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共 20950 条记录，以下是851-860 订阅

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Efficient On-device Training via Gradient Filtering

Efficient On-device Training via Gradient Filtering

引用

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

作者： Yang, Yuedong Li, Guihong Marculescu, Radu Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350301298

Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI. The problem stems from the large number of operations (e.g., floating point multiplications and additions) and memory consumption required during training by the back-propagation algorithm. Consequently, in this paper, we propose a new gradient filtering approach which enables on-device CNN model training. More precisely, our approach creates a special structure with fewer unique elements in the gradient map, thus significantly reducing the computational complexity and memory consumption of back propagation during training. Extensive experiments on image classification and semantic segmentation with multiple CNN models (e.g., MobileNet, DeepLabV3, UPerNet) and devices (e.g., Raspberry Pi and Jetson Nano) demonstrate the effectiveness and wide applicability of our approach. For example, compared to SOTA, we achieve up to 19x speedup and 77.1% memory savings on ImageNet classification with only 0.1% accuracy loss. Finally, our method is easy to implement and deploy;over 20x speedup and 90% energy savings have been observed compared to highly optimized baselines in MKLDNN and CUDNN on NVIDIA Jetson Nano. Consequently, our approach opens up a new direction of research with a huge potential for on-device training.

关键词： Efficient and scalable vision

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Crossing the Gap: Domain Generalization for Image Captioning

Crossing the Gap: Domain Generalization for Image Captioning

引用

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

作者： Ren, Yuchen Mao, Zhendong Fang, Shancheng Lu, Yan He, Tong Du, Hao Zhang, Yongdong Ouyang, Wanli Univ Sci & Technol China Hefei Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350301298

Existing image captioning methods are under the assumption that the training and testing data are from the same domain or that the data from the target domain (i.e., the domain that testing data lie in) are accessible. However, this assumption is invalid in real-world applications where the data from the target domain is inaccessible. In this paper, we introduce a new setting called Domain Generalization for Image Captioning (DGIC), where the data from the target domain is unseen in the learning process. We first construct a benchmark dataset for DGIC, which helps us to investigate models' domain generalization (DG) ability on unseen domains. With the support of the new benchmark, we further propose a new framework called language-guided semantic metric learning (LSML) for the DGIC setting. Experiments on multiple datasets demonstrate the challenge of the task and the effectiveness of our newly proposed benchmark and LSML framework.

关键词： and reasoning language vision

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High-fidelity 3D Face Generation from Natural Language Descriptions

High-fidelity 3D Face Generation from Natural Language Descr...

引用

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

作者： Wu, Menghua Zhu, Hao Huang, Linjia Zhuang, Yiyu Lu, Yuanxun Cao, Xun Nanjing Univ Nanjing Peoples R China

ISBN: (纸本)9798350301298

Synthesizing high-quality 3D face models from natural language descriptions is very valuable for many applications, including avatar creation, virtual reality, and telepresence. However, little research ever tapped into this task. We argue the major obstacle lies in 1) the lack of high-quality 3D face data with descriptive text annotation, and 2) the complex mapping relationship between descriptive language space and shape/appearance space. To solve these problems, we build DESCRIBE3D dataset, the first large-scale dataset with fine-grained text descriptions for text-to-3D face generation task. Then we propose a two-stage framework to first generate a 3D face that matches the concrete descriptions, then optimize the parameters in the 3D shape and texture space with abstract description to refine the 3D face model. Extensive experimental results show that our method can produce a faithful 3D face that conforms to the input descriptions with higher accuracy and quality than previous methods. The code and DESCRIBE3D dataset are released at https://***/zhuhao-nju/describe3d.

关键词： and reasoning language vision

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ABLE-NeRF: Attention-Based Rendering with Learnable Embeddings for Neural Radiance Field

ABLE-NeRF: Attention-Based Rendering with Learnable Embeddin...

引用

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

作者： Tang, Zhe Jun Cham, Tat-Jen Zhao, Haiyu Nanyang Technol Univ S Lab Singapore Singapore Nanyang Technol Univ Singapore Singapore SenseTime Res Singapore Singapore

ISBN: (纸本)9798350301298

Neural Radiance Field (NeRF) is a popular method in representing 3D scenes by optimising a continuous volumetric scene function. Its large success which lies in applying volumetric rendering (VR) is also its Achilles' heel in producing view-dependent effects. As a consequence, glossy and transparent surfaces often appear murky. A remedy to reduce these artefacts is to constrain this VR equation by excluding volumes with back-facing normal. While this approach has some success in rendering glossy surfaces, translucent objects are still poorly represented. In this paper, we present an alternative to the physics-based VR approach by introducing a self-attention-based framework on volumes along a ray. In addition, inspired by modern game engines which utilise Light Probes to store local lighting passing through the scene, we incorporate Learnable Embeddings to capture view dependent effects within the scene. Our method, which we call ABLE-NeRF, significantly reduces 'blurry' glossy surfaces in rendering and produces realistic translucent surfaces which lack in prior art. In the Blender dataset, ABLE-NeRF achieves SOTA results and surpasses Ref-NeRF in all 3 image quality metrics PSNR, SSIM, LPIPS.

关键词： vision + graphics

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TINC: Tree-structured Implicit Neural Compression

TINC: Tree-structured Implicit Neural Compression

引用

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

作者： Yang, Runzhao Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9798350301298

Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters, and is emerging as a promising data compression technique. However, limited spectrum coverage is intrinsic to INR, and it is non-trivial to remove redundancy in diverse complex data effectively. Preliminary studies can only exploit either global or local correlation in the target data and thus of limited performance. In this paper, we propose a Tree-structured Implicit Neural Compression (TINC) to conduct compact representation for local regions and extract the shared features of these local representations in a hierarchical manner. Specifically, we use Multi-Layer Perceptrons (MLPs) to fit the partitioned local regions, and these MLPs are organized in tree structure to share parameters according to the spatial distance. The parameter sharing scheme not only ensures the continuity between adjacent regions, but also jointly removes the local and non-local redundancy. Extensive experiments show that TINC improves the compression fidelity of INR, and has shown impressive compression capabilities over commercial tools and other deep learning based methods. Besides, the approach is of high flexibility and can be tailored for different data and parameter settings. The source code can be found at https://***/RichealYoung/TINC.

关键词： cell microscopy Medical and biological vision

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Rapid 3D Model Generation with Intuitive 3D Input

Rapid 3D Model Generation with Intuitive 3D Input

引用

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

作者： Chen, Tianrun Ding, Chaotao Zhang, Shangzhan Yu, Chunan Zang, Ying Li, Zejian Peng, Sida Sun, Lingyun Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Huzhou Univ Sch Informat Engn Huzhou Peoples R China Zhejiang Univ Sch Software Technol Hangzhou Peoples R China Moxin Huzhou Technol Co Ltd KOKONI3D Huzhou Peoples R China

ISBN: (纸本)9798350353006

With the emergence of AR/VR, 3D models are in tremendous demand. However, conventional 3D modeling with computer-Aided Design software requires much expertise and is difficult for novice users. We find that AR/VR devices, in addition to serving as effective display mediums, can offer a promising potential as an intuitive 3D model creation tool, especially with the assistance of AI generative models. Here, we propose Deep3DVRSketch, the first 3D model generation network that inputs 3D VR sketches from novice users and generates highly consistent 3D models in multiple categories within seconds, irrespective of the users' drawing abilities. We also contribute KO3D+, the largest 3D sketch-shape dataset. Our method pre-trains a conditional diffusion model on quality 3D data, then fine-tunes an encoder to map 3D sketches onto the generator's manifold using an adaptive curriculum strategy for limited ground truths. In our experiment, our approach achieves state-of- the-art performance in both model quality and fidelity with real-world input from novice users, and users can even draw and obtain very detailed geometric structures. In our user study, users were able to complete the 3D modeling tasks over 10 times faster using our approach compared to conventional CAD software tools. We believe that our Deep3DVRSketch and KO3D+ dataset can offer a promising solution for future 3D modeling in metaverse era. Check the project page at http://***/Deep3DVRSketch.

关键词： 3D models 3D vision generative models

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StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place recognition

StructVPR: Distill Structural Knowledge with Weighting Sampl...

引用

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

作者： Shen, Yanqing Zhou, Sanping Fu, Jingwen Wang, Ruotong Chen, Shitao Zheng, Nanning Xi An Jiao Tong Univ Natl Key Lab Human Machine Hybrid Augmented Intel Natl Engn Res Ctr Visual Informat & Applicat Inst Artificial Intelligence & Robot Xian Peoples R China

ISBN: (纸本)9798350301298

Visual place recognition (VPR) is usually considered as a specific image retrieval problem. Limited by existing training frameworks, most deep learning-based works cannot extract sufficiently stable global features from RGB images and rely on a time-consuming re-ranking step to exploit spatial structural information for better performance. In this paper, we propose StructVPR, a novel training architecture for VPR, to enhance structural knowledge in RGB global features and thus improve feature stability in a constantly changing environment. Specifically, StructVPR uses segmentation images as a more definitive source of structural knowledge input into a CNN network and applies knowledge distillation to avoid online segmentation and inference of seg-branch in testing. Considering that not all samples contain high-quality and helpful knowledge, and some even hurt the performance of distillation, we partition samples and weigh each sample's distillation loss to enhance the expected knowledge precisely. Finally, StructVPR achieves impressive performance on several benchmarks using only global retrieval and even outperforms many two-stage approaches by a large margin. After adding additional re-ranking, ours achieves state-of-the-art performance while maintaining a low computational cost.

关键词： detection recognition: Categorization retrieval

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Zero-shot Referring Image Segmentation with Global-Local Context Features

Zero-shot Referring Image Segmentation with Global-Local Con...

引用

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

作者： Yu, Seonghoon Seo, Paul Hongsuck Son, Jeany GIST AI Grad Sch Gwangju South Korea Google Res Mountain View CA USA

ISBN: (纸本)9798350301298

Referring image segmentation (RIS) aims to find a segmentation mask given a referring expression grounded to a region of the input image. Collecting labelled datasets for this task, however, is notoriously costly and labor-intensive. To overcome this issue, we propose a simple yet effective zero-shot referring image segmentation method by leveraging the pre-trained cross-modal knowledge from CLIP. In order to obtain segmentation masks grounded to the input text, we propose a mask-guided visual encoder that captures global and local contextual information of an input image. By utilizing instance masks obtained from off-the-shelf mask proposal techniques, our method is able to segment fine-detailed instance-level groundings. We also introduce a global-local text encoder where the global feature captures complex sentence-level semantics of the entire input expression while the local feature focuses on the target noun phrase extracted by a dependency parser. In our experiments, the proposed method outperforms several zero-shot baselines of the task and even the weakly supervised referring expression segmentation method with substantial margins. Our code is available at https://***/Seonghoon-Yu/Zero-shot-RIS.

关键词： language reasoning vision

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SFD2: Semantic-guided Feature Detection and Description

SFD2: Semantic-guided Feature Detection and Description

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

作者： Xue, Fei Budvytis, Ignas Cipolla, Roberto Univ Cambridge Cambridge England

ISBN: (纸本)9798350301298

Visual localization is a fundamental task for various applications including autonomous driving and robotics. Prior methods focus on extracting large amounts of often redundant locally reliable features, resulting in limited efficiency and accuracy, especially in large-scale environments under challenging conditions. Instead, we propose to extract globally reliable features by implicitly embedding high-level semantics into both the detection and description processes. Specifically, our semantic-aware detector is able to detect keypoints from reliable regions (e.g. building, traffic lane) and suppress unreliable areas (e.g. sky, car) implicitly instead of relying on explicit semantic labels. This boosts the accuracy of keypoint matching by reducing the number of features sensitive to appearance changes and avoiding the need of additional segmentation networks at test time. Moreover, our descriptors are augmented with semantics and have stronger discriminative ability, providing more inliers at test time. Particularly, experiments on long-term large-scale visual localization Aachen Day-Night and RobotCar-Seasons datasets demonstrate that our model outperforms previous local features and gives competitive accuracy to advanced matchers but is about 2 and 3 times faster when using 2k and 4k keypoints, respectively. Code is available at https://***/feixue94/sfd2.

关键词： Low-level vision

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CutMIB: Boosting Light Field Super-Resolution via Multi-View Image Blending

CutMIB: Boosting Light Field Super-Resolution via Multi-View...

引用

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

作者： Xiao, Zeyu Liu, Yutong Gao, Ruisheng Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Data augmentation (DA) is an efficient strategy for improving the performance of deep neural networks. Recent DA strategies have demonstrated utility in single image super-resolution (SR). Little research has, however, focused on the DA strategy for light field SR, in which multi-view information utilization is required. For the first time in light field SR, we propose a potent DA strategy called CutMIB to improve the performance of existing light field SR networks while keeping their structures unchanged. Specifically, CutMIB first cuts low-resolution (LR) patches from each view at the same location. Then CutMIB blends all LR patches to generate the blended patch and finally pastes the blended patch to the corresponding regions of high-resolution light field views, and vice versa. By doing so, CutMIB enables light field SR networks to learn from implicit geometric information during the training stage. Experimental results demonstrate that CutMIB can improve the reconstruction performance and the angular consistency of existing light field SR networks. We further verify the effectiveness of CutMIB on real-world light field SR and light field denoising. The implementation code is available at https://***/zeyuxiao1997/CutMIB.

关键词： Low-level vision

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