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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是351-360 订阅

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Ex-Model: Continual Learning from a Stream of Trained Models

Ex-Model: Continual Learning from a Stream of Trained Models

引用

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

作者： Carta, Antonio Cossu, Andrea Lomonaco, Vincenzo Bacciu, Davide Univ Pisa Pisa Italy Scuola Normale Super Pisa Pisa Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt, and generalize continually in an efficient, effective, and scalable way is fundamental for a sustainable development of Artificial Intelligent systems. However, an agent-centric view of continual learning requires learning directly from raw data, which limits the interaction between independent agents, the efficiency, and the privacy of current approaches. Instead, we argue that continual learning systems should exploit the availability of compressed information in the form of trained models. In this paper, we introduce and formalize a new paradigm named "Ex-Model Continual Learning" (ExML), where an agent learns from a sequence of previously trained models instead of raw data. We further contribute with three ex-model continual learning algorithms and an empirical setting comprising three datasets (MNIST, CIFAR-10 and CORe50), and eight scenarios, where the proposed algorithms are extensively tested. Finally, we highlight the peculiarities of the ex-model paradigm and we point out interesting future research directions.

关键词： Learning systems Data privacy computer vision conferences Computational modeling Data models pattern recognition

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Bootstrapped Representation Learning for Skeleton-Based Action recognition

Bootstrapped Representation Learning for Skeleton-Based Acti...

引用

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

作者： Moliner, Olivier Huang, Sangxia Astrom, Kalle Lund Univ Ctr Math Sci Lund Sweden Sony Europe BV R&D Ctr Lund Lab Lund Sweden

ISBN: (纸本)9781665487399

In this work, we study self-supervised representation learning for 3D skeleton-based action recognition. We extend Bootstrap Your Own Latent (BYOL) for representation learning on skeleton sequence data and propose a new data augmentation strategy including two asymmetric transformation pipelines. We also introduce a multi-viewpoint sampling method that leverages multiple viewing angles of the same action captured by different cameras. In the semi-supervised setting, we show that the performance can be further improved by knowledge distillation from wider networks, leveraging once more the unlabeled samples. We conduct extensive experiments on the NTU-60, NTU-120 and PKU-MMD datasets to demonstrate the performance of our proposed method. Our method consistently outperforms the current state of the art on linear evaluation, semi-supervised and transfer learning benchmarks.

关键词： Representation learning Three-dimensional displays Transfer learning Pipelines Self-supervised learning Cameras Sampling methods

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Learned Low Bitrate Video Compression with Space-Time Super-Resolution

Learned Low Bitrate Video Compression with Space-Time Super-...

引用

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

作者： Yang, Jiayu Yang, Chunhui Xiong, Fei Wang, Feng Wang, Ronggang Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents a learned low bitrate video compression framework that consists of pre-processing, compression and post-processing. In pre-processing stage, the source videos are optionally reduced to low-resolution or low-frame-rate ones to better meet with the limited bandwidth. In compression stage, inter-frame prediction is performed by deformable convolution (DCN). The predicted frame is then used as temporal conditions to compress the current frame. In post-processing stage, the decoded videos are fed into a Space-Time Super-Resolution module, in which the videos are restored to original spatial and temporal resolutions. Experimental results on CLIC22 video test conditions demonstrate that the proposed method shows better performance on both objective and subjective quality at low bitrate. Our team name is PKUSZ-LVC.

关键词： computer vision Convolution conferences Computational modeling Superresolution Bit rate Video compression

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SqueezeNeRF: Further factorized FastNeRF for memory-efficient inference

SqueezeNeRF: Further factorized FastNeRF for memory-efficien...

引用

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

作者： Wadhwani, Krishna Kojima, Tamaki Sony Grp Corp Nihonbashi Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural Radiance Fields (NeRF) has emerged as the state-of-the-art method for novel view generation of complex scenes, but is very slow during inference. Recently, there have been multiple works on speeding up NeRF inference, but the state of the art methods for real-time NeRF inference rely on caching the neural network output, which occupies several giga-bytes of disk space that limits their real-world applicability. As caching the neural network of original NeRF network is not feasible, Garbin et al. proposed "FastNeRF" which factorizes the problem into 2 subnetworks - one which depends only on the 3D coordinate of a sample point and one which depends only on the 2D camera viewing direction. Although this factorization enables them to reduce the cache size and perform inference at over 200 frames per second, the memory overhead is still substantial. In this work, we propose SqueezeNeRF, which is more than 60 times memory-efficient than the sparse cache of FastNeRF and is still able to render at more than 190 frames per second on a high spec GPU during inference.

关键词： computer vision Three-dimensional displays conferences Neural networks Graphics processing units Cameras Real-time systems

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Unsupervised Salient Object Detection with Spectral Cluster Voting

Unsupervised Salient Object Detection with Spectral Cluster ...

引用

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

作者： Shin, Gyungin Albanie, Samuel Xie, Weidi Univ Oxford Visual Geometry Grp Oxford England Univ Cambridge Dept Engn Cambridge England Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we tackle the challenging task of unsupervised salient object detection (SOD) by leveraging spectral clustering on self-supervised features. We make the following contributions: (i) We revisit spectral clustering and demonstrate its potential to group the pixels of salient objects across various self-supervised features, e.g., MoCov2, SwAV, and DINO;(ii) Given mask proposals from multiple applications of spectral clustering on image features computed from different self-supervised models, we propose a simple but effective winner-takes-all voting mechanism for selecting the salient masks, leveraging object priors based on framing and distinctiveness;(iii) Using the selected object segmentation as pseudo groundtruth masks, we train a salient object detector, termed SELF-MASK, which outperforms prior approaches on three unsupervised SOD benchmarks.

关键词： conferences Object detection Object segmentation Detectors Benchmark testing Feature extraction pattern recognition

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Integrating Pose and Mask Predictions for Multi-person in Videos

Integrating Pose and Mask Predictions for Multi-person in Vi...

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

作者： Heo, Miran Hwang, Sukjun Oh, Seoung Wug Lee, Joon-Young Kim, Seon Joo Yonsei Univ Seoul South Korea Adobe Res San Jose CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In real-world applications for video editing, humans are arguably the most important objects. When editing videos of humans, the efficient tracking of fine-grained masks and body joints is the fundamental requirement. In this paper, we propose a simple and efficient system for jointly tracking pose and segmenting high-quality masks for all humans in the video. We design a pipeline that globally tracks pose and locally segments fine-grained masks. Specifically, CenterTrack is first employed to track human poses by viewing the whole scene, and then the proposed local segmentation network leverages the pose information as a powerful query to carry out high-quality segmentation. Furthermore, we adopt a highly light-weight MLP-Mixer layer within the segmentation network that can efficiently propagate the query pose throughout the region of interest with minimal overhead. For the evaluation, we collect a new benchmark called KineMask which includes various appearances and actions. The experimental results demonstrate that our method has superior fine-grained segmentation performance. Moreover, it runs at 33 fps, achieving a great balance of speed and accuracy compared to the prevailing online Video Instance Segmentation methods.

关键词： computer vision conferences Computational modeling Pipelines Benchmark testing pattern recognition

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Momentum Contrastive Pruning

Momentum Contrastive Pruning

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

作者： Pan, Siyuan Qin, Yiming Li, Tingyao Li, Xiaoshuang Hou, Liang Shanghai Jiao Tong Univ Shanghai Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Momentum contrast [16] (MoCo) for unsupervised visual representation learning has a close performance to supervised learning, but it sometimes possesses excess parameters. Extracting a subnetwork from an over-parameterized unsupervised network without sacrificing performance is of particular interest to accelerate inference speed. Typical pruning methods are not applicable for MoCo, because in the fine-tune stage after pruning, the slow update of the momentum encoder will undermine the pretrained encoder. In this paper, we propose a Momentum Contrastive Pruning (MCP) method, which prunes the momentum encoder instead to obtain a momentum subnet. It maintains an unpruned momentum encoder as a smooth transition scheme to alleviate the representation gap between the encoder and momentum subnet. To fulfill the sparsity requirements of the encoder, alternating direction method of multipliers [40] (ADMM) is adopted. Experiments prove that our MCP method can obtain a momentum subnet that has almost equal performance as the over-parameterized MoCo when transferred to downstream tasks, meanwhile has much less parameters and float operations per second (FLOPs).

关键词： Representation learning Visualization computer vision conferences Computational modeling Supervised learning Self-supervised learning

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SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery

SeeTheSeams: Localized Detection of Seam Carving based Image...

引用

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

作者： Gudavalli, Chandrakanth Rosten, Erik Nataraj, Lakshmanan Chandrasekaran, Shivkumar Manjunath, B. S. Mayachitra Inc Santa Barbara CA 93111 USA UC Santa Barbara Elect & Comp Engn Dept Santa Barbara CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Seam carving is a popular technique for content aware image retargeting. It can be used to deliberately manipulate images, for example, change the GPS locations of a building or displace/remove roads in a satellite image. This paper proposes a novel approach for detecting and localizing seams in such images. While there are methods to detect seam carving based manipulations, this is the first time that robust localization and detection of seam carving forgery is made possible. We also propose a seam localization score (SLS) metric to evaluate the effectiveness of localization. The proposed method is evaluated extensively on a large collection of images from different sources, demonstrating a high level of detection and localization performance across these datasets. The code and datasets curated during this work will be released to the public.

关键词： Location awareness Measurement computer vision Satellites Image coding Roads conferences

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Temporal Alignment Networks for Long-term Video

Temporal Alignment Networks for Long-term Video

引用

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

作者： Han, Tengda Xie, Weidi Zisserman, Andrew Univ Oxford Visual Geometry Grp Oxford England Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The objective of this paper is a temporal alignment network that ingests long term video sequences, and associated text sentences, in order to: (1) determine if a sentence is alignable with the video;and (2) if it is alignable, then determine its alignment. The challenge is to train such networks from large-scale datasets, such as HowTo100M, where the associated text sentences have significant noise, and are only weakly aligned when relevant. Apart from proposing the alignment network, we also make four contributions: (i) we describe a novel co-training method that enables to denoise and train on raw instructional videos without using manual annotation, despite the considerable noise;(ii) to benchmark the alignment performance, we manually curate a 10-hour subset of HowTo100M, totalling 80 videos, with sparse temporal descriptions. Our proposed model, trained on HowTo100M, outperforms strong baselines (CLIP, MIL-NCE) on this alignment dataset by a significant margin;(iii) we apply the trained model in the zero-shot settings to multiple downstream video understanding tasks and achieve state-of-the-art results, including text-video retrieval on YouCook2, and weakly supervised video action segmentation on Breakfast-Action. (iv) we use the automatically-aligned HowTo100M annotations for end-to-end finetuning of the backbone model, and obtain improved performance on downstream action recognition tasks.

关键词： computer vision Annotations Computational modeling Video sequences Noise reduction Manuals Benchmark testing

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Video Action Detection: Analysing Limitations and Challenges

Video Action Detection: Analysing Limitations and Challenges

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

作者： Modi, Rajat Rana, Aayush Jung Kumar, Akash Tirupattur, Praveen Vyas, Shruti Rawat, Yogesh Singh Shah, Mubarak Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9781665487399

Beyond possessing large enough size to feed data hungry machines (eg, transformers), what attributes measure the quality of a dataset? Assuming that the definitions of such attributes do exist, how do we quantify among their relative existences? Our work attempts to explore these questions for video action detection. The task aims to spatio-temporally localize an actor and assign a relevant action class. We first analyze the existing datasets on video action detection and discuss their limitations. Next, we propose a new dataset, Multi Actor Multi Action (MAMA) which overcomes these limitations and is more suitable for real world applications. In addition, we perform a biasness study which analyzes a key property differentiating videos from static images: the temporal aspect. This reveals if the actions in these datasets really need the motion information of an actor, or whether they predict the occurrence of an action even by looking at a single frame. Finally, we investigate the widely held assumptions on the importance of temporal ordering: is temporal ordering important for detecting these actions? Such extreme experiments show existence of biases which have managed to creep into existing methods inspite of careful modeling.

关键词： pattern recognition conferences computer vision

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