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Wake-Sleep Energy Based Models for Continual Learning

Wake-Sleep Energy Based Models for Continual Learning

引用

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

作者： Vaibhav Singh Anna Choromanska Shuang Li Yilun Du Concordia University Mila New York University University of Toronto Massachusetts Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a novel approach for continually training Energy-Based Models (EBMs) on the classification problems in the challenging setting of class incremental learning. Despite the fact that EBMs offer longer retention of knowledge on prior tasks, training EBMs contrastively remains a challenge. Driven by biological plausibility, we leverage the observation that sleep in humans supports active system consolidation and propose a new approach for training EBMs, which we call Wake-Sleep Energy Based Models (WS-EBMs), which rely on wake-sleep cycles. Our training approach consists of short wake phases followed by long sleep phases. During the short wake phase, the free energy associated with ground truth labels is minimized, which conditions the model towards the correct solutions. This is followed by a long sleep phase, where the free energy of the whole system is minimized contrastively, which allows the model to push the energy of incorrect solutions further from the correct response. We provide a theoretical analysis of WS-EBM showing that it satisfies the sufficient condition for designing proper EBM loss. Our empirical evaluation confirms the plausibility of our approach and demonstrates favorable performance of WS-EBM compared to traditional EBM training as well as state-of-the-art class-incremental continual learning techniques. Furthermore, our proposed two-phase training strategy can be easily integrated with existing techniques resulting in substantial boosts in their performance. Finally, we also provide interesting insights justifying our approach by analyzing the orthogonality between the sequential task vectors, and flatness of the optimized energy surfaces, which may guide the design of class incremental continual learning strategies.

关键词： Training Continuing education Sufficient conditions computer vision Incremental learning Biological system modeling conferences

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Imaging Signal Recovery Using Neural Network Priors Under Uncertain Forward Model Parameters

Imaging Signal Recovery Using Neural Network Priors Under Un...

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

作者： Xiwen Chen Wenhui Zhu Peijie Qiu Abolfazl Razi School of Computing Clemson University School of Computing and Augmented Intelligence Arizona State University McKeley School of Engineering Washington University in St. Louis

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Inverse imaging problems (IIPs) arise in various applications, with the main objective of reconstructing an image from its compressed measurements. This problem is often ill-posed for being under-determined with multiple interchangeably consistent solutions. The best solution inherently depends on prior knowledge or assumptions, such as the sparsity of the image. Furthermore, the reconstruction process for most IIPs relies significantly on the imaging (i.e. forward model) parameters, which might not be fully known, or the measurement device may undergo calibration drifts. These uncertainties in the forward model create substantial challenges, where inaccurate reconstructions usually happen when the postulated parameters of the forward model do not fully match the actual ones. In this work, we devoted to tackling accurate reconstruction under the context of a set of possible forward model parameters that exist. Here, we propose a novel Moment-Aggregation (MA) framework that is compatible with the popular IIP solution by using a neural network prior. Specifically, our method can reconstruct the signal by considering all candidate parameters of the forward model simultaneously during the update of the neural network. We theoretically demonstrate the convergence of the MA framework, which has a similar complexity with reconstruction under the known forward model parameters. Proof-of-concept experiments demonstrate that the proposed MA achieves performance comparable to the forward model with the known precise parameter in reconstruction across both compressive sensing and phase retrieval applications, with a PSNR gap of 0.17 to 1.94 over various datasets, including MNIST, X-ray, Glas, and MoNuseg. This highlights our method’s significant potential in reconstruction under an uncertain forward model.

关键词： Uncertainty Image coding Computational modeling Neural networks Imaging pattern recognition Image reconstruction

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CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D Face Generation

CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D F...

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

作者： Rahul Dey Bernhard Egger Vishnu Naresh Boddeti Ye Wang Tim K. Marks Mitsubishi Electric Research Laboratories (MERL) Michigan State University (MSU) Friedrich-Alexander-Universitat (FAU) Erlangen-Nürnberg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating 3D faces and rendering them to images has numerous practical applications in areas including AR/VR, dataset generation, and avatar creation. In recent years, there has been a significant surge in the development of high-fidelity 3D face generation techniques such as StyleSDF, which combine the benefits of 3D implicit neural representations with those of style-based 2D generative adversarial networks (GANs). Although these implicit 3D GAN approaches generate highly realistic faces using a 3D representation, the properties of the generated faces cannot easily be edited or controlled. Meanwhile, linear 3D morphable models (3DMMs) and their nonlinear extensions have also made significant strides in their expressive capacity and quality, but they have yet to match the image quality achieved by GANs. This paper proposes a new method, CoLa-SDF, which combines the controllability of nonlinear 3DMMs with the high fidelity of implicit 3D GANs. Inspired by the impressive photorealism and expressive 3D representations of StyleSDF, our model uses a similar architecture but enforces the latent space to match the interpretable and physical parameters of the nonlinear 3D morphable model MOST-GAN. We demonstrate high-fidelity image synthesis and subsequent 3D manipulation with full control over the disentangled latent parameters.

关键词： Image quality Solid modeling Photorealism Three-dimensional displays Image synthesis Generative adversarial networks Rendering (computer graphics)

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Deep Learning-Based Identification of Arctic Ocean Boundaries and Near-Surface Phenomena in Underwater Echograms

Deep Learning-Based Identification of Arctic Ocean Boundarie...

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

作者： Femina Senjaliya Melissa Cote Amanda Dash Alexandra Branzan Albu Andrea Niemi Stéphane Gauthier Julek Chawarski Steve Pearce Kaan Ersahin Keath Borg Electrical and Computer Engineering University of Victoria Victoria Canada ASL Environmental Sciences Victoria Canada Fisheries and Oceans Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Monitoring marine environments is a crucial part of understanding the impact of oceans on global climate and their importance for biodiversity and ecological systems, particularly in the Arctic region. Underwater active acoustic surveys with moored multi-frequency echosounders allow for the continuous collection of valuable data reflecting the complex dynamics of these environments. This paper addresses the automatic identification of sea surface boundaries and near-surface phenomena in echograms using deep learning methods to support researchers such as biologists in their work, who typically rely on time-consuming manual analyses. We propose a two-step process that first characterizes echograms according to the surface conditions using an image classification paradigm and then identifies the sea surface boundary and near-surface bubbles and their extent in the water column using a semantic segmentation paradigm. Segmentation is carried out using surface type-specific models, which perform better than a single global segmentation model. We also propose learning strategies, such as a custom boundary loss function, that further improve performance. Experiments with various image classification and semantic segmentation architectures allow us to select the most efficient models for Arctic echogram analysis that, when used in conjunction within our proposed pipeline and our learning strategies, offer excellent results.

关键词： Surveys Sea surface Analytical models Biological system modeling Semantic segmentation Arctic Surface treatment

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Ensemble Dehazing Networks for Non-homogeneous Haze

Ensemble Dehazing Networks for Non-homogeneous Haze

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

作者： Mingzhao Yu Venkateswararao Cherukuri Tiantong Guo Vishal Monga The Department of Electrical Engineering The Pennsylvania State University PA USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Image dehazing is one of the most challenging imaging inverse problems. Although deep learning methods produce compelling results, one of the most crucial practical challenge is that of non-homogeneous haze, which remains an open problem. To address this challenge, we propose 3 models that are inspired by ensemble techniques. First, we propose a DenseNet based single-encoder four-decoders structure denoted as EDN-3J, wherein among the four decoders, three of them output estimates of dehazed images (J 1 , J 2 , J 3 ) that are then weighted and combined via weight maps learned by the fourth decoder. In our second model called EDN-AT, the single-encoder four-decoders structure is maintained while three decoders are transformed to jointly estimate two physical inverse haze models that share a common transmission map t with two distinct ambient light maps (A 1 , A 2 ). The two inverse haze models are then weighted and combined for the final dehazed image. To endow two sub-models flexibility and to induce capability of modeling non-homogeneous haze, we apply attention masks to ambient lights. Both the weight maps and attention maps are generated from the fourth decoder. Finally, in contrast to the above two ensemble models, we propose an encoder-decoder-U-net structure called EDN-EDU, which is a sequential hierarchical ensemble of two different dehazing networks with different modeling capacities. Experiments performed on challenging benchmark image datasets of NTIRE'20 and NTIRE'19 demonstrate that the proposed models outperform many state-of-the-art methods and this fact is particularly demonstrated in the NTIRE-2020 contest where the EDN-AT model achieves the best result in the sense of the perceptual quality metric LPIPS.

关键词： Decoding Task analysis Inverse problems Machine learning Training computer vision Measurement

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LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction

LGFN: Lightweight Light Field Image Super-Resolution using L...

引用

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

作者： Zhongxin Yu Liang Chen Zhiyun Zeng Kunping Yang Shaofei Luo Shaorui Chen Cheng Zhong Fujian Normal University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Capturing different intensity and directions of light rays at the same scene, Light field (LF) can encode the 3D scene cues into a 4D LF image, which has a wide range of applications (i.e., post-capture refocusing and depth sensing). LF image super-resolution (SR) aims to improve the image resolution limited by the performance of LF camera sensor. Although existing methods have achieved promising results, the practical application of these models is limited because they are not lightweight enough. In this paper, we propose a lightweight model named LGFN, which integrates the local and global features of different views and the features of different channels for LF image SR. Specifically, owing to neighboring regions of the same pixel position in different sub-aperture images exhibit similar structural relationships, we design a lightweight CNN-based feature extraction module (namely, DGCE) to extract local features better through feature modulation. Meanwhile, as the position beyond the boundaries in the LF image presents a large disparity, we propose an efficient spatial attention module (namely, ESAM) which uses decomposable large-kernel convolution to obtain an enlarged receptive field and an efficient channel attention module (namely, ECAM). Compared with the existing LF image SR models with large parameter, our model has a parameter of 0.45M and a FLOPs of 19.33G, which has achieved a competitive effect. Extensive experiments with ablation studies demonstrate the effectiveness of our proposed method, which ranked the second place in the Track 2 Fidelity & Efficiency of NTIRE2024 Light Field Super Resolution Challenge and the seventh place in the Track 1 Fidelity.

关键词： computer vision Three-dimensional displays Convolution conferences Superresolution Modulation Feature extraction

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ART•V: Auto-Regressive Text-to-Video Generation with Diffusion Models

ART•V: Auto-Regressive Text-to-Video Generation with Diffus...

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

作者： Wenming Weng Ruoyu Feng Yanhui Wang Qi Dai Chunyu Wang Dacheng Yin Zhiyuan Zhao Kai Qiu Jianmin Bao Yuhui Yuan Chong Luo Yueyi Zhang Zhiwei Xiong University of Science and Technology of China Microsoft Research Asia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present ART•V, an efficient framework for autoregressive video generation with diffusion models. Unlike existing methods that generate entire videos in one-shot, ART•V generates a single frame at a time, conditioned on the previous ones. The framework offers three distinct advantages. First, it only learns simple continual motions between adjacent frames, therefore avoiding modeling complex long-range motions that require huge training data. Second, it preserves the high-fidelity generation ability of the pre-trained image diffusion models by making only minimal network modifications. Third, it can generate arbitrarily long videos conditioned on a variety of prompts such as text, image or their combinations, making it highly versatile and flexible. To combat the common drifting issue in AR models, we propose masked diffusion model which implicitly learns which information can be drawn from reference images rather than network predictions, in order to reduce the risk of generating inconsistent appearances that cause drifting. Moreover, we further enhance generation coherence by conditioning it on the initial frame, which typically contains minimal noise. This is particularly useful for long video generation. When trained for only two weeks on four GPUs, ART•V already can generate videos with natural motions, rich details and a high level of aesthetic quality. Besides, it enables various appealing applications, e.g. composing a long video from multiple text prompts.

关键词： computer vision conferences Noise Training data Coherence Predictive models Diffusion models

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T2LM: Long-Term 3D Human Motion Generation from Multiple Sentences

T2LM: Long-Term 3D Human Motion Generation from Multiple Sen...

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

作者： Taeryung Lee Fabien Baradel Thomas Lucas Kyoung Mu Lee Grégory Rogez IPAI & ASRI Naver LABS Europe Dept. of ECE Seoul National University Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we address the challenging problem of long-term 3D human motion generation. Specifically, we aim to generate a long sequence of smoothly connected actions from a stream of multiple sentences (i.e., paragraph). Previous long-term motion generating approaches were mostly based on recurrent methods, using previously generated motion chunks as input for the next step. However, this approach has two drawbacks: 1) it relies on sequential datasets, which are expensive; 2) these methods yield unrealistic gaps between motions generated at each step. To address these issues, we introduce simple yet effective T2LM, a continuous long-term generation framework that can be trained without sequential data. T2LM comprises two components: a 1D-convolutional VQVAE, trained to compress motion to sequences of latent vectors, and a Transformer-based Text Encoder that predicts a latent sequence given an input text. At inference, a sequence of sentences is translated into a continuous stream of latent vectors. This is then decoded into a motion by the VQVAE decoder; the use of 1D convolutions with a local temporal receptive field avoids temporal inconsistencies between training and generated sequences. This simple constraint on the VQ-VAE allows it to be trained with short sequences only and produces smoother transitions. T2LM outperforms prior long-term generation models while overcoming the constraint of requiring sequential data; it is also competitive with SOTA single-action generation models.

关键词： Training computer vision Three-dimensional displays conferences Computational modeling Transformers Vectors

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The Third Monocular Depth Estimation Challenge

The Third Monocular Depth Estimation Challenge

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

作者： Jaime Spencer Fabio Tosi Matteo Poggi Ripudaman Singh Arora Chris Russell Simon Hadfield Richard Bowden GuangYuan Zhou ZhengXin Li Qiang Rao YiPing Bao Xiao Liu Dohyeong Kim Jinseong Kim Myunghyun Kim Mykola Lavreniuk Rui Li Qing Mao Jiang Wu Yu Zhu Jinqiu Sun Yanning Zhang Suraj Patni Aradhye Agarwal Chetan Arora Pihai Sun Kui Jiang Gang Wu Jian Liu Xianming Liu Junjun Jiang Xidan Zhang Jianing Wei Fangjun Wang Zhiming Tan Jiabao Wang Albert Luginov Muhammad Shahzad Seyed Hosseini Aleksander Trajcevski James H. Elder Independent University of Bologna Blue River Technology Oxford Internet Institute University of Surrey ByteDance University of Chinese Academy of Science RGA Inc. Space Research Institute NASU-SSAU Kyiv Ukraine Northwestern Polytechnical University Indian Institute of Technology Delhi Harbin Institute of Technology Fujitsu GuangXi University University of Reading York University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised. The challenge received a total of 19 submissions outperforming the baseline on the test set: 10 among them submitted a report describing their approach, highlighting a diffused use of foundational models such as Depth Anything at the core of their method. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%.

关键词： computer vision Three-dimensional displays conferences Computational modeling Estimation pattern recognition

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 ieee.

关键词： Data acquisition

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