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"BNN - BN = ?": Training Binary Neural Networks without Batch Normalization

"BNN - BN = ?": Training Binary Neural Networks without Batc...

引用

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

作者： Chen, Tianlong Zhang, Zhenyu Ouyang, Xu Liu, Zechun Shen, Zhiqiang Wang, Zhangyang Univ Texas Austin Austin TX 78712 USA Univ Sci & Technol China Hefei Anhui Peoples R China Cornell Univ Ithaca NY 14853 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665448994

Batch normalization (BN) is a key facilitator and considered essential for state-of-the-art binary neural networks (BNN). However, the BN layer is costly to calculate and is typically implemented with non-binary parameters, leaving a hurdle for the efficient implementation of BNN training. It also introduces undesirable dependence between samples within each batch. Inspired by the latest advance on Batch Normalization Free (BN-Free) training [7], we extend their framework to training BNNs, and for the first time demonstrate that BNs can be completely removed from BNN training and inference regimes. By plugging in and customizing techniques including adaptive gradient clipping, scale weight standardization, and specialized bottleneck block, a BN-free BNN is capable of maintaining competitive accuracy compared to its BN-based counterpart. Extensive experiments validate the effectiveness of our proposal across diverse BNN backbones and datasets. For example, after removing BNs from the state-of-the-art ReActNets [38], it can still be trained with our proposed methodology to achieve 92.08%, 68.34%, and 68.0% accuracy on CIFAR-10, CIFAR-100, and ImageNet respectively, with marginal performance drop (0.23% similar to 0.44% on CIFAR and 1.40% on ImageNet). Codes and pre-trained models are available at: https://***/VITA-Group/BNN_NoBN.

关键词： Training computer vision conferences Neural networks Standardization Hardware pattern recognition

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IB-DRR - Incremental Learning with Information-Back Discrete Representation Replay

IB-DRR - Incremental Learning with Information-Back Discrete...

引用

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

作者： Jiang, Jian Cetin, Edoardo Celiktutan, Oya Kings Coll London Ctr Robot Res Dept Engn London England

ISBN: (纸本)9781665448994

Incremental learning aims to enable machine learning models to continuously acquire new knowledge given new classes, while maintaining the knowledge already learned for old classes. Saving a subset of training samples of previously seen classes in the memory and replaying them during new training phases is proven to be an efficient and effective way to fulfil this aim. It is evident that the larger number of exemplars the model inherits the better performance it can achieve. However, finding a trade-off between the model performance and the number of samples to save for each class is still an open problem for replay-based incremental learning and is increasingly desirable for real-life applications. In this paper, we approach this open problem by tapping into a two-step compression approach. The first step is a lossy compression, we propose to encode input images and save their discrete latent representations in the form of 'codes' that are learned using a hierarchical Vector Quantised Variational Autoencoder (VQ-VAE). In the second step, we further compress 'codes' losslessly by learning a hierarchical latent variable model with bits-back asymmetric numeral systems (BB-ANS). To compensate for the information lost in the first step compression, we introduce an Information Back (IB) mechanism that utilizes raw exemplars for a contrastive learning loss to regularise the training of a classifier. By maintaining all seen exemplars' representations in the format of 'codes', Discrete Representation Replay (DRR) outperforms the state-of-art method on CIFAR-100 by a margin of 4% average accuracy with a much less memory cost required for saving samples. Incorporated with IB and saving a small set of old raw exemplars as well, the average accuracy of DRR can be further improved by 2%.

关键词： Training computer vision Image coding conferences Memory management Machine learning pattern recognition

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CroSpace6D: Leveraging Geometric and Motion Cues for High-Precision Cross-Domain 6DoF Pose Estimation for Non-Cooperative Spacecrafts

CroSpace6D: Leveraging Geometric and Motion Cues for High-Pr...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jianhong Zuo Shengyang Zhang Qianyu Zhang Yutao Zhao Baichuan Liu Aodi Wu Xue Wan Leizheng Shu Guohua Kang Nanjing University of Aeronautics and Astronautics Chinese Academy of Sciences Technology and Engineering Center for Space Utilization University of Chinese Academy of Sciences

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The utilization of monocular vision for non-cooperative spacecraft pose estimation has been significantly researched in space target monitoring, on-orbit servicing, and satellite maintenance. The challenge lies in addressing the cross-domain variations in shape, texture, lighting, and motion patterns between simulated and real captured images. To tackle this issue, a novel domain adaptation 6DoF pose estimation algorithm is proposed to extract the geometric and semantic consistency between cross-domain training and testing datasets. Experimental results demonstrate that our pose estimation method achieves state-of-the-art performance on the SPARK2024 dataset.

关键词： Space vehicles Training Satellites Shape Pose estimation Semantics pattern recognition

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Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery

Superpixels and Graph Convolutional Neural Networks for Effi...

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

作者： Dadsetan, Saba Pichler, David Wilson, David Hovakimyan, Naira Hobbs, Jennifer Univ Pittsburgh Pittsburgh PA 15260 USA Intelinair Inc San Francisco CA USA Univ Illinois Urbana IL USA

ISBN: (纸本)9781665448994

Advances in remote sensing technology have led to the capture of massive amounts of data. Increased image resolution, more frequent revisit times, and additional spectral channels have created an explosion in the amount of data that is available to provide analyses and intelligence across domains, including agriculture. However, the processing of this data comes with a cost in terms of computation time and money, both of which must be considered when the goal of an algorithm is to provide real-time intelligence to improve efficiencies. Specifically, we seek to identify nutrient deficient areas from remotely sensed data to alert farmers to regions that require attention;detection of nutrient deficient areas is a key task in precision agriculture as farmers must quickly respond to struggling areas to protect their harvests. Past methods have focused on pixel-level classification (i.e. semantic segmentation) of the field to achieve these tasks, often using deep learning models with tens-of-millions of parameters. In contrast, we propose a much lighter graph-based method to perform node-based classification. We first use Simple Linear Iterative Cluster (SLIC) to produce super-pixels across the field. Then, to perform segmentation across the non-Euclidean domain of superpixels, we leverage a Graph Convolutional Neural Network (GCN). This model has 4-orders-of-magnitude fewer parameters than a CNN model and trains in a matter of minutes.

关键词： Image segmentation Semantics Agriculture Real-time systems Computational efficiency pattern recognition Convolutional neural networks

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Semi-synthesis: A fast way to produce effective datasets for stereo matching

Semi-synthesis: A fast way to produce effective datasets for...

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

作者： He, Ju Zhou, Enyu Sun, Liusheng Lei, Fei Liu, Chenyang Sun, Wenxiu Johns Hopkins Univ Baltimore MD 21218 USA SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781665448994

Stereo matching is an important problem in computer vision which has drawn tremendous research attention for decades. Recent years, data-driven methods with convolutional neural networks (CNNs) are continuously pushing stereo matching to new heights. However, data-driven methods require large amount of training data, which is not an easy task for real stereo data due to the annotation difficulties of per-pixel ground-truth disparity. Though synthetic dataset is proposed to fill the gaps of large data demand, the fine-tuning on real dataset is still needed due to the domain variances between synthetic data and real data. In this paper, we found that in synthetic datasets, close-to-real-scene texture rendering is a key factor to boost up stereo matching performance, while close-to-real-scene 3D modeling is less important. We then propose semi-synthetic, an effective and fast way to synthesize large amount of data with close-to-real-scene texture to minimize the gap between synthetic data and real data. Extensive experiments demonstrate that models trained with our proposed semi-synthetic datasets achieve significantly better performance than with general synthetic datasets, especially on real data benchmarks with limited training data. With further fine-tuning on the real dataset, we also achieve SOTA performance on Middlebury and competitive results on KITTI and ETH3D datasets.

关键词： Training computer vision Three-dimensional displays Training data Benchmark testing Rendering (computer graphics) Environmental factors

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A Tale of Two CILs: The Connections between Class Incremental Learning and Class Imbalanced Learning, and Beyond

A Tale of Two CILs: The Connections between Class Incrementa...

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

作者： He, Chen Wang, Ruiping Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc CAS Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781665448994

Catastrophic forgetting, the main challenge of Class Incremental Learning, is closely related to the classifier's bias due to imbalanced data, and most researchers resort to empirical techniques to remove the bias. Such anti-bias tricks share many ideas with the field of Class Imbalanced Learning, which encourages us to reflect on why these tricks work, and how we can design more principled solutions from a different perspective. In this paper, we comprehensively analyze the connections and seek possible collaborations between these two fields, i.e. Class Incremental Learning and Class Imbalanced Learning. Specifically, we first provide a panoramic view of recent bias correction tricks from the perspective of handling class imbalance. Then, we show that an adapted post-scaling technique which originates from Class Imbalanced Learning is on par with or even outperforms SOTA Class Incremental Learning method. Visualization via violin plots and polar charts further sheds light on how SOTA methods address the class imbalance problem from a more intuitive geometric perspective. These findings may encourage further infiltration between the two closely connected fields, but also raise concerns about whether it is correct that Class Incremental Learning degenerates into a class imbalance problem.

关键词： Learning systems computer vision conferences Collaboration pattern recognition

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IQMA Network: Image Quality Multi-scale Assessment Network

IQMA Network: Image Quality Multi-scale Assessment Network

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

作者： Guo, Haiyang Bin, Yi Hou, Yuqing Zhang, Qing Luo, Hengliang Meituan Beijing Peoples R China

ISBN: (纸本)9781665448994

Image Quality Assessment (IQA), which aims to provide computational models for automatically predicting perceptual image quality, is an important computer vision task with many applications. In recent years, a variety of IQA methods have been proposed based on different metric designs, which measure the quality of images affected by various types of distortion. However, with the rapid development of Generative Adversarial Networks (GAN), a new challenge has been brought to the IQA community. Especially, the GAN-based Image Reconstruction (IR) methods overfit the traditional PSNR-based IQA methods by generating images with sharper edges and texture-like noises, leading the outputs to be similar to the reference image in appearance but with loss of details. In this paper, we propose a bilateral-branch multi-scale image quality estimation network, named IQMA network. The two branches are designed with Feature Pyramid Network (FPN)-like architecture, extracting multi-scale features for patches of the reference image and corresponding patches of the distorted image separately. Then features of the same scale from both branches are sent into several scale-specific feature fusion modules. Each module performs feature fusion and a novelly designed pooling operation for corresponding features. Then several score regression modules are used to learn a quality score for each scale. Finally, image scores for different scales are fused as the quality score of the image. IQMA network has achieved 1st place on the NTIRE 21 IQA public leaderboard and 2nd place on the NTIRE 21 IQA private leaderboard, and consistently outperforms existing state-of-the-art (SOTA) methods on LIVE and TID2013.

关键词： Image quality computer vision Image edge detection computer architecture Predictive models Feature extraction Generative adversarial networks

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CycleGANAS: Differentiable Neural Architecture Search for CycleGAN

CycleGANAS: Differentiable Neural Architecture Search for Cy...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Taegun An Changhee Joo Department of Computer Science and Engineering Korea University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We develop a Neural Architecture Search (NAS) framework for CycleGAN that carries out unpaired image-to-image translation task. Extending previous NAS techniques for Generative Adversarial Networks (GANs) to CycleGAN is not straightforward due to the task difference and greater search space. We design architectures that consist of a stack of simple ResNet-based cells and develop a search method that effectively explore the large search space. We show that our framework, called CycleGANAS, not only effectively discovers high-performance architectures that either match or surpass the performance of the original CycleGAN, but also successfully addresses the data imbalance by individual architecture search for each translation direction. To our best knowledge, it is the first NAS result for CycleGAN and shed light on NAS for more complex structures. Our code is at https://***/antaegun20/CycleGANAS. 1

关键词： Dimensionality reduction Search methods Stacking Neural networks computer architecture Generative adversarial networks Generators

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Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment

Hierarchical NeuroSymbolic Approach for Comprehensive and Ex...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Lauren Okamoto Paritosh Parmar Princeton University IHPC A*STAR Singapore

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Action quality assessment (AQA) applies computer vision to quantitatively assess the performance or execution of a human action. Current AQA approaches are end-to-end neural models, which lack transparency and tend to be biased because they are trained on subjective human judgements as ground-truth. To address these issues, we introduce a neuro-symbolic paradigm for AQA, which uses neural networks to abstract interpretable symbols from video data and makes quality assessments by applying rules to those symbols. We take diving as the case study. We found that domain experts prefer our system and find it more informative than purely neural approaches to AQA in diving. Our system also achieves state-of-the-art action recognition and temporal segmentation, and automatically generates a detailed report that breaks the dive down into its elements and provides objective scoring with visual evidence. As verified by a group of domain experts, this report may be used to assist judges in scoring, help train judges, and provide feedback to divers. Annotated training data and code: https://***/laurenok24/NSAQA.

关键词： computer vision Visualization Codes conferences Computational modeling Neural networks Symbols

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Class-Incremental Learning with Generative Classifiers

Class-Incremental Learning with Generative Classifiers

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

作者： van de Ven, Gido M. Li, Zhe Tolias, Andreas S. Baylor Coll Med Ctr Neurosci & Artificial Intelligence Houston TX 77030 USA Univ Cambridge Computat & Biol Learning Lab Cambridge England Rice Univ Dept Elect & Comp Engn POB 1892 Houston TX 77251 USA

ISBN: (纸本)9781665448994

Incrementally training deep neural networks to recognize new classes is a challenging problem. Most existing class-incremental learning methods store data or use generative replay, both of which have drawbacks, while 'rehearsal-free' alternatives such as parameter regularization or bias-correction methods do not consistently achieve high performance. Here, we put forward a new strategy for class-incremental learning: generative classification. Rather than directly learning the conditional distribution p(y\x), our proposal is to learn the joint distribution p(x, y), factorized as p(x\y)p(y), and to perform classification using Bayes' rule. As a proof-of-principle, here we implement this strategy by training a variational autoencoder for each class to be learned and by using importance sampling to estimate the likelihoods p(x\y). This simple approach performs very well on a diverse set of continual learning benchmarks, outperforming generative replay and other existing baselines that do not store data.

关键词： Training Learning systems Deep learning computer vision Monte Carlo methods conferences Benchmark testing

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