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Dynamic-OFA: Runtime DNN Architecture Switching for Performance Scaling on Heterogeneous Embedded Platforms

Dynamic-OFA: Runtime DNN Architecture Switching for Performa...

引用

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

作者： Lou, Wei Xun, Lei Sabet, Amin Bi, Jia Hare, Jonathon Merrett, Geoff, V Univ Southampton Southampton Hants England

ISBN: (纸本)9781665448994

Mobile and embedded platforms are increasingly required to efficiently execute computationally demanding DNNs across heterogeneous processing elements. At runtime, the available hardware resources to DNNs can vary considerably due to other concurrently running applications. The performance requirements of the applications could also change under different scenarios. To achieve the desired performance, dynamic DNNs have been proposed in which the number of channels/layers can be scaled in real time to meet different requirements under varying resource constraints. However, the training process of such dynamic DNNs can be costly, since platform-aware models of different deployment scenarios must be retrained to become dynamic. This paper proposes Dynamic-OFA, a novel dynamic DNN approach for state-of-the-art platform-aware NAS models (i.e. Once-for-all network (OFA)). Dynamic-OFA pre-samples a family of sub-networks from a static OFA backbone model, and contains a runtime manager to choose different sub-networks under different runtime environments. As such, Dynamic-OFA does not need the traditional dynamic DNN training pipeline. Compared to the state-of-the-art, our experimental results using ImageNet on a Jetson Xavier NX show that the approach is up to 3.5x (CPU), 2.4x (GPU) faster for similar Top-1 accuracy, or 3.8% (CPU), 5.1% (GPU) higher accuracy at similar latency.

关键词： Training Three-dimensional displays Computational modeling Pipelines Graphics processing units computer architecture Switches

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Learning CLIP Guided Visual-Text Fusion Transformer for Video-based Pedestrian Attribute recognition

Learning CLIP Guided Visual-Text Fusion Transformer for Vide...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jun Zhu Jiandong Jin Zihan Yang Xiaohao Wu Xiao Wang School of Computer Science and Technology Anhui University Hefei China School of Artificial Intelligence Anhui University Hefei China

Existing pedestrian attribute recognition (PAR) algorithms are mainly developed based on a static image. However, the performance is not reliable for images with challenging factors, such as heavy occlusion, motion blur, etc. In this work, we propose to understand human attributes using video frames that can make full use of temporal information. Specifically, we formulate the video-based PAR as a vision-language fusion problem and adopt pre-trained big models CLIP to extract the feature embeddings of given video frames. To better utilize the semantic information, we take the attribute list as another input and transform the attribute words/phrase into the corresponding sentence via split, expand, and prompt. Then, the text encoder of CLIP is utilized for language embedding. The averaged visual tokens and text tokens are concatenated and fed into a fusion Transformer for multi-modal interactive learning. The enhanced tokens will be fed into a classification head for pedestrian attribute prediction. Extensive experiments on a large-scale video-based PAR dataset fully validated the effectiveness of our proposed framework. Both the source code and pre-trained models will be released at https://***/Event–AHU/VTF_PAR.

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Video Representation Learning for Conversational Facial Expression recognition Guided by Multiple View Reconstruction

Video Representation Learning for Conversational Facial Expr...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Valeriya Strizhkova Laura M. Ferrari Hadi Kachmar Antitza Dantcheva François Brémond INRIA Université Côte D’Azur

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Conversational facial expression recognition entails challenges such as handling of facial dynamics, small available datasets, low-intensity and fine-grained emotional expressions and extreme face angle. Towards addressing these challenges, we propose the Masking Action Units and Reconstructing multiple Angles (MAURA) pre-training. MAURA is an efficient self-supervised method that permits the use of small datasets, while preserving end-to-end conversational facial expression recognition with vision Transformer. MAURA masks videos using the location with active Action Units and reconstructs synchronized multi-view videos, thus learning the dependencies between muscle movements and encoding information, which might only be visible in few frames and/or in certain views. Based on one view (e.g., frontal), the encoder reconstructs other views (e.g., top, down, laterals). Such masking and reconstructing strategy provides a powerful representation, beneficial in facial expression downstream tasks. Our experimental analysis shows that we consistently outperform the state-of-the-art in the challenging settings of low-intensity and fine-grained conversational facial expression recognition on four datasets including in-the-wild DFEW, CMU-MOSEI, MFA and multi-view MEAD. Our results suggest that MAURA is able to learn robust and generic video representations.

关键词： Representation learning computer vision Emotion recognition Face recognition conferences Muscles Transformers

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Task2Sim: Towards Effective Pre-training and Transfer from Synthetic Data

Task2Sim: Towards Effective Pre-training and Transfer from S...

引用

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

作者： Mishra, Samarth Panda, Rameswar Phoo, Cheng Perng Chen, Chun-Fu (Richard) Karlinsky, Leonid Saenko, Kate Saligrama, Venkatesh Feris, Rogerio S. Boston Univ Boston MA 02215 USA MIT IBM Watson AI Lab Cambridge MA USA Cornell Univ Ithaca NY 14853 USA FLARE JPMorgan Chase New York NY USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Pre-training models on Imagenet or other massive datasets of real images has led to major advances in computer vision, albeit accompanied with shortcomings related to curation cost, privacy, usage rights, and ethical issues. In this paper, for the first time, we study the transferability of pre-trained models based on synthetic data generated by graphics simulators to downstream tasks from very different domains. In using such synthetic data for pre-training, we find that downstream performance on different tasks are favored by different configurations of simulation parameters (e.g. lighting, object pose, backgrounds, etc.), and that there is no one-size-fits-all solution. It is thus better to tailor synthetic pre-training data to a specific downstream task, for best performance. We introduce Task2Sim, a unified model mapping downstream task representations to optimal simulation parameters to generate synthetic pre-training data for them. Task2Sim learns this mapping by training to find the set of best parameters on a set of "seen" tasks. Once trained, it can then be used to predict best simulation parameters for novel "unseen" tasks in one shot, without requiring additional training. Given a budget in number of images per class, our extensive experiments with 20 diverse downstream tasks show Task2Sim's task-adaptive pretraining data results in significantly better downstream performance than non-adaptively choosing simulation parameters on both seen and unseen tasks. It is even competitive with pre-training on real images from Imagenet.

关键词： Graphics Training Representation learning Adaptation models computer vision Computational modeling Data models

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Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zihan Wang Siyang Song Cheng Luo Yuzhi Zhou Shiling Wu Weicheng Xie Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Computing and Mathematical Sciences University of Leicester

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5.

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PromptSync: Bridging Domain Gaps in vision-Language Models through Class-Aware Prototype Alignment and Discrimination

PromptSync: Bridging Domain Gaps in Vision-Language Models t...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Anant Khandelwal Glance AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The potential for zero-shot generalization in vision-language (V-L) models such as CLIP has spurred their widespread adoption in addressing numerous downstream tasks. Previous methods have employed test-time prompt tuning to adapt the model to unseen domains, but they overlooked the issue of imbalanced class distributions. In this study, we explicitly address this problem by employing class-aware prototype alignment weighted by mean class probabilities obtained for the test sample and filtered augmented views. Additionally, we ensure that the class probabilities are as accurate as possible by performing prototype discrimination using contrastive learning. The combination of alignment and discriminative loss serves as a geometric regularizer, preventing the prompt representation from collapsing onto a single class and effectively bridging the distribution gap between the source and test domains. Our method, named PromptSync, synchronizes the prompts for each test sample on both the text and vision branches of the V-L model. In empirical evaluations on the domain generalization benchmark, our method outperforms previous best methods by 2.33% in overall performance, by 1% in base-to-novel generalization, and by 2.84% in cross-dataset transfer tasks.

关键词： Adaptation models Computational modeling conferences Prototypes Contrastive learning Benchmark testing Robustness

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ShiftAddAug: Augment Multiplication-Free Tiny Neural Network with Hybrid Computation

ShiftAddAug: Augment Multiplication-Free Tiny Neural Network...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yipin Guo Zihao Li Yilin Lang Qinyuan Ren College of Control Science and Engineering Zhejiang University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Operators devoid of multiplication, such as Shift and Add, have gained prominence for their compatibility with hardware. However, neural networks (NNs) employing these operators typically exhibit lower accuracy compared to conventional NNs with identical structures. ShiftAddAug uses costly multiplication to augment efficient but less powerful multiplication-free operators, improving performance without any inference overhead. It puts a ShiftAdd tiny NN into a large multiplicative model and encourages it to be trained as a sub-model to obtain additional supervision. In order to solve the weight discrepancy problem between hybrid operators, a new weight sharing method is proposed. Additionally, a novel two stage neural architecture search is used to obtain better augmentation effects for smaller but stronger multiplication-free tiny neural networks. The superiority of ShiftAddAug is validated through experiments in image classification and semantic segmentation, consistently delivering noteworthy enhancements. Remarkably, it secures up to a 4.95% increase in accuracy on the CIFAR100 compared to its directly trained counterparts, even surpassing the performance of multiplicative NNs.

关键词： Training computer vision Accuracy Computational modeling Semantic segmentation conferences Hardware

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Recon3D: High Quality 3D Reconstruction from a Single Image Using Generated Back-View Explicit Priors

Recon3D: High Quality 3D Reconstruction from a Single Image ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ruiyang Chen Mohan Yin Jiawei Shen Wei Ma Beijing University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Significant progress has been achieved in deep 3D reconstruction from a single frontal view with the aid of generative models; however, the unreliable nature of generated multi-views continues to present challenges in this domain. In this study, we propose Recon3D, a novel framework for 3D reconstruction. Recon3D exclusively utilizes a generated back view, which can be obtained more reliably through generative models based on the frontal reference image, as explicit priors. By incorporating these priors and guidance from a generative model, which is fine-tuned with Dreambooth and then enhanced with ControlNet, we effectively supervise NeRF rendering in the latent space. Subsequently, we convert the NeRF representation into an explicit point cloud and further optimize the explicit representation by referencing high-quality textured reference views. Extensive experiments demonstrate that our method achieves state-of-the-art performance in rendering novel views with superior geometry and texture quality.

关键词： Point cloud compression Geometry Solid modeling computer vision Three-dimensional displays conferences Rendering (computer graphics)

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Exploring the Zero-Shot Capabilities of vision-Language Models for Improving Gaze Following

Exploring the Zero-Shot Capabilities of Vision-Language Mode...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Anshul Gupta Pierre Vuillecard Arya Farkhondeh Jean-Marc Odobez Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne Switzerland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Contextual cues related to a person’s pose and interactions with objects and other people in the scene can provide valuable information for gaze following. While existing methods have focused on dedicated cue extraction methods, in this work we investigate the zero-shot capabilities of vision-Language Models (VLMs) for extracting a wide array of contextual cues to improve gaze following performance. We first evaluate various VLMs, prompting strategies, and in-context learning (ICL) techniques for zero-shot cue recognition performance. We then use these insights to extract contextual cues for gaze following, and investigate their impact when incorporated into a state of the art model for the task. Our analysis indicates that BLIP-2 is the overall top performing VLM and that ICL can improve performance. We also observe that VLMs are sensitive to the choice of the text prompt although ensembling over multiple text prompts can provide more robust performance. Additionally, we discover that using the entire image along with an ellipse drawn around the target person is the most effective strategy for visual prompting. For gaze following, incorporating the extracted cues results in better generalization performance, especially when considering a larger set of cues, highlighting the potential of this approach.

关键词： Visualization computer vision conferences Computational modeling Predictive models pattern recognition Context modeling

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Lightweight Maize Disease Detection through Post-Training Quantization with Similarity Preservation

Lightweight Maize Disease Detection through Post-Training Qu...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Carlos Victorino Padeiro Tse-Wei Chen Takahiro Komamizu Ichiro Ide Nagoya University Nagoya Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Traditional crop disease diagnosis, reliant on expert visual observation, is expensive, time-consuming, and prone to error. While Convolutional Neural Networks (CNNs) offer promising alternatives, their high resource demands limit their accessibility to farmers, particularly those in resource-constrained settings. Lightweight models that operate on resource-limited devices without network access are crucial to address this gap. This paper proposes a Similarity-Preserving Quantization (SPQ) method to convert high-precision CNNs into lower-precision models while maintaining similar feature representations. While quantization offers a promising approach for building lightweight CNNs for crop disease detection, the quality of quantized models often suffers. SPQ addresses this challenge by ensuring equivalent activation patterns for similar crop images in both the original and quantized models. Experimental evaluation using MobileNetV2 and ResNet-50 demonstrates that SPQ improves throughput, inference, and memory footprint more than 3 times while preserving the detection performance.

关键词： Visualization Quantization (signal) Crops Euclidean distance Throughput pattern recognition Medical diagnosis

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