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Linear Combination Approximation of Feature for Channel Pruning

Linear Combination Approximation of Feature for Channel Prun...

引用

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

作者： Joo, Donggyu Kim, Doyeon Yi, Eojindl Kim, Junmo Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Network pruning is an effective method that reduces the computation of neural networks while maintaining high performance. This enables the operation of deep neural networks in resource-limited environments. In a general large network, the roles of each channel often inevitably overlap with those of others. Therefore, for more effective pruning, it is important to observe the correlation between features in the network. In this paper, we propose a novel channel pruning method, namely, the linear combination approximation of features (LCAF). We approximate each feature map by a linear combination of other feature maps in the same layer, and then remove the most approximated one. Additionally, by exploiting the linearity of the convolution operation, we propose a supporting method called weight modification, to further reduce the loss change that occurs during pruning. Extensive experiments show that LCAF achieves state-of-the-art performance in several benchmarks. Furthermore, ablations on the LCAF demonstrate the effectiveness of our approach in a variety of ways.

关键词： Deep learning computer vision Correlation Convolution conferences Neural networks Linearity

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Momentum Contrastive Pruning

Momentum Contrastive Pruning

引用

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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Video Action Detection: Analysing Limitations and Challenges

Video Action Detection: Analysing Limitations and Challenges

引用

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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Dress Code: High-Resolution Multi-Category Virtual Try-On

Dress Code: High-Resolution Multi-Category Virtual Try-On

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

作者： Morelli, Davide Fincato, Matteo Cornia, Marcella Landi, Federico Cesari, Fabio Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy YOOX NET A PORTER GRP Milan Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Existing literature focuses mainly on upper-body clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body items. This shortcoming arises from a main factor: current publicly available datasets for image-based virtual try-on do not account for this variety, thus limiting progress in the field. In this research activity, we introduce Dress Code, a novel dataset which contains images of multi-category clothes. Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024 x 768) with front-view, full-body reference models. To generate HD try-on images with high visual quality and rich in details, we propose to learn fine-grained discriminating features. Specifically, we leverage a semantic-aware discriminator that makes predictions at pixel-level instead of image- or patch-level. The Dress Code dataset is publicly available at https://***/aimagelab/dress-code.

关键词： Visualization computer vision Codes Limiting conferences Clothing pattern recognition

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Unsupervised Anomaly Detection from Time-of-Flight Depth Images

Unsupervised Anomaly Detection from Time-of-Flight Depth Ima...

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

作者： Schneider, Pascal Rambach, Jason Mirbach, Bruno Stricker, Didier German Res Ctr Artificial Intelligence DFKI Trippstadter Str 122 D-67663 Kaiserslautern Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Video anomaly detection (VAD) addresses the problem of automatically finding anomalous events in video data. The primary data modalities on which current VAD systems work on are monochrome or RGB images. Using depth data in this context instead is still hardly explored in spite of depth images being a popular choice in many other computer vision research areas and the increasing availability of inexpensive depth camera hardware. We evaluate the application of existing autoencoder-based methods on depth video and propose how the advantages of using depth data can be leveraged by integration into the loss function. Training is done unsupervised using normal sequences without need for any additional annotations. We show that depth allows easy extraction of auxiliary information for scene analysis in the form of a foreground mask and demonstrate its beneficial effect on the anomaly detection performance through evaluation on a large public dataset, for which we are also the first ones to present results on.

关键词： Training Optical losses computer vision Cameras Transformers Sensors Task analysis

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Is Multimodal vision Supervision Beneficial to Language?

Is Multimodal Vision Supervision Beneficial to Language?

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Madasu, Avinash Lal, Vasudev Unc Chapel Hill Department of Computer Science United States Cognitive Computing Research Intel Labs United States

ISBN: (纸本)9798350302493

vision (image & video) - Language (VL) pre-training is the recent popular paradigm that achieved state-of-the-art results on multi-modal tasks like image-retrieval, video-retrieval, visual question answering etc. These models are trained in an unsupervised way and greatly benefit from the complementary modality supervision. In this paper, we explore if the language representations trained using vision supervision perform better than vanilla language representations on Natural Language Understanding and commonsense reasoning benchmarks. We experiment with a diverse set of image-text models such as ALBEF, BLIP, METER and video-text models like ALPRO, Frozen in Time, VIOLET. We compare the performance of language representations of stand-alone text encoders of these models to the language representations of text encoders learnt through vision supervision. Our experiments suggest that vanilla language representations show superior performance on most of the tasks. These results shed light on the current drawbacks of the vision-language models. The code is available at https://***/avinashsai/MML © 2023 ieee.

关键词： Signal encoding

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PseudoProp: Robust Pseudo-Label Generation for Semi-Supervised Object Detection in Autonomous Driving Systems

PseudoProp: Robust Pseudo-Label Generation for Semi-Supervis...

引用

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

作者： Hu, Shu Liu, Chun-Hao Dutta, Jayanta Chang, Ming-Ching Lyu, Siwei Ramakrishnan, Naveen Univ Buffalo SUNY Buffalo NY USA Bosch Ctr Artificial Intelligence Sunnyvale CA 94085 USA SUNY Albany Albany NY 12222 USA Amazon Seattle WA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Semi-supervised object detection methods are widely used in autonomous driving systems, where only a fraction of objects are labeled. To propagate information from the labeled objects to the unlabeled ones, pseudo-labels for unlabeled objects must be generated. Although pseudo-labels have proven to improve the performance of semisupervised object detection significantly, the applications of image-based methods to video frames result in numerous miss or false detections using such generated pseudo-labels. In this paper, we propose a new approach, PseudoProp, to generate robust pseudo-labels by leveraging motion continuity in video frames. Specifically, PseudoProp uses a novel bidirectional pseudo-label propagation approach to compensate for misdetection. A feature-based fusion technique is also used to suppress inference noise. Extensive experiments on the large-scale Cityscapes dataset demonstrate that our method outperforms the state-of-the-art semi-supervised object detection methods by 7.4% on mAP(75).

关键词： Training computer vision conferences Object detection Predictive models Feature extraction pattern recognition

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

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

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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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Towards a Deeper Understanding of Skeleton-based Gait recognition

Towards a Deeper Understanding of Skeleton-based Gait Recogn...

引用

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

作者： Teepe, Torben Gilg, Johannes Herzog, Fabian Hoermann, Stefan Rigoll, Gerhard Tech Univ Munich Munich Germany

ISBN: (纸本)9781665487399

Gait recognition is a promising biometric with unique properties for identifying individuals from a long distance by their walking patterns. In recent years, most gait recognition methods used the person's silhouette to extract the gait features. However, silhouette images can lose fine-grained spatial information, suffer from (self) occlusion, and be challenging to obtain in real-world scenarios. Furthermore, these silhouettes also contain other visual clues that are not actual gait features and can be used for identification, but also to fool the system. Model-based methods do not suffer from these problems and are able to represent the temporal motion of body joints, which are actual gait features. The advances in human pose estimation started a new era for model-based gait recognition with skeleton-based gait recognition. In this work, we propose an approach based on Graph Convolutional Networks (GCNs) that combines higher-order inputs, and residual networks to an efficient architecture for gait recognition. Extensive experiments on the two popular gait datasets, CASIA-B and OUMVLP-Pose, show a massive improvement (3x) of the state-of-the-art (SotA) on the largest gait dataset OUMVLP-Pose and strong temporal modeling capabilities. Finally, we visualize our method to understand skeleton-based gait recognition better and to show that we model real gait features.

关键词： Training Visualization Pose estimation computer architecture Performance gain Feature extraction pattern recognition

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Efficient Conditional Pre-training for Transfer Learning

Efficient Conditional Pre-training for Transfer Learning

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

作者： Chakraborty, Shuvam Uzkent, Burak Ayush, Kumar Tanmay, Kumar Sheehan, Evan Ermon, Stefano Stanford Univ Stanford CA 94305 USA IIT Kharagpur Kharagpur W Bengal India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Almost all the state-of-the-art neural networks for computer vision tasks are trained by (1) pre-training on a large-scale dataset and (2) finetuning on the target dataset. This strategy helps reduce dependence on the target dataset and improves convergence rate and generalization on the target task. Although pre-training on large-scale datasets is very useful for new methods or models, its foremost disadvantage is high training cost. To address this, we propose efficient filtering methods to select relevant subsets from the pre-training dataset. Additionally, we discover that lowering image resolutions in the pre-training step offers a great trade-off between cost and performance. We validate our techniques by pre-training on ImageNet in both the unsupervised and supervised settings and finetuning on a diverse collection of target datasets and tasks. Our proposed methods drastically reduce pre-training cost and provide strong performance boosts. Finally, we improve the current standard of ImageNet pre-training by 1-3% by tuning available models on our subsets and pre-training on a dataset filtered from a larger scale dataset.

关键词： Training computer vision Costs Image resolution Filtering Computational modeling Transfer learning

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