different scale levels. As a Hierarchical vision Transformer (H-ViT), we propose a dual self-attention and cross-attention mechanism that uses high-level features in the deformation field to represent low-level ones, enabling information streams in the deformation field across all voxel patch embeddings irrespective of their spatial proximity. Since high-level features contain abstract flow patterns, such patterns are expected to effectively contribute to the representation of the deformation field in lower scales. When the self-attention module utilizes within-scale short-range patterns for representation, the cross-attention modules dynamically look for the key tokens across different scales to further interact with the local query voxel patches. Our method shows superior accuracy and visual quality over the state-of-the-art registration methods in five publicly available datasets, highlighting a substantial enhancement in the performance of medical imaging registration. The project link is available at https://***/hvit.

关键词： Deformable Image Registration Hierarchical cross-attention Medical Imaging vision Transformer

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SuperLoRA: Parameter-Efficient Unified Adaptation for Large vision Models

SuperLoRA: Parameter-Efficient Unified Adaptation for Large ...

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

作者： Chen, Xiangyu Liu, Jing Wang, Ye Wang, Pu (Perry) Brand, Matthew Wang, Guanghui Koike-Akino, Toshiaki Univ Kansas Lawrence KS 66045 USA Mitsubishi Elect Res Labs MERL Cambridge MA 02139 USA Toronto Metropolitan Univ Toronto ON M5B 2K3 Canada

ISBN: (纸本)9798350365474

Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models for computer vision. This paper proposes a generalized framework called SuperLoRA that unifies and extends different LoRA variants, which can be realized under different hyper-parameter settings. Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance, with up to 10-fold gain in parameter efficiency for transfer learning tasks.

关键词： low-rank adaptation parameter efficiency

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