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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4831-4840 订阅

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Unsupervised Learning of 3D Object Categories from Videos in the Wild

Unsupervised Learning of 3D Object Categories from Videos in...

引用

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

作者： Henzler, Philipp Reizenstein, Jeremy Labatut, Patrick Shapovalov, Roman Ritschel, Tobias Vedaldi, Andrea Novotny, David UCL London England Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

Our goal is to learn a deep network that, given a small number of images of an object of a given category, reconstructs it in 3D. While several recent works have obtained analogous results using synthetic data or assuming the availability of 2D primitives such as keypoints, we are interested in working with challenging real data and with no manual annotations. We thus focus on learning a model from multiple views of a large collection of object instances. We contribute with a new large dataset of object centric videos suitable for training and benchmarking this class of models. We show that existing techniques leveraging meshes, voxels, or implicit surfaces, which work well for reconstructing isolated objects, fail on this challenging data. Finally, we propose a new neural network design, called warp-conditioned ray embedding (WCR), which significantly improves reconstruction while obtaining a detailed implicit representation of the object surface and texture, also compensating for the noise in the initial SfM reconstruction that bootstrapped the learning process. Our evaluation demonstrates performance improvements over several deep monocular reconstruction baselines on existing benchmarks and on our novel dataset. For additional material please visit: https://***/ publication/unsupervised_videos/.

关键词： Training Surface reconstruction Three-dimensional displays Benchmark testing Surface texture pattern recognition Image reconstruction

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Focal and Global Knowledge Distillation for Detectors

Focal and Global Knowledge Distillation for Detectors

引用

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

作者： Yang, Zhendong Li, Zhe Jiang, Xiaohu Gong, Yuan Yuan, Zehuan Zhao, Danpei Yuan, Chun Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China ByteDance Inc Beijing Peoples R China BeiHang Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Knowledge distillation has been applied to image classification successfully. However, object detection is much more sophisticated and most knowledge distillation methods have failed on it. In this paper, we point out that in object detection, the features of the teacher and student vary greatly in different areas, especially in the foreground and background. If we distill them equally, the uneven differences between feature maps will negatively affect the distillation. Thus, we propose Focal and Global Distillation (FGD). Focal distillation separates the foreground and background, forcing the student to focus on the teacher's critical pixels and channels. Global distillation rebuilds the relation between different pixels and transfers it from teachers to students, compensating for missing global information in focal distillation. As our method only needs to calculate the loss on the feature map, FGD can be applied to various detectors. We experiment on various detectors with different backbones and the results show that the student detector achieves excellent mAP improvement. For example, ResNet-50 based RetinaNet, Faster RCNN, RepPoints and Mask RCNN with our distillation method achieve 40.7%, 42.0%, 42.0% and 42.1% mAP on COCO2017, which are 3.3, 3.6, 3.4 and 2.9 higher than the baseline, respectively. Our codes are available at https://***/yzd-v/FGD.

关键词： computer vision Codes Detectors Object detection Feature extraction pattern recognition Image classification

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High-resolution Face Swapping via Latent Semantics Disentanglement

High-resolution Face Swapping via Latent Semantics Disentang...

引用

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

作者： Xu, Yangyang Deng, Bailin Wang, Junle Jing, Yanqing Pan, Jia He, Shengfeng South China Univ Technol Guangzhou Peoples R China Cardiff Univ Cardiff Wales Tencent Shenzhen Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. Although previous research can leverage generative priors to produce high-resolution results, their quality can suffer from the entangled semantics of the latent space. We explicitly disentangle the latent semantics by utilizing the progressive nature of the generator, deriving structure attributes from the shallow layers and appearance attributes from the deeper ones. Identity and pose information within the structure attributes are further separated by introducing a landmark-driven structure transfer latent direction. The disentangled latent code produces rich generative features that incorporate feature blending to produce a plausible swapping result. We further extend our method to video face swapping by enforcing two spatio-temporal constraints on the latent space and the image space. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art image/video face swapping methods in terms of hallucination quality and consistency.

关键词： computer vision Codes Face recognition Semantics Generators

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3MASSIV Multilingual, Multimodal and Multi-Aspect dataset of Social Media Short Videos

3MASSIV Multilingual, Multimodal and Multi-Aspect dataset of...

引用

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

作者： Gupta, Vikram Mittal, Trisha Mathur, Puneet Mishra, Vaibhav Maheshwari, Mayank Bera, Aniket Mukherjee, Debdoot Manocha, Dinesh ShareChat Bangalore Karnataka India Univ Maryland College Pk MD 20742 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present 3MASSIV, a multilingual, multimodal and multi-aspect, expertly-annotated dataset of diverse short videos extracted from short-video social media platform Maj. 3MASSIV comprises of 50k short videos (20 seconds average duration) and 100K unlabeled videos in 11 different languages and captures popular short video trends like pranks, fails, romance, comedy expressed via unique audio-visual formats like self-shot videos, reaction videos, lip-synching, self-sung songs, etc. 3MASSIV presents an opportunity for multimodal and multilingual semantic understanding on these unique videos by annotating them for concepts, affective states, media types, and audio language. We present a thorough analysis of 3MASSIV and highlight the variety and unique aspects of our dataset compared to other contemporary popular datasets with strong baselines. We also show how the social media content in 3MASSIV is dynamic and temporal in nature, which can be used for semantic understanding tasks and cross-lingual analysis.

关键词： computer vision Social networking (online) Semantics Media Market research pattern recognition Task analysis

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PhD Learning: Learning with Pompeiu-hausdorff Distances for Video-based Vehicle Re-Identification

PhD Learning: Learning with Pompeiu-hausdorff Distances for ...

引用

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

作者： Zhao, Jianan Qi, Fengliang Ren, Guangyu Xu, Lin Shanghai Em Data Technol Co Ltd Shanghai Peoples R China Imperial Coll London London England

ISBN: (纸本)9781665445092

Vehicle re-identification (re-ID) is of great significance to urban operation, management, security and has gained more attention in recent years. However, two critical challenges in vehicle re-ID have primarily been underestimated, i.e., 1): how to make full use of raw data, and 2): how to learn a robust re-ID model with noisy data. In this paper, we first create a video vehicle re-ID evaluation benchmark called VVeRI-901 and verify the performance of video-based re-ID is far better than static image-based one. Then we propose a new Pompeiu-hausdorff distance (PhD) learning method for video-to-video matching. It can alleviate the data noise problem caused by the occlusion in videos and thus improve re-ID performance significantly. Extensive empirical results on video-based vehicle and person reID datasets, i.e., VVeRI-901, MARS and PRID2011, demonstrate the superiority of the proposed method.

关键词： Learning systems Visualization Surveillance Resists Benchmark testing Data models pattern recognition

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computer vision and pattern recognition Cathedral Hill Hotel San Francisco, California June 9-1 3, 1985

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computer 1985年第3期18卷 116-117页

Provides a listing of upcoming conference events of interest to practitioners and researchers.

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Learning a Proposal Classifier for Multiple Object Tracking

Learning a Proposal Classifier for Multiple Object Tracking

引用

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

作者： Dai, Peng Weng, Renliang Choi, Wongun Zhang, Changshui He, Zhangping Ding, Wei Tsinghua Univ Beijing Peoples R China Aibee Inc Beijing Peoples R China

ISBN: (纸本)9781665445092

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. However, it is not trivial to solve the data-association problem in an end-to-end fashion. In this paper, we propose a novel proposal-based learnable framework, which models MOT as a proposal generation, proposal scoring and trajectory inference paradigm on an affinity graph. This framework is similar to the two-stage object detector Faster RCNN, and can solve the MOT problem in a data-driven way. For proposal generation, we propose an iterative graph clustering method to reduce the computational cost while maintaining the quality of the generated proposals. For proposal scoring, we deploy a trainable graph-convolutional-network (GCN) to learn the structural patterns of the generated proposals and rank them according to the estimated quality scores. For trajectory inference, a simple deoverlapping strategy is adopted to generate tracking output while complying with the constraints that no detection can be assigned to more than one track. We experimentally demonstrate that the proposed method achieves a clear performance improvement in both MOTA and IDF1 with respect to previous state-of-the-art on two public benchmarks.

关键词： Deep learning computer vision Detectors Market research Trajectory Computational efficiency Proposals

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Learning to Learn Image Classifiers with Visual Analogy 32

Learning to Learn Image Classifiers with Visual Analogy

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhou, Linjun Cui, Peng Yang, Shiqiang Zhu, Wenwu Tian, Qi Tsinghua Univ Beijing Peoples R China Huawei Noahs Ark Lab Beijing Peoples R China

ISBN: (纸本)9781728132938

Humans are far better learners who can learn a new concept very fast with only a few samples compared with machines. The plausible mystery making the difference is two fundamental learning mechanisms: learning to learn and learning by analogy. In this paper, we attempt to investigate a new human-like learning method by organically combining these two mechanisms. In particular, we study how to generalize the classification parameters from previously learned concepts to a new concept. we first propose a novel Visual Analogy Graph Embedded Regression (VAGER) model to jointly learn a low-dimensional embedding space and a linear mapping function from the embedding space to classification parameters for base classes. We then propose an out-of-sample embedding method to learn the embedding of a new class represented by a few samples through its visual analogy with base classes and derive the classification parameters for the new class. We conduct extensive experiments on ImageNet dataset and the results show that our method could consistently and significantly outperform state-of-the-art baselines.

关键词： Categorization computer vision Theory Deep Learning recognition: Detection Retrieval

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Pollen classification using brightness-based and shape-based descriptors

Pollen classification using brightness-based and shape-based...

引用

17th International conference on pattern recognition (ICPR)

作者： Rodríguez-Damián, M Cernadas, E Formella, A Sá-Otero, P Univ Vigo Dept Informat E-32004 Orense Spain

ISBN: (纸本)0769521282

Pollen grain classification have recently received more attention from computer vision researchers. To distinguish among taxa, palynologist make direct use of keys such as the size, exine structure and sculpture of the pollen grains. We propose a framework in which the pollen grains of each taxa are characterized using brightness and shape descriptors derived from their intensity images. These descriptors are associated to the ornamentation and morphology of the pollen grain. The method is statistically evaluated on preparations containing species of the Urticaceae family.

关键词： computer vision

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SAM-CLIP: Merging vision Foundation Models towards Semantic and Spatial Understanding

SAM-CLIP: Merging Vision Foundation Models towards Semantic ...

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

作者： Wang, Haoxiang Vasu, Pavan Kumar Anasosalu Faghri, Fartash Vemulapalli, Raviteja Farajtabar, Mehrdad Mehta, Sachin Rastegari, Mohammad Tuzel, Oncel Pouransari, Hadi Apple Cupertino CA 95014 USA Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9798350365474

The landscape of publicly available vision foundation models (VFMs), such as CLIP and Segment Anything Model (SAM), is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their pre-training objectives. For instance, CLIP excels in semantic understanding, while SAM specializes in spatial understanding for segmentation. In this work, we introduce a simple recipe to efficiently merge VFMs into a unified model that absorbs their expertise. Our method integrates techniques of multi-task learning, continual learning, and distillation. Further, it demands significantly less computational cost compared to traditional multi-task training from scratch, and it only needs a small fraction of the pre-training datasets that were initially used to train individual models. By applying our method to SAM and CLIP, we obtain SAM-CLIP : a unified model that combines the capabilities of SAM and CLIP into a single vision transformer. Compared with deploying SAM and CLIP independently, our merged model, SAM-CLIP, reduces storage and compute costs for inference, making it well-suited for edge device applications. We show that SAM-CLIP not only retains the foundational strengths of SAM and CLIP, but also introduces synergistic functionalities, notably in zero-shot semantic segmentation, where SAM-CLIP establishes new state-of-the-art results on 5 benchmarks. It outperforms previous models that are specifically designed for this task by a large margin, including +6.8% and +5.9% mean IoU improvement on Pascal-VOC and COCO-Stuff datasets, respectively.

关键词： CLIP Foundation Model Model Merging Segmentation

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