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Uncovering the Hidden Cost of Model Compression

Uncovering the Hidden Cost of Model Compression

引用

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

作者： Diganta Misra Muawiz Chaudhary Agam Goyal Bharat Runwal Pin Yu Chen Carnegie Mellon University Landskape AI Mila - Quebec AI Institute Concordia University University of Wisconsin-Madison Mila - Quebec AI Institute IBM Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In an age dominated by resource-intensive foundation models, the ability to efficiently adapt to downstream tasks is crucial. Visual Prompting (VP), drawing inspiration from the prompting techniques employed in Large Language Models (LLMs), has emerged as a pivotal method for transfer learning in the realm of computer vision. As the importance of efficiency continues to rise, research into model compression has become indispensable in alleviating the computational burdens associated with training and deploying over-parameterized neural networks. A primary objective in model compression is to develop sparse and/or quantized models capable of matching or even surpassing the performance of their over-parameterized, full-precision counterparts. Although previous studies have explored the effects of model compression on transfer learning, its impact on visual prompting-based transfer remains unclear. This study aims to bridge this gap, shedding light on the fact that model compression detrimentally impacts the performance of visual prompting-based transfer, particularly evident in scenarios with low data volume. Furthermore, our findings underscore the adverse influence of sparsity on the calibration of downstream visual-prompted models. However, intriguingly, we also illustrate that such negative effects on calibration are not present when models are compressed via quantization. This empirical investigation underscores the need for a nuanced understanding beyond mere accuracy in sparse and quantized settings, thereby paving the way for further exploration in Visual Prompting techniques tailored for sparse and quantized models.

关键词： Visualization Adaptation models Solid modeling computer vision Costs Quantization (signal) Computational modeling

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AdvDenoise: Fast Generation Framework of Universal and Robust Adversarial Patches Using Denoise

AdvDenoise: Fast Generation Framework of Universal and Robus...

引用

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

作者： Jing Li Zigan Wang Jinliang Li School of Economics and Management Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Adversarial patch attacks, which can mislead deep learning models and the human eye in both the digital and physical domains, have led to a trust crisis. Traditional approaches to generating powerful attack patches require extensive, multi-scenario data, but suffer from slow search speeds in adversarial gradient space, resulting in low global attack success rates and high costs. Especially high resource-consuming attack methods are not sufficient to pose sufficient threats, which leads to the vulnerability of defense. To address these challenges, we present a novel framework AdvDenoise to generate universal adversarial patches fast and robustly using denoise. Concretely, we leverage the power of denoising diffusion probabilistic models to craft or optimize these patches, deviating from traditional pure gradient-based methods. We conduct comprehensive experiments on both pre-trained convolutional neural networks and vision transformer detectors, evaluating our method on standard benchmarks as well as in simulated real-world physical settings. The results demonstrate that our framework outperforms strong baselines, achieving higher attack success rates, better transferability across models, and improved robustness to transformations while maintaining visual realism and computational efficiency. When our method’s performance approaches the state-of-the-art, the total time required to generate 100-shots adversarial patches is substantially lower than the state-of-the-art methods, with a remarkable 48.15% reduction in time complexity. The code and examples are publicly available at https://***/advdenoise/advdenoise.

关键词： computer vision Visualization Computational modeling Noise reduction Diffusion models Transformers Robustness

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Swift Parameter-free Attention Network for Efficient Super-Resolution

Swift Parameter-free Attention Network for Efficient Super-R...

引用

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

作者： Cheng Wan Hongyuan Yu Zhiqi Li Yihang Chen Yajun Zou Yuqing Liu Xuanwu Yin Kunlong Zuo Georgia Institute of Technology Xiaomi Inc

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Single Image Super-Resolution (SISR) is a crucial task in low-level computer vision, aiming to reconstruct high-resolution images from low-resolution counterparts. Conventional attention mechanisms have significantly improved SISR performance but often result in complex network structures and large number of parameters, leading to slow inference speed and large model size. To address this issue, we propose the Swift Parameter-free Attention Network (SPAN), a highly efficient SISR model that balances parameter count, inference speed, and image quality. SPAN employs a novel parameter-free attention mechanism, which leverages symmetric activation functions and residual connections to enhance high-contribution information and suppress redundant information. Our theoretical analysis demonstrates the effectiveness of this design in achieving the attention mechanism’s purpose. We evaluate SPAN on multiple benchmarks, showing that it outperforms existing efficient super-resolution models in terms of both image quality and inference speed, achieving a significant quality-speed trade-off. This makes SPAN highly suitable for real-world applications, particularly in resource-constrained scenarios. Notably, we won the first place both in the overall performance track and runtime track of the NTIRE 2024 efficient super-resolution challenge. Our code and models are made publicly available at https://***/hongyuanyu/span.

关键词： Image quality Performance evaluation computer vision Attention mechanisms Runtime Computational modeling Superresolution

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Generalized Few-Shot Meets Remote Sensing: Discovering Novel Classes in Land Cover Mapping via Hybrid Semantic Segmentation Framework

Generalized Few-Shot Meets Remote Sensing: Discovering Novel...

引用

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

作者： Zhuohong Li Fangxiao Lu Jiaqi Zou Lei Hu Hongyan Zhang Wuhan University China University of Geosciences

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Land-cover mapping is one of the vital applications in Earth observation, aiming at classifying each pixel’s land-cover type of remote-sensing images. As natural and human activities change the landscape, the land-cover map needs to be rapidly updated. However, discovering newly appeared land-cover types in existing classification systems is still a non-trivial task hindered by various scales of complex land objects and insufficient labeled data over a wide-span geographic area. In this paper, we propose a generalized few-shot segmentation-based framework, named SegLand, to update novel classes in high-resolution land-cover mapping. Specifically, the proposed framework is designed in three parts: (a) Data pre-processing: the base training set and the few-shot support sets of novel classes are analyzed and augmented; (b) Hybrid segmentation structure: Multiple base learners and a modified Projection onto Orthogonal Prototypes (POP) network are combined to enhance the base-class recognition and to dig novel classes from insufficient labels data; (c) Ultimate fusion: the semantic segmentation results of the base learners and POP network are reasonably fused. The proposed framework has won first place in the leaderboard of the OpenEarthMap Land Cover Mapping Few-Shot Challenge. Experiments demonstrate the superiority of the framework for automatically updating novel land-cover classes with limited labeled data.

关键词： Earth Training computer vision Semantic segmentation conferences Land surface Prototypes

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Semi-Stereo: A Universal Stereo Matching Framework for Imperfect Data via Semi-supervised Learning

Semi-Stereo: A Universal Stereo Matching Framework for Imper...

引用

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

作者： Xin Yue Zongqing Lu Xiangru Lin Wenjia Ren Zhijing Shao Haonan Hu Yu Zhang Qingmin Liao Tsinghua University Prometheus Vision Technology Co. Ltd.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Data matters in deep-learning-based binocular stereo matching. Obtaining a perfect dataset for stereo matching is hard and thus imperfect data is common in existing benchmark datasets, such as KITTI, ETH3D and Middlebury. The imperfectness typically has two forms: sparse-labeled data or even unlabeled data. Current stereo matching networks ignore the supervision from these imperfect data itself, even the semi-supervised networks often suffer from confirmation bias in the predictions. Besides, current methods lack a unified solution to utilize the supervision signal from those imperfect data. To mitigate this research gap, we propose Semi-Stereo, the first unified stereo matching framework empowered by the teacher-student paradigm where the teacher and the student networks are trained in a mutual-beneficial manner. To explore the rich knowledge in imperfect data, we propose a consistency regularization module with weak-strong augmentation strategies. Further, in order for the teacher to provide more reliable pseudo labels, we design a confidence module, powered by left-right consistency (LRC) check and disparity distribution entropy (DDE). Extensive experiments demonstrate Semi-Stereo produces accurate and consistent predictions in untrained semantic regions and improves the performance of baseline networks in multiple tasks, including domain adaptation and domain generalization.

关键词： Measurement computer vision conferences computer network reliability Semantics Semisupervised learning Benchmark testing

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Multiscale Kernels for Enhanced U-Shaped Network to Improve 3D Neuron Tracing

Multiscale Kernels for Enhanced U-Shaped Network to Improve ...

引用

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

作者： Heng Wang Donghao Zhang Yang Song Siqi Liu Heng Huang Mei Chen Hanchuan Peng Weidong Cai School of Computer Science University of Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Digital Services Digital Technology & Innovation Siemens Healthineers USA Department of Electrical and Computer Engineering University of Pittsburgh USA Department of Informatics University of Albany State University of New York USA Allen Institute for Brain Science USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Digital neuron morphology reconstruction from three-dimensional (3D) volumetric optical microscope images is an important procedure to rebuild the connections and structures of neural circuits. Even though many approaches have been proposed to achieve precise tracing, it is still a challenging task especially when images are polluted by noise or have discontinuity in their neuron structures. In this paper, we propose a new framework to overcome these issues by performing neuron segmentation prior to tracing. Our proposed framework adopts a novel 3D U-shaped convolutional neural network (CNN) with multiscale kernel fusion and spatial fusion to perform the image segmentation. We then perform the iterative back-tracking tracing algorithm on the output of the network. Evaluated on the Janelia dataset from the BigNeuron project, our proposed framework achieves competitive tracing performance.

关键词： Neurons Three-dimensional displays Image segmentation Kernel Gold Optical imaging Standards

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Mesh Variational Autoencoders with Edge Contraction Pooling

Mesh Variational Autoencoders with Edge Contraction Pooling

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

作者： Yu-Jie Yuan Yu-Kun Lai Jie Yang Qi Duan Hongbo Fu Lin Gao Institute of Computing Technology CAS Beijing Key Laboratory of Mobile Computing and Pervasive Device School of Computer Science and Informatics Cardiff University UK SenseTime Research City University of Hong Kong Shenzhen Research Institute of Big Data Shenzhen

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

3D shape analysis is an important research topic in computer vision and graphics. While existing methods have generalized image-based deep learning to meshes using graph-based convolutions, the lack of an effective pooling operation restricts the learning capability of their networks. In this paper, we propose a novel pooling operation for mesh datasets with the same connectivity but different geometry, by building a mesh hierarchy using mesh simplification. For this purpose, we develop a modified mesh simplification method to avoid generating highly irregularly sized triangles. Our pooling operation effectively encodes the correspondence between coarser and finer meshes in the hierarchy. We then present a variational auto-encoder (VAE) structure with the edge contraction pooling and graph-based convolutions, to explore probability latent spaces of 3D surfaces and perform 3D shape generation. Our network requires far fewer parameters than the original mesh VAE and thus can handle denser models thanks to our new pooling operation and convolutional kernels. Our evaluation also shows that our method has better generalization ability and is more reliable in various applications, including shape generation and shape interpolation.

关键词： Three-dimensional displays Shape Strain Interpolation Neural networks Machine learning

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Masked Autoencoders are Secretly Efficient Learners

Masked Autoencoders are Secretly Efficient Learners

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

作者： Zihao Wei Chen Wei Jieru Mei Yutong Bai Zeyu Wang Xianhang Li Hongru Zhu Huiyu Wang Alan Yuille Yuyin Zhou Cihang Xie University of Michigan Ann Arbor Johns Hopkins University UC Santa Cruz Meta

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper provides an efficiency study of training Masked Autoencoders (MAE), a framework introduced by He et al. [13] for pre-training vision Transformers (ViTs). Our results surprisingly reveal that MAE can learn at a faster speed and with fewer training samples while maintaining high performance. To accelerate its training, our changes are simple and straightforward: in the pre-training stage, we aggressively increase the masking ratio, decrease the number of training epochs, and reduce the decoder depth to lower the pre-training cost; in the fine-tuning stage, we demonstrate that layer-wise learning rate decay plays a vital role in unlocking the full potential of pre-trained models. Under this setup, we further verify the sample efficiency of MAE: training MAE is hardly affected even when using only 20% of the original training *** combining these strategies, we are able to accelerate MAE pre-training by a factor of 82 or more, with little performance drop. For example, we are able to pre-train a ViT-B in ~9 hours using a single NVIDIA A100 GPU and achieve 82.9% top-1 accuracy on the downstream ImageNet classification task. Additionally, we also verify the speed acceleration on another MAE extension, SupMAE.

关键词： Training computer vision Costs conferences Computational modeling Graphics processing units Transformers

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Advancing COVID-19 Detection in 3D CT Scans

Advancing COVID-19 Detection in 3D CT Scans

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

作者： Qingqiu Li Runtian Yuan Junlin Hou Jilan Xu Yuejie Zhang Rui Feng Hao Chen School of Academy for Engineering and Technology Fudan University China School of Computer Science Fudan University China Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model. Firstly, we analyze the characteristics of 3D CT scans and remove the non-lung parts, facilitating the model to focus on lesion-related areas and reducing computational cost. We use ResNeSt-50 as the strong feature extractor, exploring various pre-trained weights and fine-tuning methods. After a thorough comparison, we initialize our model with CMC v1 pre-trained weights which incorporate COVID-19-specific prior knowledge, and perform Visual Prompt Tuning to reduce the number of training parameters. The superiority of our model is demonstrated through extensive experiments, showing significant improvements in COVID-19 detection performance compared to the baseline model. Among 12 participating teams, our method ranked 4th in the 4th COVID-19 Competition Challenge I with an average Macro F1 Score of 94.24%.

关键词： COVID-19 Training Solid modeling Visualization Three-dimensional displays Computational modeling Computed tomography

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Attention Guidance Distillation Network for Efficient Image Super-Resolution

Attention Guidance Distillation Network for Efficient Image ...

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

作者： Hongyuan Wang Ziyan Wei Qingting Tang Shuli Cheng Liejun Wang Yongming Li School of Computer Science and Technology Xinjiang University Ürümqi China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Over the past decade, neural network-based super-resolution techniques have been developed on a large scale with impressive achievements. Many novel solutions have been proposed, among which lightweight solutions based on convolutional neural networks have been designed for applications in edge devices. To better realize this application, we propose a more lightweight attention guidance distillation network (AGDN). We design the attention guidance distillation block (AGDB) with more efficient space, channel and self-attention as the infrastructure of AGDN. Specifically, multi-level variance-aware spatial attention (MVSA) is designed to better capture structurally information-rich regions with new multi-scale convolution and local variance alignment. Reallocated contrast-aware channel attention (RCCA) is designed to enhance the processing of common information in all channels while redistributing weights across channels. Sparse global self-attention (SGSA) is introduced for selecting the most useful similarity values for image reconstruction. Extensive experiments demonstrate that AGDN strikes a better balance between performance and complexity compared to other models, achieving SOTA performance on several benchmark tests. In addition, our AGDN-S ranks first in the FLOPs track and second in the Parameters track of the NTIRE 2024 Efficient SR Challenge. The code is available at https://***/daydreamer2024/AGDN.

关键词： Runtime Limiting Convolution Superresolution Feature extraction pattern recognition Image restoration

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