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

共 30976 条记录，以下是4911-4920 订阅

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Effectively Leveraging Attributes for Visual Similarity

Effectively Leveraging Attributes for Visual Similarity

引用

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

作者： Mishra, Samarth Zhang, Zhongping Shen, Yuan Kumar, Ranjitha Saligrama, Venkatesh Plummer, Bryan Boston Univ Boston MA 02215 USA Univ Illinois Champaign IL USA

ISBN: (纸本)9781665448994

Measuring similarity between two images often requires performing complex reasoning along different axes (e.g., color, texture, or shape). Insights into what might be important for measuring similarity can be provided by annotated attributes. Prior work tends to view these annotations as complete, resulting in them using a simplistic approach of predicting attributes on single images, which are, in turn, used to measure similarity. However, it is impractical for a dataset to fully annotate every attribute that may be important. Thus, only representing images based on these incomplete annotations may miss out on key information. To address this issue, we propose the Pairwise Attribute-informed similarity Network (PAN), which breaks similarity learning into capturing similarity conditions and relevance scores from a joint representation of two images. This enables our model to identify that two images contain the same attribute, but can have it deemed irrelevant (e.g., due to fine-grained differences between them) and ignored for measuring similarity between the two images. Notably, while prior methods of using attribute annotations are often unable to outperform prior art, PAN obtains a 4-9% improvement on compatibility prediction between clothing items on Polyvore Outfits and a 5% gain on few shot classification of images using Caltech-UCSD Birds (CUB), and over 1% boost to Recall@1 on In-Shop Clothes Retrieval.

关键词： Visualization computer vision Annotations Shape Image color analysis Shape measurement conferences

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Polymorphic-GAN: Generating Aligned Samples across Multiple Domains with Learned Morph Maps

Polymorphic-GAN: Generating Aligned Samples across Multiple ...

引用

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

作者： Kim, Seung Wook Kreis, Karsten Li, Daiqing Torralba, Antonio Fidler, Sanja NVIDIA Santa Clara CA 95051 USA Univ Toronto Toronto ON Canada Vector Inst Bengaluru Karnataka India MIT Cambridge MA 02139 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Modern image generative models show remarkable sample quality when trained on a single domain or class of objects. In this work, we introduce a generative adversarial network that can simultaneously generate aligned image samples from multiple related domains. We leverage the fact that a variety of object classes share common attributes, with certain geometric differences. We propose Polymorphic-GAN which learns shared features across all domains and a per-domain morph layer to morph shared features according to each domain. In contrast to previous works, our framework allows simultaneous modelling of images with highly varying geometries, such as images of human faces, painted and artistic faces, as well as multiple different animal faces. We demonstrate that our model produces aligned samples for all domains and show how it can be used for applications such as segmentation transfer and cross-domain image editing, as well as training in low-data regimes. Additionally, we apply our Polymorphic-GAN on image-to-image translation tasks and show that we can greatly surpass previous approaches in cases where the geometric differences between domains are large.

关键词： Training Geometry Image segmentation computer vision Shape Generative adversarial networks Generators

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METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens

METransformer: Radiology Report Generation by Transformer wi...

引用

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

作者： Wang, Zhanyu Liu, Lingqiao Wang, Lei Zhou, Luping Univ Sydney Sydney NSW Australia Univ Adelaide Adelaide SA Australia Univ Wollongong Wollongong NSW Australia

ISBN: (纸本)9798350301298

In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a "multi-expert joint diagnosis" mechanism to upgrade the existing "single expert" framework commonly seen in the current literature. To this end, we propose METransformer, a method to realize this idea with a transformer-based backbone. The key design of our method is the introduction of multiple learnable "expert" tokens into both the transformer encoder and decoder. In the encoder, each expert token interacts with both vision tokens and other expert tokens to learn to attend different image regions for image representation. These expert tokens are encouraged to capture complementary information by an orthogonal loss that minimizes their overlap. In the decoder, each attended expert token guides the cross-attention between input words and visual tokens, thus influencing the generated report. A metrics-based expert voting strategy is further developed to generate the final report. By the multi-experts concept, our model enjoys the merits of an ensemble-based approach but through a manner that is computationally more efficient and supports more sophisticated interactions among experts. Experimental results demonstrate the promising performance of our proposed model on two widely used benchmarks. Last but not least, the framework-level innovation makes our work ready to incorporate advances on existing "single-expert" models to further improve its performance.

关键词： and reasoning language vision

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Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation

Exploring Regional Clues in CLIP for Zero-Shot Semantic Segm...

引用

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

作者： Zhang, Yi Guo, Meng-Hao Wang, Miao Hu, Shi-Min Beihang Univ State Key Lab Virtual Real Technol & Syst SCSE Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol BNRist Beijing Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

CLIP has demonstrated marked progress in visual recognition due to its powerful pre-training on large-scale image-text pairs. However, it still remains a critical challenge: how to transfer image-level knowledge into pixel-level understanding tasks such as semantic segmentation. In this paper, to solve the mentioned challenge, we analyze the gap between the capability of the CLIP model and the requirement of the zero-shot semantic segmentation task. Based on our analysis and observations, we propose a novel method for zero-shot semantic segmentation, dubbed CLIP-RC (CLIP with Regional Clues), bringing two main insights. On the one hand, a region-level bridge is necessary to provide fine-grained semantics. On the other hand, overfitting should be mitigated during the training stage. Benefiting from the above discoveries, CLIP-RC achieves state-of-the-art performance on various zero-shot semantic segmentation benchmarks, including PASCAL VOC, PASCAL Context, and COCO-Stuff 164K. Code will be available at https://***/Jittor/JSeg.

关键词： computer vision semantic segmentation zero-shot semantic segmentation

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Class-Aware Contrastive Semi-Supervised Learning

Class-Aware Contrastive Semi-Supervised Learning

引用

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

作者： Yang, Fan Wu, Kai Zhang, Shuyi Jiang, Guannan Liu, Yong Zheng, Feng Zhang, Wei Wang, Chengjie Zeng, Long Tencent Youtu Lab Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China Southern Univ Sci & Technol Shenzhen Peoples R China CATL Xining Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization. However, its training procedure suffers from confirmation bias due to the noise contained in self-generated artificial labels. Moreover, the model's judgment becomes noisier in real-world applications with extensive out-of-distribution data. To address this issue, we propose a general method named Class-aware Contrastive Semi-Supervised Learning (CCSSL), which is a drop-in helper to improve the pseudo-label quality and enhance the model's robustness in the real-world setting. Rather than treating real-world data as a union set, our method separately handles reliable in-distribution data with class-wise clustering for blending into downstream tasks and noisy out-of-distribution data with image-wise contrastive for better generalization. Furthermore, by applying target reweighting, we successfully emphasize clean label learning and simultaneously reduce noisy label learning. Despite its simplicity, our proposed CCSSL has significant performance improvements over the state-of-the-art SSL methods on the standard datasets CIFAR100 [18] and STL10 [8]. On the real-world dataset Semi-iNat 2021 [27], we improve FixMatch [25] by 9.80% and CoMatch [19] by 3.18%. Code is available https://***/TencentYoutuResearch/Classification-SemiCLS.

关键词： Training computer vision Semisupervised learning Robustness Data models pattern recognition Noise measurement

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Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation

Exploring and Distilling Posterior and Prior Knowledge for R...

引用

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

作者： Liu, Fenglin Wu, Xian Ge, Shen Fan, Wei Zou, Yuexian Peking Univ Sch ECE ADSPLAB Beijing Peoples R China Tencent Med AI Lab Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Automatically generating radiology reports can improve current clinical practice in diagnostic radiology. On one hand, it can relieve radiologists from the heavy burden of report writing;On the other hand, it can remind radiologists of abnormalities and avoid the misdiagnosis and missed diagnosis. Yet, this task remains a challenging job for data-driven neural networks, due to the serious visual and textual data biases. To this end, we propose a Posterior-and-Prior Knowledge Exploring-and-Distilling approach (PPKED) to imitate the working patterns of radiologists, who will first examine the abnormal regions and assign the disease topic tags to the abnormal regions, and then rely on the years of prior medical knowledge and prior working experience accumulations to write reports. Thus, the PPKED includes three modules: Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE) and Multi-domain Knowledge Distiller (MKD). In detail, PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias;PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias. The explored knowledge is distilled by the MKD to generate the final reports. Evaluated on MIMIC-CXR and IU-Xray datasets, our method is able to outperform previous state-of-the-art models on these two datasets.

关键词： Visualization computer vision MIMICs Neural networks Radiology Writing pattern recognition

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Learnable Lookup Table for Neural Network Quantization

Learnable Lookup Table for Neural Network Quantization

引用

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

作者： Wang, Longguang Dong, Xiaoyu Wang, Yingqian Liu, Li An, Wei Guo, Yulan Natl Univ Def Technol Changsha Hunan Peoples R China Univ Tokyo Tokyo Japan RIKEN AIP Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Neural network quantization aims at reducing bit-widths of weights and activations for memory and computational efficiency. Since a linear quantizer (i.e., round(.) function) cannot well fit the bell-shaped distributions of weights and activations, many existing methods use pre-defined functions (e.g., exponential function) with learnable parameters to build the quantizer for joint optimization. However, these complicated quantizers introduce considerable computational overhead during inference since activation quantization should be conducted online. In this paper, we formulate the quantization process as a simple lookup operation and propose to learn lookup tables as quantizers. Specifically, we develop differentiable lookup tables and introduce several training strategies for optimization. Our lookup tables can be trained with the network in an endto-end manner to fit the distributions in different layers and have very small additional computational cost. Comparison with previous methods show that quantized networks using our lookup tables achieve state-of-the-art performance on image classification, image super-resolution, and point cloud classification tasks.

关键词： Training Point cloud compression Quantization (signal) Neural networks Superresolution Computational efficiency pattern recognition

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DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients

DeepLSD: Line Segment Detection and Refinement with Deep Ima...

引用

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

作者： Pautrat, Remi Barath, Daniel Larsson, Viktor Oswald, Martin R. Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Lund Univ Lund Sweden Univ Amsterdam Amsterdam Netherlands Microsoft Mixed Real & AI Zurich Lab Zurich Switzerland

ISBN: (纸本)9798350301298

Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. Their learned counterparts are more repeatable and can handle challenging images, but at the cost of a lower accuracy and a bias towards wireframe lines. We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector that can be trained in the wild without ground truth lines. Our new line segment detector, DeepLSD, processes images with a deep network to generate a line attraction field, before converting it to a surrogate image gradient magnitude and angle, which is then fed to any existing handcrafted line detector. Additionally, we propose a new optimization tool to refine line segments based on the attraction field and vanishing points. This refinement improves the accuracy of current deep detectors by a large margin. We demonstrate the performance of our method on low-level line detection metrics, as well as on several downstream tasks using multiple challenging datasets. The source code and models are available at https://***/cvg/DeepLSD.

关键词： Low-level vision

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Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes

Synthesizing Long-Term 3D Human Motion and Interaction in 3D...

引用

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

作者： Wang, Jiashun Xu, Huazhe Xu, Jingwei Liu, Sifei Wang, Xiaolong Univ Calif San Diego San Diego CA 92093 USA Univ Calif Berkeley Berkeley CA USA Shanghai Jiao Tong Univ Shanghai Peoples R China NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity. While many efforts have been made on generating realistic and natural human motion, most approaches neglect the importance of modeling human-scene interactions and affordance. On the other hand, affordance reasoning (e.g., standing on the floor or sitting on the chair) has mainly been studied with static human pose and gestures, and it has rarely been addressed with human motion. In this paper, we propose to bridge human motion synthesis and scene affordance reasoning. We present a hierarchical generative framework to synthesize long-term 3D human motion conditioning on the 3D scene structure. Building on this framework, we further enforce multiple geometry constraints between the human mesh and scene point clouds via optimization to improve realistic synthesis. Our experiments show significant improvements over previous approaches on generating natural and physically plausible human motion in a scene.

关键词： Graphics Geometry computer vision Three-dimensional displays Affordances Computational modeling Cognition

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City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones

City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad...

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

作者： Liu, Chong Zhang, Yuqi Luo, Hao Tang, Jiasheng Chen, Weihua Xu, Xianzhe Wang, Fan Li, Hao Shen, Yi-Dong Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Alibaba Grp Machine Intelligence Technol Lab Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions. This paper describes our solution to the Track 3 multi-camera vehicle tracking task in 2021 AI City Challenge (AICITY21). This paper proposes a multi-target multi-camera vehicle tracking framework guided by the crossroad zones. The framework includes: (1) Use mature detection and vehicle re-identification models to extract targets and appearance features. (2) Use modified JDE-Tracker (without detection module) to track single-camera vehicles and generate single-camera tracklets. (3) According to the characteristics of the crossroad, the Tracklet Filter Strategy and the Direction Based Temporal Mask are proposed. (4) Propose Sub-clustering in Adjacent Cameras for multi-camera tracklets matching. Through the above techniques, our method obtained an IDF1 score of 0.8095, ranking first on the leaderboard (1). The code will be released later.

关键词： Matched filters computer vision Target tracking conferences Urban areas Feature extraction Cameras

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