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共 11897 条记录，以下是1661-1670 订阅

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Self-Supervised Equivariant Learning for Oriented Keypoint Detection

Self-Supervised Equivariant Learning for Oriented Keypoint D...

引用

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

作者： Lee, Jongmin Kim, Byungjin Cho, Minsu Pohang Univ Sci & Technol POSTECH Pohang South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Detecting robust keypoints from an image is an integral part of many computer vision problems, and the characteristic orientation and scale of keypoints play an important role for keypoint description and matching. Existing learning-based methods for keypoint detection rely on standard translation-equivariant CNNs but often fail to detect reliable keypoints against geometric variations. To learn to detect robust oriented keypoints, we introduce a self-supervised learning framework using rotation-equivariant CNNs. We propose a dense orientation alignment loss by an image pair generated by synthetic transformations for training a histogram-based orientation map. Our method outperforms the previous methods on an image matching benchmark and a camera pose estimation benchmark.

关键词： Training computer vision Histograms Image matching Pose estimation Self-supervised learning Benchmark testing

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Modality-Aware Representation Learning for Zero-shot Sketch-based Image Retrieval

Modality-Aware Representation Learning for Zero-shot Sketch-...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Lyou, Eunyi Lee, Doyeon Kim, Jooeun Lee, Joonseok Seoul Natl Univ Seoul South Korea Google Res Mountain View CA USA

ISBN: (纸本)9798350318920;9798350318937

Zero-shot learning offers an efficient solution for a machine learning model to treat unseen categories, avoiding exhaustive data collection. Zero-shot Sketch-based Image Retrieval (ZS-SBIR) simulates real-world scenarios where it is hard and costly to collect paired sketch-photo samples. We propose a novel framework that indirectly aligns sketches and photos by contrasting them through texts, removing the necessity of access to sketch-photo pairs. With an explicit modality encoding learned from data, our approach disentangles modality-agnostic semantics from modality-specific information, bridging the modality gap and enabling effective cross-modal content retrieval within a joint latent space. From comprehensive experiments, we verify the efficacy of the proposed model on ZS-SBIR, and it can be also applied to generalized and fine-grained settings.

关键词： Algorithms Algorithms Image recognition and understanding vision + language and/or other modalities

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Input-level Inductive Biases for 3D Reconstruction

Input-level Inductive Biases for 3D Reconstruction

引用

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

作者： Wang Yifan Doersch, Carl Arandjelovic, Relja Carreira, Joao Zisserman, Andrew Swiss Fed Inst Technol Zurich Switzerland DeepMind London England Univ Oxford Dept Engn Sci VGG Oxford England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Much of the recent progress in 3D vision has been driven by the development of specialized architectures that incorporate geometrical inductive biases. In this paper we tackle 3D reconstruction using a domain agnostic architecture and study how to inject the same type of inductive biases directly as extra inputs to the model. This approach makes it possible to apply existing general models, such as Perceivers, on this rich domain, without the need for architectural changes, while simultaneously maintaining data efficiency of bespoke models. In particular we study how to encode cameras, projective ray incidence and epipolar geometry as model inputs, and demonstrate competitive multi-view depth estimation performance on multiple benchmarks.

关键词： Geometry Solid modeling computer vision Three-dimensional displays Computational modeling Estimation computer architecture

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Token Fusion: Bridging the Gap between Token Pruning and Token Merging.

Token Fusion: Bridging the Gap between Token Pruning and Tok...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Kim, Minchul Gao, Shangqian Hsu, Yen-Chang Shen, Yilin Jin, Hongxia Michigan State Univ E Lansing MI 48824 USA Samsung Res Amer Mountain View CA USA

ISBN: (纸本)9798350318920;9798350318937

vision Transformers (ViTs) have emerged as powerful backbones in computer vision, outperforming many traditional CNNs. However, their computational overhead, largely attributed to the self-attention mechanism, makes deployment on resource-constrained edge devices challenging. Multiple solutions rely on token pruning or token merging. In this paper, we introduce"Token Fusion" (ToFu), a method that amalgamates the benefits of both token pruning and token merging. Token pruning proves advantageous when the model exhibits sensitivity to input interpolations, while token merging is effective when the model manifests close to linear responses to inputs. We combine this to propose a new scheme called Token Fusion. Moreover, we tackle the limitations of average merging, which doesn't preserve the intrinsic feature norm, resulting in distributional shifts. To mitigate this, we introduce MLERP merging, a variant of the SLERP technique, tailored to merge multiple tokens while maintaining the norm distribution. ToFu is versatile, applicable to ViTs with or without additional training. Our empirical evaluations indicate that ToFu establishes new benchmarks in both classification and image generation tasks concerning computational efficiency and model accuracy.

关键词： 3D Algorithms Algorithms Algorithms and algorithms etc. formulations Generative models for image Image recognition and understanding Machine learning architectures video

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Accelerating Video Object Segmentation with Compressed Video

Accelerating Video Object Segmentation with Compressed Video

引用

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

作者： Xu, Kai Yao, Angela Natl Univ Singapore Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose an efficient plug-and-play acceleration framework for semi-supervised video object segmentation by exploiting the temporal redundancies in videos presented by the compressed bitstream. Specifically, we propose a motion vector-based warping method for propagating segmentation masks from keyframes to other frames in a bidirectional and multi-hop manner. Additionally, we introduce a residual-based correction module that can fix wrongly propagated segmentation masks from noisy or erroneous motion vectors. Our approach is flexible and can be added on top of several existing video object segmentation algorithms. We achieved highly competitive results on DAVIS17 and YouTube-VOS on various base models with substantial speed-ups of up to 3.5X with minor drops in accuracy.(1)

关键词： computer vision Analytical models Tracking Shape Motion segmentation Computational modeling Redundancy

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PMVC: Promoting Multi-View Consistency for 3D Scene Reconstruction

PMVC: Promoting Multi-View Consistency for 3D Scene Reconstr...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Zhang, Chushan Tong, Jinguang Lin, Tao Jun Chuong Nguyen Li, Hongdong Australian Natl Univ Canberra ACT Australia CSIRO Data61 Canberra ACT Australia

ISBN: (纸本)9798350318920;9798350318937

Reconstructing the geometry of a 3D scene from its multi-view 2D observations has been a central task of 3D computer vision. Recent methods based on neural rendering that use implicit shape representations, such as the neural Signed Distance Function (SDF), have shown impressive performance. However, they fall short in recovering fine details in the scene, especially when employing a multilayer perceptron (MLP) as the interpolation function for the SDF representation. Per-frame image normal or depth-map prediction have been utilized to tackle this issue, but these learning-based depth/normal predictions are based on a single image frame only, hence overlooking the underlying multiview consistency of the scene, leading to inconsistent erroneous 3D reconstruction. To mitigate this problem, we propose to leverage multi-view deep features computed on the images. In addition, we employ an adaptive sampling strategy that assesses the fidelity of the multi-view image consistency. Our approach outperforms current state-of-the-art methods, delivering an accurate and robust scene representation with particularly enhanced details. The effectiveness of our proposed approach is evaluated by extensive experiments conducted on the ScanNet and Replica datasets, showing superior performance than the current state-of-the-art.

关键词： 3D computer vision Algorithms Algorithms Applications Image recognition and understanding Virtual / augmented reality

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Camera Pose Estimation using Implicit Distortion Models

Camera Pose Estimation using Implicit Distortion Models

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

作者： Pan, Linfei Pollefeys, Marc Larsson, Viktor Swiss Fed Inst Technol Zurich Switzerland Microsoft Redmond WA USA Lund Univ Lund Sweden

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric model with a regularization term that ensures the latent distortion map varies smoothly throughout the image. The proposed model is effectively parameter-free and allows us to optimize the 6 degree-of-freedom camera pose without explicitly knowing the intrinsic calibration. We show that the method is applicable to a wide selection of cameras with varying distortion and in multiple applications, such as visual localization and structure-from-motion.

关键词： Solid modeling computer vision Visualization Computational modeling Pose estimation Cameras Distortion

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SciOL and MuLMS-Img: Introducing A Large-Scale Multimodal Scientific Dataset and Models for Image-Text Tasks in the Scientific Domain

SciOL and MuLMS-Img: Introducing A Large-Scale Multimodal Sc...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Tarsi, Tim Adel, Heike Metzen, Jan Hendrik Zhang, Dan Finco, Matteo Friedrich, Annemarie Bosch Ctr Artificial Intelligence Renningen Germany Hsch Medien Stuttgart Germany Univ Augsburg Augsburg Germany Robert Bosch GmbH Renningen Germany

ISBN: (纸本)9798350318920;9798350318937

In scientific publications, a substantial part of the information is expressed via figures containing images and diagrams. Hence, the retrieval of relevant figures given a natural language query is an important real-world task. However, due to the lack of training and evaluation data, most existing approaches are either limited to one modality or focus on non-scientific domains, making their application to scientific publications challenging. In this paper, we address this gap by introducing two novel datasets: (1) SciOL, the largest openly-licensed pre-training corpus for multimodal models in the scientific domain, covering multiple sciences including materials science, physics, and computer science, and (2) MuLMSImg, a high-quality dataset in the materials science domain, manually annotated for various image-text tasks. Our experiments show that pre-training large-scale vision-language models on SciOL increases performance considerably across a broad variety of image-text tasks including figure type classification, optical character recognition, captioning, and figure retrieval. Using MuLMS-Img, we show that integrating text-based features extracted via a fine-tuned model for a specific domain can boost cross-modal scientific figure retrieval performance by up to 50%.

关键词： Algorithms Algorithms Datasets and evaluations vision + language and/or other modalities

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Cross-Domain Few-Shot Incremental Learning for Point-Cloud recognition

Cross-Domain Few-Shot Incremental Learning for Point-Cloud R...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Tan, Yuwen Xiang, Xiang Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Sensing 3D objects is critical when 2D object recognition is not accessible. A robot pre-trained on a large point-cloud dataset will encounter unseen classes of 3D objects after deploying it. Therefore, the robot should be able to learn continuously in real-world scenarios. Few-shot class-incremental learning (FSCIL) requires the model to learn from few-shot new examples continually and not forget past classes. However, there is an implicit but strong assumption in the FSCIL that the distribution of the base and incremental classes is the same. In this paper, we focus on cross-domain FSCIL for point-cloud recognition. We decompose the catastrophic forgetting into base class forgetting and incremental class forgetting and alleviate them separately. We utilize the base model to discriminate base samples and new samples by treating base samples as in-distribution samples, and new objects as out-of-distribution samples. We retain the base model to avoid catastrophic forgetting of base classes and train an extra domain-specific module for all new samples to adapt to new classes. At inference, we first discriminate whether the sample belongs to the base class or the new class. Once classified at the model level, test samples are then passed to the corresponding model for class-level classification. To better mitigate the forgetting of new classes, we adopt the soft label and hard label replay together. Extensive experiments on synthetic-to-real incremental 3D datasets show that our proposed method can balance the performance between the base and new objects and outperforms the previous state-of-the-art methods.

关键词： 3D computer vision Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Learning Visual Body-shape-Aware Embeddings for Fashion Compatibility

Learning Visual Body-shape-Aware Embeddings for Fashion Comp...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Pang, Kaicheng Zou, Xingxing Wong, Waikeung Hong Kong Polytech Univ Sch Fash & Text Lab Artificial Intelligence Design Hong Kong Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Body shape is a crucial factor in outfit recommendation. Previous studies that directly used body measurement data to investigate the relationship between body shape and outfit have achieved limited performance due to oversimplified body shape representations. This paper proposes a Visual Body-shape-Aware Network (ViBA-Net) to improve the fashion compatibility model's awareness of human body shape through visual-level information. Specifically, ViBA-Net consists of three modules: a body-shape embedding module, which extracts visual and anthropometric features of body shape from a newly introduced large-scale body shape dataset;an outfit embedding module, which learns the outfit representation based on visual features extracted from a try-on image and textual features extracted from fashion attributes;and a joint embedding module, which jointly models the relationship between the representations of body shape and outfit. ViBA-Net is designed to generate attribute-level explanations for the evaluation results based on the computed attention weights. The effectiveness of ViBA-Net is evaluated on two mainstream datasets through qualitative and quantitative analysis. Data and code are released1.

关键词： Algorithms Algorithms Applications Commercial / retail Image recognition and understanding vision + language and/or other modalities

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