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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4941-4950 订阅

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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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AdaMT-Net: An Adaptive Weight Learning Based Multi-Task Learning Model For Scene Understanding

AdaMT-Net: An Adaptive Weight Learning Based Multi-Task Lear...

引用

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

作者： Jha, Ankit Kumar, Awanish Banerjee, Biplab Chaudhuri, Subhasis Indian Inst Technol Dept Elect Engn Mumbai Maharashtra India Indian Inst Technol Ctr Studies Resources Engn Mumbai Maharashtra India

ISBN: (纸本)9781728193601

We tackle the problem of deep end-to-end multi-task learning (MTL) for visual scene understanding from monocular images in this paper. It is proven that learning several related tasks together helps in attaining improved performance per-task than training them independently. This is due to the fact that related tasks share important feature characteristics among themselves, which the MTL techniques can effectively explore for improved joint training. Based on this premise, we are interested in generic to specific feature extraction given the different tasks within a common framework. To this end, we propose a typical U-Net based encoder-decoder architecture called AdaMT-Net, where the densely-connected deep convolutional neural network (CNN) based feature encoder is shared among the tasks while the soft-attention based task-specific decoder modules produce the desired outcomes. One major issue in MTL is to assign the weights for the task-specific loss-terms in the final cumulative optimization function. As opposed to the manual approach, we propose a novel adaptive weight learning strategy by carefully exploring the loss-gradients per-task over the training iterations. Experimental results on the benchmark CityScapes, NYUv2, and ISPRS datasets confirm that AdaMT-Net achieves state-of-the-art performance on most of the evaluation metrics.

关键词： computer vision

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Self-supervised Correlation Mining Network for Person Image Generation

Self-supervised Correlation Mining Network for Person Image ...

引用

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

作者： Wang, Zijian Qi, Xingqun Yuan, Kun Sun, Muyi Beijing Univ Posts & Telecommun Sch AI Auto Beijing Peoples R China Kuaishou Technol Beijing Peoples R China Chinese Acad Sci Inst Automat CRIPAC Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Person image generation aims to perform non-rigid deformation on source images, which generally requires unaligned data pairs for training. Recently, self-supervised methods express great prospects in this task by merging the disentangled representations for self-reconstruction. However, such methods fail to exploit the spatial correlation between the disentangled features. In this paper, we propose a Self-supervised Correlation Mining Network (SCMNet) to rearrange the source images in the feature space, in which two collaborative modules are integrated, Decomposed Style Encoder (DSE) and Correlation Mining Module (CMM). Specifically, the DSE first creates unaligned pairs at the feature level. Then, the CMM establishes the spatial correlation field for feature rearrangement. Eventually, a translation module transforms the rearranged features to realistic results. Meanwhile, for improving the fidelity of cross-scale pose transformation, we propose a graph based Body Structure Retaining Loss (BSR Loss) to preserve reasonable body structures on half body to full body generation. Extensive experiments conducted on DeepFashion dataset demonstrate the superiority of our method compared with other supervised and unsupervised approaches. Furthermore, satisfactory results on face generation show the versatility of our method in other deformation tasks.

关键词： Training computer vision Correlation Image synthesis Face recognition Merging Transforms

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CENet: Consolidation-and-Exploration Network for Continuous Domain Adaptation

CENet: Consolidation-and-Exploration Network for Continuous ...

引用

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

作者： Zhang, Chi Cheng, Yalu Wei, Pengxu He, Hongliang Chen, Jie Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unsupervised Domain Adaptation (UDA) deals with transferring knowledge from labeled source domains to an unlabeled target domain under domain shift. However, this does not reflect the breadth of scenarios that arise in real-world applications since source domains could increase. A plausible conjecture is: can we train a life-long learning model learned on continuous source domains given the target without the presence of labels? We formalize this task as the Continuous Domain Adaptation (CDA) and empirically show that conventional domain adaptation methods may suffer severe generalization deterioration due to the limited incremental transferability and negative transfer. To tackle this issue, we propose a novel sample-to-sample framework-Consolidationand-Exploration Network (CENet) to facilitate incremental transferring. This method underscores both the qualitative and quantitative relationship between samples. Moreover, we conduct comprehensive experiments to evaluate the effectiveness of each component in our pair-based method. Extensive experiments show that our approach achieves significant improvement over related state-of-the-art methods.

关键词： Representation learning Adaptation models computer vision Computational modeling conferences Reliability engineering Data models

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Gradient Reweighting: Towards Imbalanced Class-Incremental Learning

Gradient Reweighting: Towards Imbalanced Class-Incremental L...

引用

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

作者： He, Jiangpeng Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9798350353006

Class-Incremental Learning (CIL) trains a model to continually recognize new classes from non-stationary data while retaining learned knowledge. A major challenge of CIL arises when applying to real-world data characterized by non-uniform distribution, which introduces a dual imbalance problem involving (i) disparities between stored exemplars of old tasks and new class data (inter-phase imbalance), and (ii) severe class imbalances within each individual task (intra-phase imbalance). We show that this dual imbalance issue causes skewed gradient updates with biased weights in FC layers, thus inducing over/under-fitting and catastrophic forgetting in CIL. Our method addresses it by reweighting the gradients towards balanced optimization and unbiased classifier learning. Additionally, we observe imbalanced forgetting where paradoxically the instance-rich classes suffer higher performance degradation during CIL due to a larger amount of training data becoming unavailable in subsequent learning phases. To tackle this, we further introduce a distribution-aware knowledge distillation loss to mitigate forgetting by aligning output logits proportionally with the distribution of lost training data. We validate our method on CIFAR-100, ImageNetSubset, and Food101 across various evaluation protocols and demonstrate consistent improvements compared to existing works, showing great potential to apply CIL in real-world scenarios with enhanced robustness and effectiveness.

关键词： computer vision continual learniing imbalanced gradient long-tailed learning

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From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation

From Synthetic to Real: Unsupervised Domain Adaptation for A...

引用

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

作者： Li, Chen Lee, Gim Hee Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (纸本)9781665445092

Animal pose estimation is an important field that has received increasing attention in the recent years. The main challenge for this task is the lack of labeled data. Existing works circumvent this problem with pseudo labels generated from data of other easily accessible domains such as synthetic data. However, these pseudo labels are noisy even with consistency check or confidence-based filtering due to the domain shift in the data. To solve this problem, we design a multi-scale domain adaptation module (MDAM) to reduce the domain gap between the synthetic and real data. We further introduce an online coarse-to-fine pseudo label updating strategy. Specifically, we propose a self-distillation module in an inner coarse-update loop and a mean-teacher in an outer fine-update loop to generate new pseudo labels that gradually replace the old ones. Consequently, our model is able to learn from the old pseudo labels at the early stage, and gradually switch to the new pseudo labels to prevent overfitting in the later stage. We evaluate our approach on the TigDog and VisDA 2019 datasets, where we outperform existing approaches by a large margin. We also demonstrate the generalization ability of our model by testing extensively on both unseen domains and unseen animal categories. Our code is available at the project website(1).

关键词： Training computer vision Codes Animals Filtering Pose estimation Switches

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Wide Compression: Tensor Ring Nets 31

Wide Compression: Tensor Ring Nets

引用

31st IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Wenqi Sun, Yifan Eriksson, Brian Wang, Wenlin Aggarwal, Vaneet Purdue Univ W Lafayette IN 47907 USA Technicolor Res Los Altos CA USA Adobe Los Altos CA USA Duke Univ Durham NC 27706 USA

ISBN: (纸本)9781538664209

Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications. In order to obtain performance gains, these networks have grown larger and deeper, containing millions or even billions of parameters and over a thousand layers. The tradeoff is that these large architectures require an enormous amount of memory, storage, and computation, thus limiting their usability. Inspired by the recent tensor ring factorization, we introduce Tensor Ring Networks (TR-Nets), which significantly compress both the fully connected layers and the convolutional layers of deep neural networks. Our results show that our TR-Nets approach is able to compress LeNet-5 by 11x without losing accuracy, and can compress the state-of-the-art Wide ResNet by 243x with only 2.3% degradation in Cifar10 image classification. Overall, this compression scheme shows promise in scientific computing and deep learning, especially for emerging resource-constrained devices such as smartphones, wearables, and IoT devices.

关键词： Neural networks Image coding Shape Merging computer architecture

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Learning to Track Instances without Video Annotations

Learning to Track Instances without Video Annotations

引用

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

作者： Fu, Yang Liu, Sifei Iqbal, Umar De Mello, Shalini Shi, Humphrey Kautz, Jan Univ Illinois Urbana IL 61801 USA NVIDIA Santa Clara CA USA Univ Oregon Eugene OR 97403 USA

ISBN: (纸本)9781665445092

Tracking segmentation masks of multiple instances has been intensively studied, but still faces two fundamental challenges: 1) the requirement of large-scale, frame-wise annotation, and 2) the complexity of two-stage approaches. To resolve these challenges, we introduce a novel semi-supervised framework by learning instance tracking networks with only a labeled image dataset and unlabeled video sequences. With an instance contrastive objective, we learn an embedding to discriminate each instance from the others. We show that even when only trained with images, the learned feature representation is robust to instance appearance variations, and is thus able to track objects steadily across frames. We further enhance the tracking capability of the embedding by learning correspondence from unlabeled videos in a self-supervised manner. In addition, we integrate this module into single-stage instance segmentation and pose estimation frameworks, which significantly reduce the computational complexity of tracking compared to two-stage networks. We conduct experiments on the YouTube-VIS and PoseTrack datasets. Without any video annotation efforts, our proposed method can achieve comparable or even better performance than most fully-supervised methods(1).

关键词： Image segmentation computer vision Image resolution Video sequences Pose estimation Image annotation Entropy

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A Characteristic Function Approach to Deep Implicit Generative Modeling

A Characteristic Function Approach to Deep Implicit Generati...

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

作者： Ansari, Abdul Fatir Scarlett, Jonathan Soh, Harold Natl Univ Singapore Dept Comp Sci Singapore Singapore Natl Univ Singapore Dept Math Singapore Singapore

ISBN: (纸本)9781728171685

Implicit Generative Models (IGMs) such as GANs have emerged as effective data-driven models for generating samples, particularly images. In this paper, we formulate the problem of learning an IGM as minimizing the expected distance between characteristic functions. Specifically, we minimize the distance between characteristic functions of the real and generated data distributions under a suitably-chosen weighting distribution. This distance metric, which we term as the characteristic function distance (CFD), can be (approximately) computed with linear time-complexity in the number of samples, in contrast with the quadratic-time Maximum Mean Discrepancy (MMD). By replacing the discrepancy measure in the critic of a GAN with the CFD, we obtain a model that is simple to implement and stable to train. The proposed metric enjoys desirable theoretical properties including continuity and differentiability with respect to generator parameters, and continuity in the weak topology. We further propose a variation of the CFD in which the weighting distribution parameters are also optimized during training;this obviates the need for manual tuning, and leads to an improvement in test power relative to CFD. We demonstrate experimentally that our proposed method outperforms WGAN and MMD-GAN variants on a variety of unsupervised image generation benchmarks.

关键词： computer vision

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Neural Texture Extraction and Distribution for Controllable Person Image Synthesis

Neural Texture Extraction and Distribution for Controllable ...

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

作者： Ren, Yurui Fan, Xiaoqing Li, Ge Liu, Shan Li, Thomas H. Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Tencent Amer Palo Alto CA USA Peking Univ Adv Inst Informat Technol Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We deal with the controllable person image synthesis task which aims to re-render a human from a reference image with explicit control over body pose and appearance. Observing that person images are highly structured, we propose to generate desired images by extracting and distributing semantic entities of reference images. To achieve this goal, a neural texture extraction and distribution operation based on double attention is described. This operation first extracts semantic neural textures from reference feature maps. Then, it distributes the extracted neural textures according to the spatial distributions learned from target poses. Our model is trained to predict human images in arbitrary poses, which encourages it to extract disentangled and expressive neural textures representing the appearance of different semantic entities. The disentangled representation further enables explicit appearance control. Neural textures of different reference images can be fused to control the appearance of the interested areas. Experimental comparisons show the superiority of the proposed model. Code is available at https://***/ RenYurui/ Neural- Texture- Extraction- Distribution.

关键词： computer vision Graphical models Image synthesis Computational modeling Semantics Predictive models Feature extraction

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