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Contextualising Implicit Representations for Semantic Tasks

Contextualising Implicit Representations for Semantic Tasks

引用

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

作者： Theo W. Costain Kejie Li Victor A. Prisacariu Active Vision Lab University of Oxford

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Prior works have demonstrated that implicit representations trained only for reconstruction tasks typically generate encodings that are not useful for semantic tasks. In this work, we propose a method that contextualises the encodings of implicit representations, enabling their use in downstream tasks (e.g. semantic segmentation), without requiring access to the original training data or encoding network. Using an implicit representation trained for a reconstruction task alone, our contextualising module takes an encoding trained for reconstruction only and reveals meaningful semantic information that is hidden in the encodings, without compromising the reconstruction performance. With our proposed module, it becomes possible to pre-train implicit representations on larger datasets, improving their reconstruction performance compared to training on only a smaller labelled dataset, whilst maintaining their segmentation performance on the labelled dataset. Importantly, our method allows for future foundation implicit representation models to be fine-tuned on unseen tasks, without retraining the encoder.

关键词： Training computer vision Semantic segmentation conferences Semantics Training data Encoding

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I Find Your Lack of Uncertainty in computer vision Disturbing

I Find Your Lack of Uncertainty in Computer Vision Disturbin...

引用

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

作者： Valdenegro-Toro, Matias German Res Ctr Artificial Intelligence Robert Hooke Str 1 D-28359 Bremen Germany

ISBN: (纸本)9781665448994

Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computer vision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computer vision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computer vision applications and recommendations.

关键词： computer vision Uncertainty Computational modeling conferences Neural networks pattern recognition

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A Survey on 3D Egocentric Human Pose Estimation

A Survey on 3D Egocentric Human Pose Estimation

引用

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

作者： Md Mushfiqur Azam Kevin Desai The University of Texas at San Antonio

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Egocentric human pose estimation aims to estimate human body poses and develop body representations from a first-person camera perspective. It has gained vast popularity in recent years because of its wide range of applications in sectors like XR-technologies, human-computer interaction, and fitness tracking. However, to the best of our knowledge, there is no systematic literature review based on the proposed solutions regarding egocentric 3D human pose estimation. To that end, the aim of this survey paper is to provide an extensive overview of the current state of egocentric pose estimation research. In this paper, we categorize and discuss the popular datasets and the different pose estimation models, highlighting the strengths and weaknesses of different methods by comparative analysis. This survey can be a valuable resource for both researchers and practitioners in the field, offering insights into key concepts and cutting-edge solutions in egocentric pose estimation, its wide-ranging applications, as well as the open problems with future scope.

关键词： Surveys Human computer interaction computer vision Analytical models Three-dimensional displays conferences Pose estimation

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Lost in Translation: Lip-Sync Deepfake Detection from Audio-Video Mismatch

Lost in Translation: Lip-Sync Deepfake Detection from Audio-...

引用

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

作者： Matyas Bohacek Hany Farid Stanford University Stanford CA USA University of California Berkeley Berkeley CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Highly realistic voice cloning combined with AI-powered video manipulation allows for the creation of compelling lip-sync deepfakes where anyone can be made to say things they never did. The resulting fakes are being used to entertain, but also for everything from election related disinformation to small- and large-scale fraud. Lip-sync deepfakes can be particularly difficult to detect because only the mouth and jaw of the person talking is modified. We describe a robust and general-purpose technique to detect these fakes. This technique begins by independently translating the audio (using audio-to-text transcription) and video (using automated lip-reading). We then show that the resulting transcriptions are significantly mismatched for lip-sync deepfakes as compared to authentic videos. The robustness of this technique is evaluated against a controlled dataset of our creation and in-the-wild fakes, all of varying length and resolution.

关键词： Deepfakes computer vision Voting conferences Mouth Cloning Robustness

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Purposeful Regularization with Reinforcement Learning for Facial Expression recognition In-the-Wild

Purposeful Regularization with Reinforcement Learning for Fa...

引用

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

作者： SangHwa Hong Department of Industrial Engineering Seoul National University of Science and Technology Seoul South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Facial Expression recognition (FER), an essential aspect of emotion analysis through artificial intelligence, is a crucial research area. Although traditional approaches utilizing Convolutional Neural Networks (CNNs) for analyzing human emotions from facial expressions achieve superior accuracy over conventional machine learning methods, overfitting—especially arising severely from data collected in uncontrolled, In-the-wild settings—significantly impedes CNNs performance. This is due to the data scarcity and inherent noise inside In-the-wild data. To address this challenge, this paper introduces a novel regularization method that employs Reinforcement Learning (RL) to adaptively apply regularization hyperparameters appropriate for the evolving state of trained CNNs. Through experiments on various datasets such as CIFAR100, FER2013, and AffectNet datasets including diverse perspective analysis such as graphical, Grad-CAM and numerical analysis, it is demonstrated that the suggested method can alleviate memorization of noise in training data and promote learning of essential features. The significance of the suggested method lies in its demonstrated remarkable effectiveness in enhancing CNNs’ generalization and reducing overfitting.

关键词： Training Emotion recognition computer vision Numerical analysis Face recognition conferences Noise

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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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Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy

Self-Supervised Learning with Generative Adversarial Network...

引用

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

作者： Bashir Kazimi Karina Ruzaeva Stefan Sandfeld IAS-9 Forschungszentrum Jülich GmbH Jülich Germany RWTH Aachen University Aachen Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this work, we explore the potential of self-supervised learning with Generative Adversarial Networks (GANs) for electron microscopy datasets. We show how self-supervised pretraining facilitates efficient fine-tuning for a spectrum of downstream tasks, including semantic segmentation, denoising, noise & background removal, and super-resolution. Experimentation with varying model complexities and receptive field sizes reveals the remarkable phenomenon that fine-tuned models of lower complexity consistently outperform more complex models with random weight initialization. We demonstrate the versatility of self-supervised pretraining across various downstream tasks in the context of electron microscopy, allowing faster convergence and better performance. We conclude that self-supervised pretraining serves as a powerful catalyst, being especially advantageous when limited annotated data are available and efficient scaling of computational cost is important.

关键词： Training computer vision Superresolution Noise reduction Noise Self-supervised learning Predictive models

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Temporal surface frame anomalies for deepfake video detection

Temporal surface frame anomalies for deepfake video detectio...

引用

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

作者： Andrea Ciamarra Roberto Caldelli Alberto Del Bimbo University of Florence Florence Italy Universitas Mercatorum Rome Italy CNIT Florence Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Looking at a video sequence where a foreground person is represented is not as time ago anymore. Deepfakes have revolutionized our way to watch at such contents and nowadays we are more often used to wonder if what we are seeing is real or is just a mystification. In this context of generalized disinformation, the need for reliable solutions to help common users, and not only, to make an assessment on this kind of video sequences is strongly upcoming. In this paper, a novel approach which leverages on temporal surface frame anomalies in order to reveal deepfake videos is introduced. The method searches for possible discrepancies, induced by deepfake manipulation, in the surfaces belonging to the captured scene and in their evolution along the temporal axis. These features are used as input of a pipeline based on deep neural networks to perform a binary assessment on the video itself. Experimental results witness that such a methodology can achieve significant performance in terms of detection accuracy.

关键词： Deepfakes Accuracy Instruments Video sequences Pipelines Watches pattern recognition

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Strategies to Leverage Foundational Model Knowledge in Object Affordance Grounding

Strategies to Leverage Foundational Model Knowledge in Objec...

引用

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

作者： Arushi Rai Kyle Buettner Adriana Kovashka Department of Computer Science University of Pittsburgh PA USA Intelligent Systems Program University of Pittsburgh PA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

An important task for intelligent systems is affordance grounding, where the goal is to locate regions on an object where an action can be performed. Past weakly supervised approaches learn from human-object interaction (HOI) by transferring grounding knowledge from exocentric to ego-centric views of an object. The use of HOI priors is inherently noisy and thus provides a limited source of supervision. To address this challenge, we identify that recent foundational models (i.e. VLMs and LLMs) can serve as auxiliary sources of knowledge for frameworks due to their vast world knowledge. In this work, we propose strategies to extract and leverage foundational model knowledge related to attributes and object parts to enhance an HOI-based affordance grounding framework. In particular, we propose to combine HOI and foundational model priors through (1) a spatial consistency loss and (2) heatmap aggregation. Our strategies result in mKLD and mNSS improvements, and insights suggest future directions for improving affordance grounding capabilities.

关键词： Heating systems computer vision Grounding Affordances conferences Pipelines pattern recognition

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S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal

S3R-Net: A Single-Stage Approach to Self-Supervised Shadow R...

引用

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

作者： Nikolina Kubiak Armin Mustafa Graeme Phillipson Stephen Jolly Simon Hadfield University of Surrey BBC R&D

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper we present S3R-Net, the Self-Supervised Shadow Removal Network. The two-branch WGAN model achieves self-supervision 1 relying on the unify-and-adapt phenomenon - it unifies the style of the output data and infers its characteristics from a database of unaligned shadow-free reference images. This approach stands in contrast to the large body of supervised frameworks. S3R-Net also differentiates itself from the few existing self-supervised models operating in a cycle-consistent manner, as it is a non-cyclic, unidirectional solution. The proposed framework achieves comparable numerical scores to recent self-supervised shadow removal models while exhibiting superior qualitative performance and keeping the computational cost low. The pre-trained models and the code can be found in our github repo.

关键词： computer vision Codes Databases Computational modeling conferences Data models Numerical models

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