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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111-120 订阅

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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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CONFORM: Contrast is All You Need For High-Fidelity Text-to-Image Diffusion Models

CONFORM: Contrast is All You Need For High-Fidelity Text-to-...

引用

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

作者： Meral, Tuna Han Salih Simsar, Enis Tombari, Federico Yanardag, Pinar Virginia Tech Blacksburg VA USA Swiss Fed Inst Technol Zurich Switzerland TUM Munich Germany Google Menlo Pk CA USA

ISBN: (纸本)9798350353006

Images produced by text-to-image diffusion models might not always faithfully represent the semantic intent of the provided text prompt, where the model might overlook or entirely fail to produce certain objects. Existing solutions often require customly tailored functions for each of these problems, leading to sub-optimal results, especially for complex prompts. Our work introduces a novel perspective by tackling this challenge in a contrastive context. Our approach intuitively promotes the segregation of objects in attention maps while also maintaining that pairs of related attributes are kept close to each other. We conduct extensive experiments across a wide variety of scenarios, each involving unique combinations of objects, attributes, and scenes. These experiments effectively showcase the versatility, efficiency, and flexibility of our method in working with both latent and pixel-based diffusion models, including Stable Diffusion and Imagen. Moreover, we publicly share our source code to facilitate further research.

关键词： computer vision Contrastive learning Generative AI Semantic fidelity Stable Diffusion Text-to-image diffusion models

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StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN

StyleCineGAN: Landscape Cinemagraph Generation using a Pre-t...

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

作者： Choi, Jongwoo Seo, Kwanggyoon Ashtari, Amirsaman Noh, Junyong Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea

ISBN: (纸本)9798350353013;9798350353006

We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent unconditional video generation, we leverage a powerful pre-trained image generator to synthesize high-quality cinemagraphs. Unlike previous approaches that mainly utilize the latent space of a pre-trained StyleGAN, our approach utilizes its deep feature space for both GAN inversion and cinemagraph generation. Specifically, we propose multi-scale deep feature warping (MSDFW), which warps the intermediate features of a pre-trained StyleGAN at different resolutions. By using MSDFW, the generated cinemagraphs are of high resolution and exhibit plausible looping animation. We demonstrate the superiority of our method through user studies and quantitative comparisons with state-of-the-art cinemagraph generation methods and a video generation method that uses a pre-trained StyleGAN.

关键词： Image and video synthesis and generation vision + graphics vision applications and systems

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Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning

Context-based and Diversity-driven Specificity in Compositio...

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

作者： Li, Yun Liu, Zhe Chen, Hang Yao, Lina CSIROs Data61 Clayton Vic Australia Bytedance Ltd Beijing Peoples R China Snap Inc Santa Monica CA USA

ISBN: (纸本)9798350353006

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object pairs based on a limited set of observed examples. Current CZSL methodologies, despite their advancements, tend to neglect the distinct specificity levels present in attributes. For instance, given images of sliced strawberries, they may fail to prioritize 'Sliced-Strawberry' over a generic 'Red-Strawberry', despite the former being more informative. They also suffer from ballooning search space when shifting from Close-World (CW) to Open-World (OW) CZSL. To address the issues, we introduce the Context-based and Diversity-driven Specificity learning framework for CZSL (CDS-CZSL). Our framework evaluates the specificity of attributes by considering the diversity of objects they apply to and their related context. This novel approach allows for more accurate predictions by emphasizing specific attribute-object pairs and improves composition filtering in OW-CZSL. We conduct experiments in both CW and OW scenarios, and our model achieves state-of-the-art results across three datasets.

关键词： compositional zero-shot learning transfer learning vision language model

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Improving Visual recognition with Hyperbolical Visual Hierarchy Mapping

Improving Visual Recognition with Hyperbolical Visual Hierar...

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

作者： Kwon, Hyeongjun Jang, Jinhyun Kim, Jin Kim, Kwonyoung Sohn, Kwanghoon Yonsei Univ Seoul South Korea Korea Inst Sci & Technol KIST Seoul South Korea

ISBN: (纸本)9798350353006

Visual scenes are naturally organized in a hierarchy, where a coarse semantic is recursively comprised of several fine details. Exploring such a visual hierarchy is crucial to recognize the complex relations of visual elements, leading to a comprehensive scene understanding. In this paper, we propose a Visual Hierarchy Mapper (Hi-Mapper), a novel approach for enhancing the structured understanding of the pre-trained Deep Neural Networks (DNNs). Hi-Mapper investigates the hierarchical organization of the visual scene by 1) pre-defining a hierarchy tree through the encapsulation of probability densities;and 2) learning the hierarchical relations in hyperbolic space with a novel hierarchical contrastive loss. The pre-defined hierarchy tree recursively interacts with the visual features of the pre-trained DNNs through hierarchy decomposition and encoding procedures, thereby effectively identifying the visual hierarchy and enhancing the recognition of an entire scene. Extensive experiments demonstrate that Hi-Mapper significantly enhances the representation capability of DNNs, leading to an improved performance on various tasks, including image classification and dense prediction tasks. The code is available at https://***/kwonjunn01/Hi-Mapper.

关键词： DNNs Hyperbolic Deep Learning Probabilistic Modeling

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Mitigating Object Hallucinations in Large vision-Language Models through Visual Contrastive Decoding

Mitigating Object Hallucinations in Large Vision-Language Mo...

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

作者： Leng, Sicong Zhang, Hang Chen, Guanzheng Li, Xin Lug, Shijian Miao, Chunyan Bing, Lidong Alibaba Grp DAMO Acad Hangzhou Peoples R China Nanyang Technol Univ Singapore Singapore Hupan Lab Hangzhou 310023 Peoples R China

ISBN: (纸本)9798350353006

Large vision-Language Models (LVLMs) have advanced considerably, intertwining visual recognition and language understanding to generate content that is not only coherent but also contextually attuned. Despite their success, LVLMs still suffer from the issue of object hallucinations, where models generate plausible yet incorrect outputs that include objects that do not exist in the images. To mitigate this issue, we introduce Visual Contrastive Decoding (VCD), a simple and training-free method that contrasts output distributions derived from original and distorted visual inputs. The proposed VCD effectively reduces the over-reliance on statistical bias and unimodal priors, two essential causes of object hallucinations. This adjustment ensures the generated content is closely grounded to visual inputs, resulting in contextually accurate outputs. Our experiments show that VCD, without either additional training or the usage of external tools, significantly mitigates the object hallucination issue across different LVLM families. Beyond mitigating object hallucinations, VCD also excels in general LVLM benchmarks, highlighting its wide-ranging applicability.

关键词： Large Multimodal Models Multimodality vision and Language

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Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence

Telling Left from Right: Identifying Geometry-Aware Semantic...

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

作者： Zhang, Junyi Herrmann, Charles Hur, Junhwa Chen, Eric Jampani, Varun Sun, Deqing Yang, Ming-Hsuan Shanghai Jiao Tong Univ Shanghai Peoples R China Google Res Mountain View CA USA UIUC Champaign IL USA Stabil AI London England UC Merced Merced CA USA

ISBN: (纸本)9798350353013;9798350353006

While pre-trained large-scale vision models have shown significant promise for semantic correspondence, their features often struggle to grasp the geometry and orientation of instances. This paper identifies the importance of being geometry-aware for semantic correspondence and reveals a limitation of the features of current foundation models under simple post-processing. We show that incorporating this information can markedly enhance semantic correspondence performance with simple but effective solutions in both zero-shot and supervised settings. We also construct a new challenging benchmark for semantic correspondence built from an existing animal pose estimation dataset, for both pre-training validating models. Our method achieves a PCK@0.10 score of 65.4 (zero-shot) and 85.6 (supervised) on the challenging SPair-71k dataset, surpassing the state of the art by 5.5p and 11.0p absolute gains, respectively. Our code and datasets are publicly available at: https://***

关键词： diffusion models semantic correspondence vision transformer

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Probing the 3D Awareness of Visual Foundation Models

Probing the 3D Awareness of Visual Foundation Models

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

作者： El Banani, Mohamed Raj, Amit Maninis, Kevis-Kokitsi Kar, Abhishek Li, Yuanzhen Rubinstein, Michael Sun, Deqing Guibas, Leonidas Johnson, Justin Jampani, Varun Univ Michigan Ann Arbor MI 48109 USA Google Mountain View CA 94043 USA Stability AI London ON Canada

ISBN: (纸本)9798350353006

Recent advances in large-scale pretraining have yielded visual foundation models with strong capabilities. Not only can recent models generalize to arbitrary images for their training task, their intermediate representations are useful for other visual tasks such as detection and segmentation. Given that such models can classify, delineate, and localize objects in 2D, we ask whether they also represent their 3D structure? In this work, we analyze the 3D awareness of visual foundation models. We posit that 3D awareness implies that representations (1) encode the 3D structure of the scene and (2) consistently represent the surface across views. We conduct a series of experiments using task-specific probes and zero-shot inference procedures on frozen features. Our experiments reveal several limitations of the current models. Our code and analysis can be found at https://***/mbanani/probe3d.

关键词： 3D Awareness 3D vision Foundation Models Representation Learning

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A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives

A Backpack Full of Skills: Egocentric Video Understanding wi...

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

作者： Peirone, Simone Alberto Pistilli, Francesca Alliegro, Antonio Averta, Giuseppe Politecnico Torino Turin Italy Ist Italiano Tecnol Genoa Italy

ISBN: (纸本)9798350353006

Human comprehension of a video stream is naturally broad: in a few instants, we are able to understand what is happening, the relevance and relationship of objects, and forecast what will follow in the near future, everything all at once. We believe that - to effectively transfer such an holistic perception to intelligent machines - an important role is played by learning to correlate concepts and to abstract knowledge coming from different tasks, to synergistically exploit them when learning novel skills. To accomplish this, we look for a unified approach to video understanding which combines shared temporal modelling of human actions with minimal overhead, to support multiple down-stream tasks and enable cooperation when learning novel skills. We then propose EgoPack, a solution that creates a collection of task perspectives that can be carried across downstream tasks and used as a potential source of additional insights, as a backpack of skills that a robot can carry around and use when needed. We demonstrate the effectiveness and efficiency of our approach on four Ego4D benchmarks, outperforming current state-of-the-art methods. Project webpage: ***/EgoPack.

关键词： Egocentric vision Video Understanding

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ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization

ICON: Incremental CONfidence for Joint Pose and Radiance Fie...

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

作者： Wang, Weiyao Gleize, Pierre Tang, Hao Chen, Xingyu Liang, Kevin J. Feiszli, Matt Meta FAIR Menlo Pk CA 94025 USA

ISBN: (纸本)9798350353013;9798350353006

Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel View Synthesis (NVS) given a set of 2D images. However, NeRF training requires accurate camera pose for each input view, typically obtained by Structure-from-Motion (SfM) pipelines. Recent works have attempted to relax this constraint, but they still often rely on decent initial poses which they can refine. Here we aim at removing the requirement for pose initialization. We present Incremental CONfidence (ICON), an optimization procedure for training NeRFs from 2D video frames. ICON only assumes smooth camera motion to estimate initial guess for poses. Further, ICON introduces "confidence": an adaptive measure of model quality used to dynamically reweight gradients. ICON relies on high-confidence poses to learn NeRF, and high-confidence 3D structure (as encoded by NeRF) to learn poses. We show that ICON, without prior pose initialization, achieves superior performance in both CO3D and HO3D versus methods which use SfM pose.

关键词： Three dimensional computer graphics

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