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

共 31021 条记录，以下是4301-4310 订阅

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Leveraging Style and Content features for Text Conditioned Image Retrieval

Leveraging Style and Content features for Text Conditioned I...

引用

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

作者： Chawla, Pranit Jandial, Surgan Badjatiya, Pinkesh Chopra, Ayush Sarkar, Mausoom Krishnamurthy, Balaji IIT Kharagpur Kharagpur W Bengal India IIT Hyderabad Kandi Telangana India Adobe Media & Data Sci Res Lab San Jose CA USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665448994

Image Search is a fundamental task playing a significant role in the success of wide variety of frameworks and applications. However, with the increasing sizes of product catalogues and the number of attributes per product, it has become difficult for users to express their needs effectively. Therefore, we focus on the problem of Image Retrieval with Text Feedback, which involves retrieving modified images according to the natural language feedback provided by users. In this work, we hypothesise that since an image can be delineated by its content and style features, modifications to the image can also take place in the two sub spaces respectively. Hence, we decompose an input image into its corresponding style and content features, apply modification of the text feedback individually in both the style and content spaces and finally fuse them for retrieval. Our experiments show that our approach outperforms a recent state of the art method in this task, TIRG, that seeks to use a single vector in contrast to leveraging the modification via text over style and content spaces separately.

关键词： computer vision Fuses conferences Image retrieval Natural languages pattern recognition Task analysis

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Linguistic Structures as Weak Supervision for Visual Scene Graph Generation

Linguistic Structures as Weak Supervision for Visual Scene G...

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

作者： Ye, Keren Kovashka, Adriana Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9781665445092

Prior work in scene graph generation requires categorical supervision at the level of triplets-subjects and objects, and predicates that relate them, either with or without bounding box information. However, scene graph generation is a holistic task: thus holistic, contextual supervision should intuitively improve performance. In this work, we explore how linguistic structures in captions can benefit scene graph generation. Our method captures the information provided in captions about relations between individual triplets, and context for subjects and objects (e.g. visual properties are mentioned). Captions are a weaker type of supervision than triplets since the alignment between the exhaustive list of human-annotated subjects and objects in triplets, and the nouns in captions, is weak. However, given the large and diverse sources of multimodal data on the web (e.g. blog posts with images and captions), linguistic supervision is more scalable than crowdsourced triplets. We show extensive experimental comparisons against prior methods which leverage instance- and image-level supervision, and ablate our method to show the impact of leveraging phrasal and sequential context, and techniques to improve localization of subjects and objects.

关键词： Location awareness Visualization computer vision Blogs Linguistics pattern recognition Noise measurement

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Understanding and Improving Source-Free Domain Adaptation from a Theoretical Perspective

Understanding and Improving Source-Free Domain Adaptation fr...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yu Mitsuzumi Akisato Kimura Hisashi Kashima NTT Corporation Kyoto University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Source-free Domain Adaptation (SFDA) is an emerging and challenging research area that addresses the problem of unsupervised domain adaptation (UDA) without source data. Though numerous successful methods have been proposed for SFDA, a theoretical understanding of why these methods work well is still absent. In this paper, we shed light on the theoretical perspective of existing SFDA methods. Specifically, we find that SFDA loss functions comprising discriminability and diversity losses work in the same way as the training objective in the theory of self-training based on the expansion assumption, which shows the existence of the target error bound. This finding brings two novel insights that enable us to build an improved SFDA method comprising 1) Model Training with Auto-Adjusting Diversity Constraint and 2) Augmentation Training with Teacher-Student Framework, yielding a better recognition performance. Extensive experiments on three benchmark datasets demonstrate the validity of the theoretical analysis and our method.

关键词： Training computer vision Adaptation models Computational modeling Benchmark testing pattern recognition

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SuperLoRA: Parameter-Efficient Unified Adaptation for Large vision Models

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

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Xiangyu Chen Jing Liu Ye Wang Pu Perry Wang Matthew Brand Guanghui Wang Toshiaki Koike-Akino University of Kansas Lawrence KS USA Mitsubishi Electric Research Laboratories (MERL) Cambridge MA USA Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

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： computer vision Adaptation models Tensors Computational modeling Large language models conferences Transfer learning

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TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations

TransFill: Reference-guided Image Inpainting by Merging Mult...

引用

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

作者： Zhou, Yuqian Barnes, Connelly Shechtman, Eli Amirghodsi, Sohrab UIUC IFP Champaign IL 61820 USA Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

Image inpainting is the task of plausibly restoring missing pixels within a hole region that is to be removed from a target image. Most existing technologies exploit patch similarities within the image, or leverage large-scale training data to fill the hole using learned semantic and texture information. However, due to the ill-posed nature of the inpainting task, such methods struggle to complete larger holes containing complicated scenes. In this paper, we propose TransFill, a multi-homography transformed fusion method to fill the hole by referring to another source image that shares scene contents with the target image. We first align the source image to the target image by estimating multiple homographies guided by different depth levels. We then learn to adjust the color and apply a pixel-level warping to each homography-warped source image to make it more consistent with the target. Finally, a pixel-level fusion module is learned to selectively merge the different proposals. Our method achieves state-of-the-art performance on pairs of images across a variety of wide baselines and color differences, and generalizes to user-provided image pairs.

关键词： computer vision Image color analysis Semantics Merging Training data pattern recognition Image restoration

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Disentangled Cycle Consistency for Highly-realistic Virtual Try-On

Disentangled Cycle Consistency for Highly-realistic Virtual ...

引用

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

作者： Ge, Chongjian Song, Yibing Ge, Yuying Yang, Han Liu, Wei Luo, Ping Univ Hong Kong Hong Kong Peoples R China Tencent AI Lab Bellevue WA 98004 USA Swiss Fed Inst Technol Zurich Switzerland Tencent Data Platform Bellevue WA USA

ISBN: (纸本)9781665445092

Image virtual try-on replaces the clothes on a person image with a desired in-shop clothes image. It is challenging because the person and the in-shop clothes are unpaired. Existing methods formulate virtual try-on as either in-painting or cycle consistency. Both of these two formulations encourage the generation networks to reconstruct the input image in a self-supervised manner. However, existing methods do not differentiate clothing and non-clothing regions. A straightforward generation impedes the virtual try-on quality because of the heavily coupled image contents. In this paper, we propose a Disentangled Cycle-consistency Try-On Network (DCTON). The DCTON is able to produce highly-realistic try-on images by disentangling important components of virtual try-on including clothes warping, skin synthesis, and image composition. Moreover, DCTON can be naturally trained in a self-supervised manner following cycle consistency learning. Extensive experiments on challenging benchmarks show that DCTON outperforms state-of-the-art approaches favorably.

关键词： computer vision Clothing Benchmark testing Skin pattern recognition Image reconstruction

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SwiftNet: Real-time Video Object Segmentation

SwiftNet: Real-time Video Object Segmentation

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

作者： Wang, Haochen Jiang, Xiaolong Ren, Haibing Hu, Yao Bai, Song Alibaba Youku Cognit & Intelligent Lab Beijing Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9781665445092

In this work we present SwiftNet for real-time semisupervised video object segmentation (one-shot VOS), which reports 77.8% J&F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance. We achieve this by elaborately compressing spatiotemporal redundancy in matching-based VOS via Pixel-Adaptive Memory (PAM). Temporally, PAM adaptively triggers memory updates on frames where objects display noteworthy inter-frame variations. Spatially, PAM selectively performs memory update and match on dynamic pixels while ignoring the static ones, significantly reducing redundant computations wasted on segmentation-irrelevant pixels. To promote efficient reference encoding, light-aggregation encoder is also introduced in SwiftNet deploying reversed sub-pixel. We hope SwiftNet could set a strong and efficient baseline for real-time VOS and facilitate its application in mobile vision.

关键词： computer vision Codes Redundancy Memory management Object segmentation Streaming media Real-time systems

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Animating Pictures with Eulerian Motion Fields

Animating Pictures with Eulerian Motion Fields

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

作者： Holynski, Aleksander Curless, Brian Seitz, Steven M. Szeliski, Richard Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781665445092

In this paper, we demonstrate a fully automatic method for converting a still image into a realistic animated looping video. We target scenes with continuous fluid motion, such as flowing water and billowing smoke. Our method relies on the observation that this type of natural motion can be convincingly reproduced from a static Eulerian motion description, i.e. a single, temporally constant flow field that defines the immediate motion of a particle at a given 2D location. We use an image-to-image translation network to encode motion priors of natural scenes collected from online videos, so that for a new photo, we can synthesize a corresponding motion field. The image is then animated using the generated motion through a deep warping technique: pixels are encoded as deep features, those features are warped via Eulerian motion, and the resulting warped feature maps are decoded as images. In order to produce continuous, seamlessly looping video textures, we propose a novel video looping technique that flows features both forward and backward in time and then blends the results. We demonstrate the effectiveness and robustness of our method by applying it to a large collection of examples including beaches, waterfalls, and flowing rivers.

关键词： computer vision Fluids Animation Robustness Rivers pattern recognition

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Video Object Segmentation Using Global and Instance Embedding Learning

Video Object Segmentation Using Global and Instance Embeddin...

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

作者： Ge, Wenbin Lu, Xiankai Shen, Jianbing Beijing Inst Technol Beijing Peoples R China Shandong Univ Sch Software Jinan Peoples R China Incept Inst Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose a feature embedding based video object segmentation (VOS) method which is simple, fast and effective. The current VOS task involves two main challenges: object instance differentiation and cross-frame instance alignment. Most state-of-the-art matching based VOS methods simplify this task into a binary segmentation task and tackle each instance independently. In contrast, we decompose the VOS task into two subtasks: global embedding learning that segments foreground objects of each frame in a pixel-to-pixel manner, and instance feature embedding learning that separates instances. The outputs of these two subtasks are fused to obtain the final instance masks quickly and accurately. Through using the relation among different instances per-frame as well as temporal relation across different frames, the proposed network learns to differentiate multiple instances and associate them properly in one feed-forward manner. Extensive experimental results on the challenging DAVIS[34] and YoutubeVOS [57] datasets show that our method achieves better performances than most counterparts in each case.

关键词： computer vision Annotations Object segmentation pattern recognition Task analysis

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Source-Free Domain Adaptation for Semantic Segmentation

Source-Free Domain Adaptation for Semantic Segmentation

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

作者： Liu, Yuang Zhang, Wei Wang, Jun East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network (CNN)-based approaches for semantic segmentation heavily rely on the pixel-level annotated data, which is labor-intensive. However, existing UDA approaches in this regard inevitably require the full access to source datasets to reduce the gap between the source and target domains during model adaptation, which are impractical in the real scenarios where the source datasets are private, and thus cannot be released along with the well-trained source models. To cope with this issue, we propose a source-free domain adaptation framework for semantic segmentation, namely SFDA, in which only a well-trained source model and an unlabeled target domain dataset are available for adaptation. SFDA not only enables to recover and preserve the source domain knowledge from the source model via knowledge transfer during model adaptation, but also distills valuable information from the target domain for self-supervised learning. The pixel- and patch-level optimization objectives tailored for semantic segmentation are seamlessly integrated in the framework. The extensive experimental results on numerous benchmark datasets highlight the effectiveness of our framework against the existing UDA approaches relying on source data.

关键词： Adaptation models Image segmentation computer vision Semantics Benchmark testing pattern recognition Convolutional neural networks

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