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Regularized Parameter Uncertainty for Improving Generalization in Reinforcement Learning

Regularized Parameter Uncertainty for Improving Generalizati...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Pehuen Moure Longbiao Cheng Joachim Ott Zuowen Wang Shih-Chii Liu Institute of Neuroinformatics ETH Zurich and University of Zurich

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In order for reinforcement learning (RL) agents to be deployed in real-world environments, they must be able to generalize to unseen environments. However, RL struggles with out-of-distribution generalization, often due to overfitting the particulars of the training environment. Although regularization techniques from supervised learning can be applied to avoid over-fitting, the differences between supervised learning and RL limit their application. To address this, we propose the Signal-to-Noise Ratio regulated Parameter Uncertainty Network (SNR PUN) for RL. We introduce SNR as a new measure of regularizing the parameter uncertainty of a network and provide a formal analysis explaining why SNR regularization works well for RL. We demonstrate the effectiveness of our proposed method to generalize in several simulated environments; and in a physical system showing the possibility of using SNR PUN for applying RL to real-world applications.

关键词： Training Uncertain systems computer vision Supervised learning Measurement uncertainty Reinforcement learning pattern recognition

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SMD-Nets: Stereo Mixture Density Networks

SMD-Nets: Stereo Mixture Density Networks

引用

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

作者： Tosi, Fabio Liao, Yiyi Schmitt, Carolin Geiger, Andreas Univ Bologna Dept Comp Sci & Engn DISI Bologna Italy Univ Tubingen Autonomous Vis Grp MPI IS Tubingen Germany

ISBN: (纸本)9781665445092

Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs efficiently remains challenging. In this paper, we propose Stereo Mixture Density Networks (SMD-Nets), a simple yet effective learning framework compatible with a wide class of 2D and 3D architectures which ameliorates both issues. Specifically, we exploit bimodal mixture densities as output representation and show that this allows for sharp and precise disparity estimates near discontinuities while explicitly modeling the aleatoric uncertainty inherent in the observations. Moreover, we formulate disparity estimation as a continuous problem in the image domain, allowing our model to query disparities at arbitrary spatial precision. We carry out comprehensive experiments on a new high-resolution and highly realistic synthetic stereo dataset, consisting of stereo pairs at 8Mpx resolution, as well as on real-world stereo datasets. Our experiments demonstrate increased depth accuracy near object boundaries and prediction of ultra high-resolution disparity maps on standard GPUs. We demonstrate the flexibility of our technique by improving the performance of a variety of stereo backbones.

关键词： Solid modeling Three-dimensional displays Uncertainty Superresolution Estimation pattern recognition Spatial resolution

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Effective Snapshot Compressive-spectral Imaging via Deep Denoising and Total Variation Priors

Effective Snapshot Compressive-spectral Imaging via Deep Den...

引用

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

作者： Qiu, Haiquan Wang, Yao Meng, Deyu Xi An Jiao Tong Univ Xian Peoples R China Shanghai Data Technol Co Ltd Shanghai Peoples R China Macau Univ Sci & Technol Macau Peoples R China

ISBN: (纸本)9781665445092

Snapshot compressive imaging (SCI) is a new type of compressive imaging system that compresses multiple frames of images into a single snapshot measurement, which enjoys low cost, low bandwidth, and high-speed sensing rate. By applying the existing SCI methods to deal with hyperspectral images, however, could not fully exploit the underlying structures, and thereby demonstrate unsatisfactory reconstruction performance. To remedy such issue, this paper aims to propose a new effective method by taking advantage of two intrinsic priors of the hyperspectral images, namely deep image denoising and total variation (TV) priors. Specifically, we propose an optimization objective to utilize these two priors. By solving this optimization objective, our method is equivalent to incorporate a weighted FFDNet and a 2DTV or 3DTV denoiser into the plug-and-play framework. Extensive numerical experiments demonstrate the outperformance of the proposed method over several state-of-the-art alternatives. Additionally, we provide a detailed convergence analysis of the resulting plug-and-play algorithm under relatively weak conditions such as without using diminishing step sizes. The code is available at https://***/ucker/SCI-TVFFDNet.

关键词： Image coding TV Noise reduction Imaging Sensors pattern recognition Optimization

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Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification

Contrastive Learning based Hybrid Networks for Long-Tailed I...

引用

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

作者： Wang, Peng Han, Kai Wei, Xiu-Shen Zhang, Lei Wang, Lei Univ Wollongong Wollongong NSW Australia Univ Bristol Bristol Avon England Nanjing Univ Sci & Technol Nanjing Peoples R China Northwestern Polytech Univ Xian Peoples R China

ISBN: (纸本)9781665445092

Learning discriminative image representations plays a vital role in long-tailed image classification because it can ease the classifier learning in imbalanced cases. Given the promising performance contrastive learning has shown recently in representation learning, in this work, we explore effective supervised contrastive learning strategies and tailor them to learn better image representations from imbalanced data in order to boost the classification accuracy thereon. Specifically, we propose a novel hybrid network structure being composed of a supervised contrastive loss to learn image representations and a cross-entropy loss to learn classifiers, where the learning is progressively transited from feature learning to the classifier learning to embody the idea that better features make better classifiers. We explore two variants of contrastive loss for feature learning, which vary in the forms but share a common idea of pulling the samples from the same class together in the normalized embedding space and pushing the samples from different classes apart. One of them is the recently proposed supervised contrastive (SC) loss, which is designed on top of the state-of-the-art unsupervised contrastive loss by incorporating positive samples from the same class. The other is a prototypical supervised contrastive (PSC) learning strategy which addresses the intensive memory consumption in standard SC loss and thus shows more promise under limited memory budget. Extensive experiments on three long-tailed classification datasets demonstrate the advantage of the proposed contrastive learning based hybrid networks in long-tailed classification.

关键词： Training computer vision Memory management Graphics processing units Image representation pattern recognition Proposals

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Technical Report of NICE Challenge at cvpr 2024: Caption Re-ranking Evaluation Using Ensembled CLIP and Consensus Scores

Technical Report of NICE Challenge at CVPR 2024: Caption Re-...

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Kiyoon Jeong Woojun Lee Woongchan Nam Minjeong Ma Pilsung Kang School of Industrial and Management Engineering Korea University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This report presents the ECO (Ensembled Clip score and cOnsensus score) pipeline from team DSBALAB, which is a new framework used to evaluate and rank captions for a given image. ECO selects the most accurate caption describing image. It is made possible by combining an Ensembled CLIP score, which considers the semantic alignment between the image and captions, with a Consensus score that accounts for the essentialness of the captions. Using this framework, we achieved notable success in the cvpr 2024 Workshop Challenge on Caption Re-ranking Evaluation at the New Frontiers for Zero-Shot Image Captioning Evaluation (NICE). Specifically, we secured third place based on the CIDEr metric, second in both the SPICE and METEOR metrics, and first in the ROUGE-L and all BLEU Score metrics. The code and configuration for the ECO framework are available at https://***/DSBA-Lab/ECO.

关键词： Measurement Training computer vision conferences Semantics Pipelines Writing

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Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning

Removing the Background by Adding the Background: Towards Ba...

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

作者： Wang, Jinpeng Gao, Yuting Li, Ke Lin, Yiqi Ma, Andy J. Cheng, Hao Peng, Pai Huang, Feiyue Ji, Rongrong Sun, Xing Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Tencent Youtu Lab Shenzhen Guangdong Peoples R China Xiamen Univ Xiamen Fujian Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665445092

Self-supervised learning has shown great potentials in improving the video representation ability of deep neural networks by getting supervision from the data itself. However, some of the current methods tend to cheat from the background, i.e., the prediction is highly dependent on the video background instead of the motion, making the model vulnerable to background changes. To mitigate the model reliance towards the background, we propose to remove the background impact by adding the background. That is, given a video, we randomly select a static frame and add it to every other frames to construct a distracting video sample. Then we force the model to pull the feature of the distracting video and the feature of the original video closer, so that the model is explicitly restricted to resist the background influence, focusing more on the motion changes. We term our method as Background Erasing (BE). It is worth noting that the implementation of our method is so simple and neat and can be added to most of the SOTA methods without much efforts. Specifically, BE brings 16.4% and 19.1% improvements with MoCo on the severely biased datasets UCF101 and HMDB51, and 14.5% improvement on the less biased dataset Diving48.

关键词： Deep learning computer vision Force Focusing Resists Predictive models Benchmark testing

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Few-Shot Object Detection via Classification Refinement and Distractor Retreatment

Few-Shot Object Detection via Classification Refinement and ...

引用

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

作者： Li, Yiting Zhu, Haiyue Cheng, Yu Wang, Wenxin Teo, Chek Sing Xiang, Cheng Vadakkepat, Prahlad Lee, Tong Heng Natl Univ Singapore Singapore Singapore Agcy Sci Technol & Res SIMTech Singapore Singapore

ISBN: (纸本)9781665445092

We aim to tackle the challenging Few-Shot Object Detection (FSOD), where data-scarce categories are presented during the model learning. The failure modes of Faster-RCNN in FSOD are investigated, and we find that the performance degradation is mainly due to the classification incapability (false positives) caused by category confusion, which motivates us to address FSOD from a novel aspect of classification refinement. Specifically, we address the intrinsic limitation from the aspects of both architectural enhancement and hard-example mining. We introduce a novel few-shot classification refinement mechanism where a de coupled Few-Shot Classification Network (FSCN) is employed to improve the final classification of a base detector. Moreover, we especially probe a commonly-overlooked but destructive issue of FSOD, i.e., the presence of distractor samples due to the incomplete annotations where images from the base set may contain novel-class objects but remain unlabelled. Retreatment solutions are developed to eliminate the incurred false positives. For FSCN training, the distractor is formulated as a semi-supervised problem, where a distractor utilization loss is proposed to make proper use of it for boosting the data-scarce classes, while a confidence-guided dataset pruning (CGDP) technique is developed to facilitate the few-shot adaptation of base detector. Experiments demonstrate that our proposed framework achieves state-of-the-art FSOD performance on public datasets, e.g., Pascal VOC and MS-COCO.

关键词： Training Degradation computer vision Filtering Detectors Object detection Boosting

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Adapters Strike Back

Adapters Strike Back

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jan-Martin O. Steitz Stefan Roth Department of Computer Science TU Darmstadt hessian.AI

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Adapters provide an efficient and lightweight mechanism for adapting trained transformer models to a variety of dif-ferent tasks. However, they have often been found to be outperformed by other adaptation mechanisms including low-rank adaptation. In this paper, we provide an in-depth study of adapters, their internal structure, as well as vari-ous implementation choices. We uncover pitfalls for using adapters and suggest a concrete, improved adapter architecture, called Adapter+, that not only outperforms previous adapter implementations but surpasses a number of other, more complex adaptation mechanisms in several challenging settings. Despite this, our suggested adapter is highly robust and, unlike previous work, requires little to no manual inter-vention when addressing a novel scenario. Adapter+ reaches state-of-the-art average accuracy on the VTAB benchmark, even without a per-task hyperparameter optimization. † † Code is available at https://***/visinf/adapter_plus.

关键词： computer vision Adaptation models Accuracy Systematics computer architecture Benchmark testing Transformers

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Learning Correlation Structures for vision Transformers

Learning Correlation Structures for Vision Transformers

引用

conference on computer vision and pattern recognition (cvpr)

作者： Manjin Kim Paul Hongsuck Seo Cordelia Schmid Minsu Cho POSTECH Korea University Google Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce a new attention mechanism, dubbed structural self-attention (StructSA), that leverages rich correlation patterns naturally emerging in key-query interactions of attention. StructSA generates attention maps by recog-nizing space-time structures of key-query correlations via convolution and uses them to dynamically aggregate lo-cal contexts of value features. This effectively leverages rich structural patterns in images and videos such as scene layouts, object motion, and inter-object relations. Using StructSA as a main building block, we develop the structural vision transformer (StructViT) and evaluate its effective-ness on both image and video classification tasks, achieving state-of-the-art results on ImageNet-I K, Kinetics-400, Something-Something VI & V2, Diving-48, and FineGym.

关键词： Representation learning computer vision Visualization Correlation Aggregates Layout Transformers

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Reconsidering CO2 emissions from computer vision

Reconsidering CO2 emissions from Computer Vision

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

作者： Fu, Andre Hosseini, Mahdi S. Plataniotis, Konstantinos N. Univ Toronto Toronto ON Canada Univ New Brunswick Fredericton NB Canada

ISBN: (纸本)9781665448994

Climate change is a pressing issue that is currently affecting and will affect every part of our lives. It's becoming incredibly vital we, as a society, address the climate crisis as a universal effort, including those in the computer vision (CV) community. In this work, we analyze the total cost of CO2 emissions by breaking it into (1) the architecture creation cost and (2) the life-time evaluation cost. We show that over time, these costs are non-negligible and are having a direct impact on our future. Importantly, we conduct an ethical analysis of how the CV-community is unintentionally overlooking its own ethical AI principles by emitting this level of CO2. To address these concerns, we propose adding "enforcement" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.

关键词： computer vision Climate change conferences computer architecture Pressing pattern recognition Artificial intelligence

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