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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

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Polygonal Point Set Tracking

Polygonal Point Set Tracking

引用

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

作者： Nam, Gunhee Heo, Miran Oh, Seoung Wug Lee, Joon-Young Kim, Seon Joo Lunit Inc Seoul South Korea Yonsei Univ Seoul South Korea Adobe Res Redmond WA USA

ISBN: (纸本)9781665445092

In this paper, we propose a novel learning-based polygonal point set tracking method. Compared to existing video object segmentation (VOS) methods that propagate pixel-wise object mask information, we propagate a polygonal point set over frames. Specifically, the set is defined as a subset of points in the target contour, and our goal is to track corresponding points on the target contour. Those outputs enable us to apply various visual effects such as motion tracking, part deformation, and texture mapping. To this end, we propose a new method to track the corresponding points between frames by the global-local alignment with delicately designed losses and regularization terms. We also introduce a novel learning strategy using synthetic and VOS datasets that makes it possible to tackle the problem without developing the point correspondence dataset. Since the existing datasets are not suitable to validate our method, we build a new polygonal point set tracking dataset and demonstrate the superior performance of our method over the baselines and existing contour-based VOS methods. In addition, we present visual-effects applications of our method on part distortion and text mapping.

关键词： Learning systems computer vision Target tracking Image analysis Object segmentation Visual effects Distortion

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RelTransformer: A Transformer-Based Long-Tail Visual Relationship recognition

RelTransformer: A Transformer-Based Long-Tail Visual Relatio...

引用

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

作者： Chen, Jun Agarwal, Aniket Abdelkarim, Sherif Zhu, Deyao Elhoseiny, Mohamed King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Indian Inst Technol Chennai Tamil Nadu India

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The visual relationship recognition (VRR) task aims at understanding the pairwise visual relationships between interacting objects in an image. These relationships typically have a long-tail distribution due to their compositional nature. This problem gets more severe when the vocabulary becomes large, rendering this task very challenging. This paper shows that modeling an effective message-passing flow through an attention mechanism can be critical to tackling the compositionality and long-tail challenges in VRR. The method, called RelTransformer, represents each image as a fully-connected scene graph and restructures the whole scene into the relation-triplet and global-scene contexts. It directly passes the message from each element in the relation-triplet and global-scene contexts to the target relation via self-attention. We also design a learnable memory to augment the long-tail relation representation learning. Through extensive experiments, we find that our model generalizes well on many VRR benchmarks. Our model outperforms the best-performing models on two large-scale long-tail VRR benchmarks, VG8K-LT (+2.0% overall acc) and GQA-LT (+26.0% overall acc), both having a highly skewed distribution towards the tail. It also achieves strong results on the VG200 relation detection task.

关键词： Representation learning Visualization Vocabulary Image recognition Tail Benchmark testing Transformers

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Mask3D: Pre-training 2D vision Transformers by Learning Masked 3D Priors

Mask3D: Pre-training 2D Vision Transformers by Learning Mask...

引用

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

作者： Hou, Ji Dai, Xiaoliang He, Zijian Dai, Angela Niessner, Matthias Meta Real Labs Menlo Pk CA 94025 USA Tech Univ Munich Munich Germany

ISBN: (纸本)9798350301298

Current popular backbones in computer vision, such as vision Transformers (ViT) and ResNets are trained to perceive the world from 2D images. However, to more effectively understand 3D structural priors in 2D backbones, we propose Mask3D to leverage existing large-scale RGB-D data in a self-supervised pre-training to embed these 3D priors into 2D learned feature representations. In contrast to traditional 3D contrastive learning paradigms requiring 3D reconstructions or multi-view correspondences, our approach is simple: we formulate a pre-text reconstruction task by masking RGB and depth patches in individual RGB-D frames. We demonstrate the Mask3D is particularly effective in embedding 3D priors into the powerful 2D ViT backbone, enabling improved representation learning for various scene understanding tasks, such as semantic segmentation, instance segmentation and object detection. Experiments show that Mask3D notably outperforms existing self-supervised 3D pre-training approaches on ScanNet, NYUv2, and Cityscapes image understanding tasks, with an improvement of +6.5% mIoU against the state-of-the-art Pri3D on ScanNet image semantic segmentation.

关键词： 3D from single images

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Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization

Learning to Refactor Action and Co-occurrence Features for T...

引用

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

作者： Xia, Kun Wang, Le Zhou, Sanping Zheng, Nanning Tang, Wei Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Univ Illinois Chicago IL USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The main challenge of Temporal Action Localization is to retrieve subtle human actions from various co-occurring ingredients, e.g., context and background, in an untrimmed video. While prior approaches have achieved substantial progress through devising advanced action detectors, they still suffer from these co-occurring ingredients which often dominate the actual action content in videos. In this paper, we explore two orthogonal but complementary aspects of a video snippet, i.e., the action features and the co-occurrence features. Especially, we develop a novel auxiliary task by decoupling these two types of features within a video snippet and recombining them to generate a new feature representation with more salient action information for accurate action localization. We term our method RefactorNet, which first explicitly factorizes the action content and regularizes its co-occurrence features, and then synthesizes a new actiondominated video representation. Extensive experimental results and ablation studies on THUMOS14 and ActivityNet v1.3 demonstrate that our new representation, combined with a simple action detector, can significantly improve the action localization performance.

关键词： Location awareness computer vision Tracking Detectors Feature extraction pattern recognition Task analysis

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Unified Language-driven Zero-shot Domain Adaptation

Unified Language-driven Zero-shot Domain Adaptation

引用

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

作者： Yang, Senqiao Tian, Zhuotao Jiang, Li Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Harbin Inst Technol Shenzhen Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China

ISBN: (纸本)9798350353006

This paper introduces Unified Language-driven Zero-shot Domain Adaptation ( ULDA), a novel task setting that enables a single model to adapt to diverse target domains without explicit domain-ID knowledge. We identify the constraints in the existing language-driven zero-shot domain adaptation task, particularly the requirement for domain IDs and domain-specific models, which may restrict flexibility and scalability. To overcome these issues, we propose a new framework for ULDA, consisting of Hierarchical Context Alignment (HCA), Domain Consistent Representation Learning (DCRL), and Text-Driven Rectifier (TDR). These components work synergistically to align simulated features with target text across multiple visual levels, retain semantic correlations between different regional representations, and rectify biases between simulated and real target visual features, respectively. Our extensive empirical evaluations demonstrate that this framework achieves competitive performance in both settings, surpassing even the model that requires domain-ID, showcasing its superiority and generalization ability. The proposed method is not only effective but also maintains practicality and efficiency, as it does not introduce additional computational costs during inference. The code is available on the project website(1).

关键词： Domain Adaptation Segmentation vision-Language Model

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Deep Hashing Network for Unsupervised Domain Adaptation 30

Deep Hashing Network for Unsupervised Domain Adaptation

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Venkateswara, Hemanth Eusebio, Jose Chakraborty, Shayok Panchanathan, Sethuraman Arizona State Univ Ctr Cognit Ubiquitous Comp Tempe AZ 85287 USA

ISBN: (纸本)9781538604571

In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. The dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation.

关键词： Neural networks Adaptation models Machine learning computer vision Training Data models

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DETRs with Hybrid Matching

DETRs with Hybrid Matching

引用

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

作者： Jia, Ding Yuan, Yuhui He, Haodi Wu, Xiaopei Yu, Haojun Lin, Weihong Sun, Lei Zhang, Chao Hu, Han Peking Univ Beijing Peoples R China Stanford Univ Stanford CA USA Zhejiang Univ Hangzhou Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections. This end-to-end signature is important for the versatility of DETR, and it has been generalized to broader vision tasks. However, we note that there are few queries assigned as positive samples and the one-to-one set matching significantly reduces the training efficacy of positive samples. We propose a simple yet effective method based on a hybrid matching scheme that combines the original one-to-one matching branch with an auxiliary one-to-many matching branch during training. Our hybrid strategy has been shown to significantly improve accuracy. In inference, only the original one-to-one match branch is used, thus maintaining the end-to-end merit and the same inference efficiency of DETR. The method is named H-DETR, and it shows that a wide range of representative DETR methods can be consistently improved across a wide range of visual tasks, including Deformable-DETR, PETRv2, PETR, and TransTrack, among others. Code is available at: https://***/HDETR.

关键词： detection recognition: Categorization retrieval

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ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization

ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised...

引用

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

作者： He, Bo Yang, Xitong Kang, Le Cheng, Zhiyu Zhou, Xin Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA Baidu Res Sunnyvale CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Weakly-supervised temporal action localization aims to recognize and localize action segments in untrimmed videos given only video-level action labels for training. Without the boundary information of action segments, existing methods mostly rely on multiple instance learning (MIL), where the predictions of unlabeled instances (i.e., video snippets) are supervised by classifying labeled bags (i.e., untrimmed videos). However, this formulation typically treats snippets in a video as independent instances, ignoring the underlying temporal structures within and across action segments. To address this problem, we propose ASM-Loc, a novel WTAL framework that enables explicit, action-aware segment modeling beyond standard MIL-based methods. Our framework entails three segment-centric components: (i) dynamic segment sampling for compensating the contribution of short actions;(ii) infra- and inter-segment attention for modeling action dynamics and capturing temporal dependencies;(iii) pseudo instance-level supervision for improving action boundary prediction. Furthermore, a multistep refinement strategy is proposed to progressively improve action proposals along the model training process. Extensive experiments on THUMOS-14 and ActivityNet-v1.3 demonstrate the effectiveness of our approach, establishing new state of the art on both datasets.

关键词： Training Location awareness computer vision Computational modeling Pipelines Predictive models pattern recognition

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InOut : Diverse Image Outpainting via GAN Inversion

InOut : Diverse Image Outpainting via GAN Inversion

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

作者： Cheng, Yen-Chi Lin, Chieh Hubert Lee, Hsin-Ying Ren, Jian Tulyakov, Sergey Yang, Ming-Hsuan Carnegie Mellon Univ Pittsburgh PA 15213 USA UC Merced Merced CA USA Snap Inc Santa Monica CA USA Yonsei Univ Seoul South Korea Google Res Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. Compared to inpainting - filling in missing pixels in a way coherent with the neighboring pixels - outpainting can be achieved in more diverse ways since the problem is less constrained by the surrounding pixels. Existing image outpainting methods pose the problem as a conditional image-to-image translation task, often generating repetitive structures and textures by replicating the content available in the input image. In this work, we formulate the problem from the perspective of inverting generative adversarial networks. Our generator renders micro-patches conditioned on their joint latent code as well as their individual positions in the image. To outpaint an image, we seek for multiple latent codes not only recovering available patches but also synthesizing diverse outpainting by patch-based generation. This leads to richer structure and content in the outpainted regions. Furthermore, our formulation allows for outpainting conditioned on the categorical input, thereby enabling flexible user controls. Extensive experimental results demonstrate the proposed method performs favorably against existing in- and outpainting methods, featuring higher visual quality and diversity.

关键词： Representation learning Visualization computer vision Codes Generative adversarial networks Generators Filling

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Captioning Images with Diverse Objects 30

Captioning Images with Diverse Objects

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Venugopalan, Subhashini Mooney, Raymond Hendricks, Lisa Anne Darrell, Trevor Rohrbach, Marcus Saenko, Kate UT Austin Austin TX 78712 USA Univ Calif Berkeley Berkeley CA USA Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781538604571

Recent captioning models are limited in their ability to scale and describe concepts unseen in paired image-text corpora. We propose the Novel Object Captioner (NOC), a deep visual semantic captioning model that can describe a large number of object categories not present in existing image-caption datasets. Our model takes advantage of external sources -labeled images from object recognition datasets, and semantic knowledge extracted from unannotated text. We propose minimizing a joint objective which can learn from these diverse data sources and leverage distributional semantic embeddings, enabling the model to generalize and describe novel objects outside of image-caption datasets. We demonstrate that our model exploits semantic information to generate captions for hundreds of object categories in the Image Net object recognition data set that are not observed in MSCOCO image-caption training data, as well as many categories that are observed very rarely. Both automatic evaluations and human judgements show that our model considerably outperforms prior work in being able to describe many more categories of objects.

关键词： captions Data sources Dataset Semantics object recognition

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