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Knowledge Distillation via the Target-aware Transformer

Knowledge Distillation via the Target-aware Transformer

引用

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

作者： Lin, Sihao Xie, Hongwei Wang, Bing Yu, Kaicheng Chang, Xiaojun Liang, Xiaodan Wang, Gang RMIT Univ Melbourne Vic Australia Alibaba Grp Hangzhou Peoples R China UTS AAII ReLER Sydney NSW Australia Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Knowledge distillation becomes a de facto standard to improve the performance of small neural networks. Most of the previous works propose to regress the representational features from the teacher to the student in a one-to-one spatial matching fashion. However, people tend to overlook the fact that, due to the architecture differences, the semantic information on the same spatial location usually vary. This greatly undermines the underlying assumption of the one-to-one distillation approach. To this end, we propose a novel one-to-all spatial matching knowledge distillation approach. Specifically, we allow each pixel of the teacher feature to be distilled to all spatial locations of the student features given its similarity, which is generated from a target-aware transformer. Our approach surpasses the state-of-the-art methods by a significant margin on various computer vision benchmarks, such as ImageNet, Pascal VOC and COCOStuff10k.

关键词： Knowledge engineering Deep learning computer vision Codes Semantics Neural networks computer architecture

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Discovering Multi-Hardware Mobile Models via Architecture Search

Discovering Multi-Hardware Mobile Models via Architecture Se...

引用

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

作者： Chu, Grace Arikan, Okan Bender, Gabriel Wang, Weijun Brighton, Achille Kindermans, Pieter-Jan Liu, Hanxiao Akin, Berkin Gupta, Suyog Howard, Andrew Google LLC Mountain View CA 94043 USA

ISBN: (纸本)9781665448994

Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another important factor, model deployment complexity, has been largely ignored. In this paper, we argue that, for applications that may be deployed on multiple hardware, having different single-hardware models across the deployed hardware makes it hard to guarantee consistent outputs across hardware and duplicates engineering work for debugging and fixing. To minimize such deployment cost, we propose an alternative solution, multi-hardware models, where a single architecture is developed for multiple hardware. With thoughtful search space design and incorporating the proposed multi-hardware metrics in neural architecture search, we discover multi-hardware models that give state-of-the-art (SoTA) performance across multiple hardware in both average and worse case scenarios. For performance on individual hardware, the single multi-hardware model yields similar or better results than SoTA performance on accelerators like GPU, DSP and EdgeTPU which was achieved by different models, while having similar performance with MobilenetV3 Large Minimalistic model on mobile CPU.(1)

关键词： computer vision conferences Graphics processing units Focusing computer architecture Debugging Extraterrestrial measurements

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Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation

Efficient Pre-trained Features and Recurrent Pseudo-Labeling...

引用

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

作者： Zhang, Youshan Davison, Brian D. Lehigh Univ Comp Sci & Engn Bethlehem PA 18015 USA

ISBN: (纸本)9781665448994

Domain adaptation (DA) mitigates the domain shift problem when transferring knowledge from one annotated domain to another similar but different unlabeled domain. However, existing models often utilize one of the ImageNet models as the backbone without exploring others, and fine-tuning or retraining the backbone ImageNet model is also time-consuming. Moreover, pseudo-labeling has been used to improve the performance in the target domain, while how to generate confident pseudo labels and explicitly align domain distributions has not been well addressed. In this paper, we show how to efficiently opt for the best pre-trained features from seventeen well-known ImageNet models in unsupervised DA problems. In addition, we propose a recurrent pseudo-labeling model using the best pre-trained features (termed PRPL) to improve classification performance. To show the effectiveness of PRPL, we evaluate it on three benchmark datasets, Office+Caltech-10, Office31, and Office-Home. Extensive experiments show that our model reduces computation time and boosts the mean accuracy to 98.1%, 92.4%, and 81.2%, respectively, substantially outperforming the state of the art.

关键词： Training Adaptation models computer vision Computational modeling conferences Optimized production technology Benchmark testing

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MSTR: Multi-Scale Transformer for End-to-End Human-Object Interaction Detection

MSTR: Multi-Scale Transformer for End-to-End Human-Object In...

引用

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

作者： Kim, Bumsoo Mun, Jonghwan On, Kyoung-Woon Shin, Minchul Lee, Junhyun Kim, Eun-Sol LG AI Res Geumsan South Korea Kakao Brain Seongnam South Korea Korea Univ Seoul South Korea Hanyang Univ Dept Comp Sci Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Human-Object Interaction (HOI) detection is the task of identifying a set of (human, object, interaction) triplets from an image. Recent work proposed transformer encoder-decoder architectures that successfully eliminated the need for many hand-designed components in HOI detection through end-to-end training. However, they are limited to single-scale feature resolution, providing suboptimal performance in scenes containing humans, objects, and their interactions with vastly different scales and distances. To tackle this problem, we propose a Multi-Scale TRansformer (MSTR) for HOI detection powered by two novel HOIaware deformable attention modules called Dual-Entity attention and Entity-conditioned Context attention. While existing deformable attention comes at a huge cost in HOI detection performance, our proposed attention modules of MSTR learn to effectively attend to sampling points that are essential to identify interactions. In experiments, we achieve the new state-of-the-art performance on two HOI detection benchmarks.

关键词： Training Image resolution Semantics Detectors computer architecture Benchmark testing Transformers

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Masked-attention Mask Transformer for Universal Image Segmentation

Masked-attention Mask Transformer for Universal Image Segmen...

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

作者： Cheng, Bowen Misra, Ishan Schwing, Alexander G. Kirillov, Alexander Girdhar, Rohit Facebook AI Res FAIR Menlo Pk CA 94025 USA Univ Illinois Urbana Champaign UIUC Champaign IL 61820 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image segmentation groups pixels with different semantics, e.g., category or instance membership. Each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K).

关键词： Image segmentation Shape Computational modeling Semantics computer architecture Transformers Feature extraction

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Deep Dehazing Powered by Image Processing Network

Deep Dehazing Powered by Image Processing Network

引用

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

作者： Guisik Kim Jinhee Park Junseok Kwon Korea Electronics Technology Institute Chung-Ang University Seoul Korea School of Computer Science and Engineering Chung-Ang University Seoul Korea

Image processing is a very fundamental technique in the field of low-level vision. However, with the development of deep learning over the past five years, most low-level vision methods tend to ignore this technique. Recent dehazing methods also refrain from using conventional image processing techniques, whereas only focusing on the development of new deep neural network (DNN) architectures. Unlike this recent trend, we show that image processing techniques are still competitive, if they are incorporated into DNNs. In this paper, we utilize conventional image processing techniques (i.e. curve adjustment, retinex decomposition, and multiple image fusion) for accurate dehazing. Moreover, we employ direct learning for stable dehazing performance. The proposed method can perform with low computational cost and easy to learn. The experimental results demonstrate that the proposed method produces accurate dehazing results compared to recent algorithms.

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Towards Fair Federated Learning with Zero-Shot Data Augmentation

Towards Fair Federated Learning with Zero-Shot Data Augmenta...

引用

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

作者： Hao, Weituo El-Khamy, Mostafa Lee, Jungwon Zhang, Jianyi Liang, Kevin J. Chen, Changyou Carin, Lawrence Duke Univ Durham NC 27706 USA Samsung Suwon South Korea SUNY Buffalo Buffalo NY USA

ISBN: (纸本)9781665448994

Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models, while having no access to the client data. Although it is recognized that statistical heterogeneity of the client local data yields slower global model convergence, it is less commonly recognized that it also yields a biased federated global model with a high variance of accuracy across clients. In this work, we aim to provide federated learning schemes with improved fairness. To tackle this challenge, we propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statistical heterogeneity, and encourage more uniform accuracy performance across clients in federated networks. We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server). Empirical results on a suite of datasets demonstrate the effectiveness of our methods on simultaneously improving the test accuracy and fairness.

关键词： computer vision computer aided instruction Distance learning conferences Distributed databases Collaborative work Data models

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HaLViT: Half of the Weights are Enough

HaLViT: Half of the Weights are Enough

引用

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

作者： Onur Can Koyun Behçet Uğur Töreyin Dept. of Artificial Intelligence and Data Engineering Informatics Institute Signal Processing for Computational Intelligence Research Group (SP4CING) İstanbul Technical University İstanbul Türkiye

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep learning architectures like Transformers and Convolutional Neural Networks (CNNs) have led to ground-breaking advances across numerous fields. However, their extensive need for parameters poses challenges for implementation in environments with limited resources. In our research, we propose a strategy that focuses on the utilization of the column and row spaces of weight matrices, significantly reducing the number of required model parameters without substantially affecting performance. This technique is applied to both Bottleneck and Attention layers, achieving a notable reduction in parameters with minimal impact on model efficacy. Our proposed model, HaLViT, exemplifies a parameter-efficient vision Transformer. Through rigorous experiments on the ImageNet dataset and COCO dataset, HaLViT’s performance validates the effectiveness of our method, offering results comparable to those of conventional models.

关键词： computer vision conferences Computational modeling Deep architecture Transformers pattern recognition Convolutional neural networks

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UBoCo : Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection

UBoCo : Unsupervised Boundary Contrastive Learning for Gener...

引用

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

作者： Kang, Hyolim Kim, Jinwoo Kim, Taehyun Kim, Seon Joo Yonsei Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Generic Event Boundary Detection (GEBD) is a newly suggested video understanding task that aims to find one level deeper semantic boundaries of events. Bridging the gap between natural human perception and video understanding, it has various potential applications, including interpretable and semantically valid video parsing. Still at an early development stage, existing GEBD solvers are simple extensions of relevant video understanding tasks, disregarding GEBD's distinctive characteristics. In this paper, we propose a novel framework for unsupervised/supervised GEBD, by using the Temporal Self-similarity Matrix (TSM) as the video representation. The new Recursive TSM Parsing (RTP) algorithm exploits local diagonal patterns in TSM to detect boundaries, and it is combined with the Boundary Contrastive (BoCo) loss to train our encoder to generate more informative TSMs. Our framework can be applied to both unsupervised and supervised settings, with both achieving state-of-the-art performance by a huge margin in GEBD benchmark. Especially, our unsupervised method outperforms the previous state-of-the-art "supervised" model, implying its exceptional efficacy.

关键词： computer vision Computational modeling Semantics Benchmark testing pattern recognition Task analysis

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Combining Weight Pruning and Knowledge Distillation For CNN Compression

Combining Weight Pruning and Knowledge Distillation For CNN ...

引用

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

作者： Aghli, Nima Ribeiro, Eraldo Florida Inst Technol 150 W Univ Blvd Melbourne FL 32901 USA

ISBN: (纸本)9781665448994

Complex deep convolutional neural networks such as ResNet require expensive hardware such as powerful GPUs to achieve real-time performance. This problem is critical for applications that run on low-end embedded GPU or CPU systems with limited resources. As a result, model compression for deep neural networks becomes an important research topic. Popular compression methods such as weight pruning remove redundant neurons from the CNN without affecting the network's output accuracy. While these pruning methods work well on simple networks such as VGG or AlexNet, they are not suitable for compressing current state-of-the-art networks such as ResNets because of these networks' complex architectures with dimensionality dependencies. This dependency results in filter pruning breaking the structure of ResNets leading to an untrainable network. In this paper, we first use the weight pruning method only on a selective number of layers in the ResNet architecture to avoid breaking the network structure. Second, we introduce a knowledge distillation architecture and a loss function to compress the untouched layers during the pruning. We test our method on both image-based regression and classification networks for head-pose estimation and image classification. Our compression method reduces the models' size significantly while maintaining the accuracy very close to the baseline model.

关键词： Image coding Neurons Estimation Graphics processing units computer architecture Real-time systems Convolutional neural networks

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