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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015"

共 19687 条记录，以下是841-850 订阅

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GeoVLN: Learning Geometry-Enhanced Visual Representation with Slot Attention for vision-and-Language Navigation

GeoVLN: Learning Geometry-Enhanced Visual Representation wit...

引用

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

作者： Huo, Jingyang Sun, Qiang Jiang, Boyan Lin, Haitao Fu, Yanwei Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9798350301298

Most existing works solving Room-to-Room VLN problem only utilize RGB images and do not consider local context around candidate views, which lack sufficient visual cues about surrounding environment. Moreover, natural language contains complex semantic information thus its correlations with visual inputs are hard to model merely with cross attention. In this paper, we propose GeoVLN, which learns Geometry-enhanced visual representation based on slot attention for robust Visual-and-Language Navigation. The RGB images are compensated with the corresponding depth maps and normal maps predicted by Omnidata as visual inputs. Technically, we introduce a two-stage module that combine local slot attention and CLIP model to produce geometry-enhanced representation from such input. We employ V&L BERT to learn a cross-modal representation that incorporate both language and vision informations. Additionally, a novel multiway attention module is designed, encouraging different phrases of input instruction to exploit the most related features from visual input. Extensive experiments demonstrate the effectiveness of our newly designed modules and show the compelling performance of the proposed method.

关键词： Multi-modal learning

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D²Former: Jointly Learning Hierarchical Detectors and Contextual Descriptors via Agent-based Transformers

D<SUP>2</SUP>Former: Jointly Learning Hierarchical Detectors...

引用

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

作者： He, Jianfeng Gao, Yuan Zhang, Tianzhu Zhang, Zhe Wu, Feng Univ Sci & Technol China Hefei Peoples R China Deep Space Explorat Lab Hefei Peoples R China

ISBN: (纸本)9798350301298

Establishing pixel-level matches between image pairs is vital for a variety of computer vision applications. However, achieving robust image matching remains challenging because CNN extracted descriptors usually lack discriminative ability in texture-less regions and keypoint detectors are only good at identifying keypoints with a specific level of structure. To deal with these issues, a novel image matching method is proposed by Jointly Learning Hierarchical Detectors and Contextual Descriptors via Agentbased Transformers (D2Former), including a contextual feature descriptor learning (CFDL) module and a hierarchical keypoint detector learning (HKDL) module. The proposed D2Former enjoys several merits. First, the proposed CFDL module can model long-range contexts efficiently and effectively with the aid of designed descriptor agents. Second, the HKDL module can generate keypoint detectors in a hierarchical way, which is helpful for detecting keypoints with diverse levels of structures. Extensive experimental results on four challenging benchmarks show that our proposed method significantly outperforms state-of-the-art image matching methods.

关键词： detection recognition: Categorization retrieval

来源：评论

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Learning Customized Visual Models with Retrieval-Augmented Knowledge

Learning Customized Visual Models with Retrieval-Augmented K...

引用

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

作者： Liu, Haotian Son, Kilho Yang, Jianwei Liu, Ce Gao, Jianfeng Lee, Yong Jae Li, Chunyuan Univ Wisconsin Madison Madison WI 53706 USA Microsoft Redmond WA USA

ISBN: (纸本)9798350301298

Image-text contrastive learning models such as CLIP have demonstrated strong task transfer ability. The high generality and usability of these visual models is achieved via a web-scale data collection process to ensure broad concept coverage, followed by expensive pre-training to feed all the knowledge into model weights. Alternatively, we propose REACT, REtrieval-Augmented CusTomization, a framework to acquire the relevant web knowledge to build customized visual models for target domains. We retrieve the most relevant image-text pairs (similar to 3% of CLIP pre-training data) from the web-scale database as external knowledge and propose to customize the model by only training new modularized blocks while freezing all the original weights. The effectiveness of REACT is demonstrated via extensive experiments on classification, retrieval, detection and segmentation tasks, including zero, few, and full-shot settings. Particularly, on the zero-shot classification task, compared with CLIP, it achieves up to 5.4% improvement on ImageNet and 3.7% on the ELEVATER benchmark (20 datasets).

关键词： and reasoning language vision

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VecFontSDF: Learning to Reconstruct and Synthesize High-quality Vector Fonts via Signed Distance Functions

VecFontSDF: Learning to Reconstruct and Synthesize High-qual...

引用

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

作者： Xia, Zeqing Xiong, Bojun Lian, Zhouhui Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Font design is of vital importance in the digital content design and modern printing industry. Developing algorithms capable of automatically synthesizing vector fonts can significantly facilitate the font design process. However, existing methods mainly concentrate on raster image generation, and only a few approaches can directly synthesize vector fonts. This paper proposes an end-to-end trainable method, VecFontSDF, to reconstruct and synthesize high-quality vector fonts using signed distance functions (SDFs). Specifically, based on the proposed SDF-based implicit shape representation, VecFontSDF learns to model each glyph as shape primitives enclosed by several parabolic curves, which can be precisely converted to quadratic Bezier curves that are widely used in vector font products. In this manner, most image generation methods can be easily extended to synthesize vector fonts. Qualitative and quantitative experiments conducted on a publicly-available dataset demonstrate that our method obtains high-quality results on several tasks, including vector font reconstruction, interpolation, and few-shot vector font synthesis, markedly outperforming the state of the art.

关键词： vision + graphics

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Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval

Pic2Word: Mapping Pictures to Words for Zero-shot Composed I...

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

作者： Saito, Kuniaki Sohn, Kihyuk Zhang, Xiang Li, Chun-Liang Lee, Chen-Yu Saenko, Kate Pfister, Tomas Boston Univ Boston MA 02215 USA Google Cloud AI Res Mountain View CA 94043 USA Google Res Mountain View CA USA MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350301298

In Composed Image Retrieval (CIR), a user combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image. Labeling such triplets is expensive and hinders broad applicability of CIR. In this work, we propose to study an important task, Zero-Shot Composed Image Retrieval (ZS-CIR), whose goal is to build a CIR model without requiring labeled triplets for training. To this end, we propose a novel method, called Pic2Word, that requires only weakly labeled image-caption pairs and unlabeled image datasets to train. Unlike existing supervised CIR models, our model trained on weakly labeled or unlabeled datasets shows strong generalization across diverse ZS-CIR tasks, e.g., attribute editing, object composition, and domain conversion. Our approach outperforms several supervised CIR methods on the common CIR benchmark, CIRR and Fashion-IQ. Code will be made publicly available at https://***/google-research/composed_image_retrieval

关键词： language reasoning vision

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Learning Transferable Compound Expressions from Masked AutoEncoder Pretraining

Learning Transferable Compound Expressions from Masked AutoE...

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

作者： Qiu, Feng Du, Wining Zhang, Wei Liu, Chen Li, Lincheng Guo, Tianchen Yu, Xin Netease Fuxi AI Lab Guangzhou Peoples R China Univ Queensland Brisbane Qld Australia

ISBN: (纸本)9798350365474

Video-based Compound Expression recognition (CER) aims to identify compound expressions in everyday interactions per frame. Unlike rapid progress in Facial Expression recognition (FER) for the basic emotions (e.g., surprised, sad, and fearful), CER with the compound emotions (e.g., fearfully surprised, and sadly fearful) remains underexplored, with an evident gap in the availability of substantial datasets. In this paper, we design a framework to demonstrate the feasibility of predicting compound expressions in-the-wild without relying on domain-specific supervision. To be specific, we first train a model on a large-scale facial dataset using the Masked Autoencoder (MAE) approach to learn comprehensive facial features. Then, to tailor it for facial expression analysis, we fine-tune the ViT encoder on an Action Unit (AU) detection task. To address the issue of insufficient data, we transform the task of recognizing compound emotions into a multi-label recognition task for basic emotions. We train a network by fine-tuning the pretrained ViT encoder to predict the probability of each basic emotion, and then combine these probabilities to arrive at the final prediction for the compound emotions. Experiments conducted on the C-EXPR-DB dataset demonstrate the effectiveness of our framework in the frame-by-frame prediction of compound expressions in-the-wild. Our framework is recognized as the leading solution in the Compound Expression (CE) recognition Challenge in the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). More information for the Competition can be found in: 6th ABAW.

关键词： affective behavior analysis compound expression recognition

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TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in vision-Centric Autonomous Driving

TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Jo...

引用

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

作者： Fang, Shaoheng Wang, Zi Zhong, Yiqi Ge, Junhao Chen, Siheng Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Univ Southern Calif Dept Comp Sci Los Angeles CA USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

vision-centric joint perception and prediction (PnP) has become an emerging trend in autonomous driving research. It predicts the future states of the traffic participants in the surrounding environment from raw RGB images. However, it is still a critical challenge to synchronize features obtained at multiple camera views and timestamps due to inevitable geometric distortions and further exploit those spatial-temporal features. To address this issue, we propose a temporal bird's-eye-view pyramid transformer (TBP-Former) for vision-centric PnP, which includes two novel designs. First, a pose-synchronized BEV encoder is proposed to map raw image inputs with any camera pose at any time to a shared and synchronized BEV space for better spatial-temporal synchronization. Second, a spatial-temporal pyramid transformer is introduced to comprehensively extract multi-scale BEV features and predict future BEV states with the support of spatial priors. Extensive experiments on nuScenes dataset show that our proposed framework overall outperforms all state-of-the-art vision-based prediction methods. Code is available at: https://***/MediaBrain-SJTU/TBP-Former

关键词： Autonomous driving

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Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging

Residual Degradation Learning Unfolding Framework with Mixin...

引用

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

作者： Dong, Yubo Gao, Dahua Qiu, Tian Li, Yuyan Yang, Minxi Shi, Guangming Xidian Univ Sch Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9798350301298

To acquire a snapshot spectral image, coded aperture snapshot spectral imaging (CASSI) is proposed. A core problem of the CASSI system is to recover the reliable and fine underlying 3D spectral cube from the 2D measurement. By alternately solving a data subproblem and a prior subproblem, deep unfolding methods achieve good performance. However, in the data subproblem, the used sensing matrix is ill-suited for the real degradation process due to the device errors caused by phase aberration, distortion;in the prior subproblem, it is important to design a suitable model to jointly exploit both spatial and spectral priors. In this paper, we propose a Residual Degradation Learning Unfolding Framework (RDLUF), which bridges the gap between the sensing matrix and the degradation process. Moreover, a MixS2 Transformer is designed via mixing priors across spectral and spatial to strengthen the spectral-spatial representation capability. Finally, plugging the MixS2 Transformer into the RDLUF leads to an end-to-end trainable neural network RDLUF-MixS2. Experimental results establish the superior performance of the proposed method over existing ones. Code is available: https: //***/ShawnDong98/RDLUF_MixS2

关键词： Low-level vision

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Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding

Detection Hub: Unifying Object Detection Datasets via Query ...

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

作者： Meng, Lingchen Dai, Xiyang Chen, Yinpeng Zhang, Pengchuan Chen, Dongdong Liu, Mengchen Wang, Jianfeng Wu, Zuxuan Yuan, Lu Jiang, Yu-Gang Fudan Univ Shanghai Key Lab Intell Info Proc Sch CS Shanghai Peoples R China Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Microsoft Redmond WA USA

ISBN: (纸本)9798350301298

Combining multiple datasets enables performance boost on many computer vision tasks. But similar trend has not been witnessed in object detection when combining multiple datasets due to two inconsistencies among detection datasets: taxonomy difference and domain gap. In this paper, we address these challenges by a new design (named Detection Hub) that is dataset-aware and category-aligned. It not only mitigates the dataset inconsistency but also provides coherent guidance for the detector to learn across multiple datasets. In particular, the dataset-aware design is achieved by learning a dataset embedding that is used to adapt object queries as well as convolutional kernels in detection heads. The categories across datasets are semantically aligned into a unified space by replacing one-hot category representations with word embedding and leveraging the semantic coherence of language embedding. Detection Hub fulfills the benefits of large data on object detection. Experiments demonstrate that joint training on multiple datasets achieves significant performance gains over training on each dataset alone. Detection Hub further achieves SoTA performance on UODB benchmark with wide variety of datasets.

关键词： detection recognition: Categorization retrieval

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BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation

BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly ...

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

作者： Cheng, Tianheng Wang, Xinggang Chen, Shaoyu Zhang, Qian Liu, Wenyu Huazhong Univ Sci & Technol Sch EIC Wuhan Peoples R China Horizon Robot Beijing Peoples R China

ISBN: (纸本)9798350301298

Labeling objects with pixel-wise segmentation requires a huge amount of human labor compared to bounding boxes. Most existing methods for weakly supervised instance segmentation focus on designing heuristic losses with priors from bounding boxes. While, we find that box-supervised methods can produce some fine segmentation masks and we wonder whether the detectors could learn from these fine masks while ignoring low-quality masks. To answer this question, we present BoxTeacher, an efficient and end-to-end training framework for high-performance weakly supervised instance segmentation, which leverages a sophisticated teacher to generate high-quality masks as pseudo labels. Considering the massive noisy masks hurt the training, we present a mask-aware confidence score to estimate the quality of pseudo masks, and propose the noise-aware pixel loss and noise-reduced affinity loss to adaptively optimize the student with pseudo masks. Extensive experiments can demonstrate effectiveness of the proposed BoxTeacher. Without bells and whistles, BoxTeacher remarkably achieves 35.0 mask AP and 36.5 mask AP with ResNet-50 and ResNet-101 respectively on the challenging COCO dataset, which outperforms the previous state-of-the-art methods by a significant margin and bridges the gap between box-supervised and mask-supervised methods.

关键词： detection recognition: Categorization retrieval

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