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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11891 条记录，以下是881-890 订阅

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Learning to Render Novel Views from Wide-Baseline Stereo Pairs

Learning to Render Novel Views from Wide-Baseline Stereo Pai...

引用

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

作者： Du, Yilun Smith, Cameron Tewari, Ayush Sitzmann, Vincent MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9798350301298

We introduce a method for novel view synthesis given only a single wide-baseline stereo image pair. In this challenging regime, 3D scene points are regularly observed only once, requiring prior-based reconstruction of scene geometry and appearance. We find that existing approaches to novel view synthesis from sparse observations fail due to recovering incorrect 3D geometry and due to the high cost of differentiable rendering that precludes their scaling to large-scale training. We take a step towards resolving these shortcomings by formulating a multi-view transformer encoder, proposing an efficient, image-space epipolar line sampling scheme to assemble image features for a target ray, and a lightweight cross-attention-based renderer. Our contributions enable training of our method on a large-scale real-world dataset of indoor and outdoor scenes. We demonstrate that our method learns powerful multi-view geometry priors while reducing the rendering time. We conduct extensive comparisons on held-out test scenes across two real-world datasets, significantly outperforming prior work on novel view synthesis from sparse image observations and achieving multi-view-consistent novel view synthesis.

关键词： vision + graphics

来源：评论

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Multivariate, Multi-frequency and Multimodal: Rethinking Graph Neural Networks for Emotion recognition in Conversation

Multivariate, Multi-frequency and Multimodal: Rethinking Gra...

引用

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

作者： Chen, Feiyu Shao, Jie Zhu, Shuyuan Shen, Heng Tao Univ Elect Sci & Technol China Chengdu Peoples R China Sichuan Artificial Intelligence Res Inst Yibin Peoples R China

ISBN: (纸本)9798350301298

Complex relationships of high arity across modality and context dimensions is a critical challenge in the Emotion recognition in Conversation (ERC) task. Yet, previous works tend to encode multimodal and contextual relationships in a loosely-coupled manner, which may harm relationship modelling. Recently, Graph Neural Networks (GNN) which show advantages in capturing data relations, offer a new solution for ERC. However, existing GNN-based ERC models fail to address some general limits of GNNs, including assuming pairwise formulation and erasing high-frequency signals, which may be trivial for many applications but crucial for the ERC task. In this paper, we propose a GNN-based model that explores multivariate relationships and captures the varying importance of emotion discrepancy and commonality by valuing multi-frequency signals. We empower GNNs to better capture the inherent relationships among utterances and deliver more sufficient multimodal and contextual modelling. Experimental results show that our proposed method outperforms previous state-of-the-art works on two popular multimodal ERC datasets.

关键词： Multi-modal learning

来源：评论

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Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors

Towards Efficient Use of Multi-Scale Features in Transformer...

引用

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

作者： Zhang, Gongjie Luo, Zhipeng Tian, Zichen Zhang, Jingyi Zhang, Xiaoqin Lu, Shijian Nanyang Technol Univ S Lab Singapore Singapore Black Sesame Technol Singapore Singapore SenseTime Res Hong Kong Peoples R China Wenzhou Univ Wenzhou Peoples R China

ISBN: (纸本)9798350301298

Multi-scale features have been proven highly effective for object detection but often come with huge and even prohibitive extra computation costs, especially for the recent Transformer-based detectors. In this paper, we propose Iterative Multi-scale Feature Aggregation (IMFA) - a generic paradigm that enables efficient use of multi-scale features in Transformer-based object detectors. The core idea is to exploit sparse multi-scale features from just a few crucial locations, and it is achieved with two novel designs. First, IMFA rearranges the Transformer encoder-decoder pipeline so that the encoded features can be iteratively updated based on the detection predictions. Second, IMFA sparsely samples scale-adaptive features for refined detection from just a few keypoint locations under the guidance of prior detection predictions. As a result, the sampled multi-scale features are sparse yet still highly beneficial for object detection. Extensive experiments show that the proposed IMFA boosts the performance of multiple Transformer-based object detectors significantly yet with only slight computational overhead.

关键词： detection recognition: Categorization retrieval

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LANA: A Language-Capable Navigator for Instruction Following and Generation

LANA: A Language-Capable Navigator for Instruction Following...

引用

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

作者： Wang, Xiaohan Wang, Wenguan Shao, Jiayi Yang, Yi Zhejiang Univ CCAI Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Recently, visual-language navigation (VLN) - entailing robot agents to follow navigation instructions - has shown great advance. However, existing literature put most emphasis on interpreting instructions into actions, only delivering "dumb" wayfinding agents. In this article, we devise LANA, a language-capable navigation agent which is able to not only execute human-written navigation commands, but also provide route descriptions to humans. This is achieved by simultaneously learning instruction following and generation with only one single model. More specifically, two encoders, respectively for route and language encoding, are built and shared by two decoders, respectively for action prediction and instruction generation, so as to exploit cross-task knowledge and capture task-specific characteristics. Throughout pretraining and fine-tuning, both instruction following and generation are set as optimization objectives. We empirically verify that, compared with recent advanced task-specific solutions, LANA attains better performances on both instruction following and route description, with nearly half complexity. In addition, endowed with language generation capability, LANA can explain to human its behaviours and assist human's wayfinding. This work is expected to foster future efforts towards building more trustworthy and socially intelligent navigation robots.

关键词： Embodied vision: Active agents simulation

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Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction

Causally-Aware Intraoperative Imputation for Overall Surviva...

引用

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

作者： Li, Xiang Qian, Xuelin Liang, Litian Kong, Lingjie Dong, Qiaole Chen, Jiejun Liu, Dingxia Yao, Xiuzhong Fu, Yanwei Fudan Univ Sch Data Sci Shanghai Peoples R China Fudan Univ Zhongshan Hosp Dept Radiol Shanghai Peoples R China

ISBN: (纸本)9798350301298

Previous efforts in vision community are mostly made on learning good representations from visual patterns. Beyond this, this paper emphasizes the high-level ability of causal reasoning. We thus present a case study of solving the challenging task of Overall Survival (OS) time in primary liver cancers. Critically, the prediction of OS time at the early stage remains challenging, due to the unobvious image patterns of reflecting the OS. To this end, we propose a causal inference system by leveraging the intraoperative attributes and the correlation among them, as an intermediate supervision to bridge the gap between the images and the final OS. Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction. We present a novel Causally-aware Intraoperative Imputation Model (CAWIM) that can sequentially predict each attribute using its parent nodes in the estimated causal graph. To determine the causal directions, we propose a splitting-voting mechanism, which votes for the direction for each pair of adjacent nodes among multiple predictions obtained via causal discovery from heterogeneity. The practicability and effectiveness of our method are demonstrated by the promising results on liver cancer dataset of 361 patients with long-term observations.

关键词： cell microscopy Medical and biological vision

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

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

引用

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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Internal Diverse Image Completion

Internal Diverse Image Completion

引用

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

作者： Alkobi, Noa Shaham, Tamar Rott Michaeli, Tomer Technion Haifa Israel MIT Technion Haifa Israel

ISBN: (纸本)9798350302493

Image completion is widely used in photo restoration and editing applications, e.g. for object removal. Recently, there has been a surge of research on generating diverse completions for missing regions. However, existing methods require large training sets from a specific domain of interest, and often fail on general-content images. In this paper, we propose a diverse completion method that does not require a training set and can thus treat arbitrary images from any domain. Our internal diverse completion (IDC) approach draws inspiration from recent single-image generative models that are trained on multiple scales of a single image, adapting them to the extreme setting in which only a small portion of the image is available for training. We illustrate the strength of IDC on several datasets, using both user studies and quantitative comparisons.

关键词： computer vision

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Slimmable Dataset Condensation

Slimmable Dataset Condensation

引用

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

作者： Liu, Songhua Ye, Jingwen Yu, Runpeng Wang, Xinchao Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798350301298

Dataset distillation, also known as dataset condensation, aims to compress a large dataset into a compact synthetic one. Existing methods perform dataset condensation by assuming a fixed storage or transmission budget. When the budget changes, however, they have to repeat the synthesizing process with access to original datasets, which is highly cumbersome if not infeasible at all. In this paper, we explore the problem of slimmable dataset condensation, to extract a smaller synthetic dataset given only previous condensation results. We first study the limitations of existing dataset condensation algorithms on such a successive compression setting and identify two key factors: (1) the inconsistency of neural networks over different compression times and (2) the underdetermined solution space for synthetic data. Accordingly, we propose a novel training objective for slimmable dataset condensation to explicitly account for both factors. Moreover, synthetic datasets in our method adopt a significance-aware parameterization. Theoretical derivation indicates that an upper-bounded error can be achieved by discarding the minor components without training. Alternatively, if training is allowed, this strategy can serve as a strong initialization that enables a fast convergence. Extensive comparisons and ablations demonstrate the superiority of the proposed solution over existing methods on multiple benchmarks.

关键词： Efficient and scalable vision

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Language-Guided Audio-Visual Source Separation via Trimodal Consistency

Language-Guided Audio-Visual Source Separation via Trimodal ...

引用

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

作者： Tan, Reuben Ray, Arijit Burns, Andrea Plummer, Bryan A. Salamon, Justin Nieto, Oriol Russell, Bryan Saenko, Kate Boston Univ Boston MA 02215 USA Adobe Res San Francisco CA USA IBM Res MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350301298

We propose a self-supervised approach for learning to perform audio source separation in videos based on natural language queries, using only unlabeled video and audio pairs as training data. A key challenge in this task is learning to associate the linguistic description of a sound-emitting object to its visual features and the corresponding components of the audio waveform, all without access to annotations during training. To overcome this challenge, we adapt off-the-shelf vision-language foundation models to provide pseudo-target supervision via two novel loss functions and encourage a stronger alignment between the audio, visual and natural language modalities. During inference, our approach can separate sounds given text, video and audio input, or given text and audio input alone. We demonstrate the effectiveness of our self-supervised approach on three audio-visual separation datasets, including MUSIC, SOLOS and AudioSet, where we outperform state-of-the-art strongly supervised approaches despite not using object detectors or text labels during training. Our project page including publicly available code can be found at https://***/rxtan/projects/VAST.

关键词： and reasoning language vision

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Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning

Temporal Attention Unit: Towards Efficient Spatiotemporal Pr...

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

作者： Tan, Cheng Gao, Zhangyang Wu, Lirong Xu, Yongjie Xia, Jun Li, Siyuan Li, Stan Z. Westlake Univ AI Lab Res Ctr Ind Future Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Spatiotemporal predictive learning aims to generate future frames by learning from historical frames. In this paper, we investigate existing methods and present a general framework of spatiotemporal predictive learning, in which the spatial encoder and decoder capture intra-frame features and the middle temporal module catches inter-frame correlations. While the mainstream methods employ recurrent units to capture long-term temporal dependencies, they suffer from low computational efficiency due to their unparallelizable architectures. To parallelize the temporal module, we propose the Temporal Attention Unit (TAU), which decomposes temporal attention into intra-frame statical attention and inter-frame dynamical attention. Moreover, while the mean squared error loss focuses on intra-frame errors, we introduce a novel differential divergence regularization to take inter-frame variations into account. Extensive experiments demonstrate that the proposed method enables the derived model to achieve competitive performance on various spatiotemporal prediction benchmarks.

关键词： Low-level vision

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