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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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Complex Style Image Transformations for Domain Generalization in Medical Images

Complex Style Image Transformations for Domain Generalizatio...

引用

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

作者： Nikolaos Spanos Anastasios Arsenos Paraskevi-Antonia Theofilou Paraskevi Tzouveli Athanasios Voulodimos Stefanos Kollias National Technical University of Athens Greece

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The absence of well-structured large datasets in medical computer vision results in decreased performance of automated systems and, especially, of deep learning models. Domain generalization techniques aim to approach unknown domains from a single data source. In this paper we introduce a novel framework, named CompStyle, which leverages style transfer and adversarial training, along with high-level input complexity augmentation to effectively expand the domain space and address unknown distributions. State-of-the-art style transfer methods depend on the existence of subdomains within the source dataset. However, this can lead to an inherent dataset bias in the image creation. Input-level augmentation can provide a solution to this problem by widening the domain space in the source dataset and boost performance on out-of-domain distributions. We provide results from experiments on semantic segmentation on prostate data and corruption robustness on cardiac data which demonstrate the effectiveness of our approach. Our method increases performance in both tasks, without added cost to training time or resources.

关键词： Training computer vision Soft sensors Semantics Transformers Robustness Complexity theory

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Video Representation Learning for Conversational Facial Expression recognition Guided by Multiple View Reconstruction

Video Representation Learning for Conversational Facial Expr...

引用

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

作者： Valeriya Strizhkova Laura M. Ferrari Hadi Kachmar Antitza Dantcheva François Brémond INRIA Université Côte D’Azur

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Conversational facial expression recognition entails challenges such as handling of facial dynamics, small available datasets, low-intensity and fine-grained emotional expressions and extreme face angle. Towards addressing these challenges, we propose the Masking Action Units and Reconstructing multiple Angles (MAURA) pre-training. MAURA is an efficient self-supervised method that permits the use of small datasets, while preserving end-to-end conversational facial expression recognition with vision Transformer. MAURA masks videos using the location with active Action Units and reconstructs synchronized multi-view videos, thus learning the dependencies between muscle movements and encoding information, which might only be visible in few frames and/or in certain views. Based on one view (e.g., frontal), the encoder reconstructs other views (e.g., top, down, laterals). Such masking and reconstructing strategy provides a powerful representation, beneficial in facial expression downstream tasks. Our experimental analysis shows that we consistently outperform the state-of-the-art in the challenging settings of low-intensity and fine-grained conversational facial expression recognition on four datasets including in-the-wild DFEW, CMU-MOSEI, MFA and multi-view MEAD. Our results suggest that MAURA is able to learn robust and generic video representations.

关键词： Representation learning computer vision Emotion recognition Face recognition conferences Muscles Transformers

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Event-based Ball Spin Estimation in Sports

Event-based Ball Spin Estimation in Sports

引用

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

作者： Takuya Nakabayashi Kyota Higa Masahiro Yamaguchi Ryo Fujiwara Hideo Saito Keio University Yokohama Japan NEC Corporation Kawasaki Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Ball spin estimation in sports is important for analyzing the game. Since spin is generally too fast to be captured by a conventional camera, a high-speed camera is often used to capture images of the ball and estimate its spin. However, since a high-speed camera is not robust to changes in the lighting conditions, it is difficult to estimate spin in some environments. To solve these problems, this paper proposes a new method for ball spin estimation using an event camera. An event camera is a sensor inspired by the visual system of animals, which outputs the brightness changes in a scene. Event cameras have advantages such as high temporal resolution and high dynamic range, and can accurately capture the motion of a fast-spinning ball in various lighting conditions. Experimental results in a synthesized dataset showed that the proposed method can stably estimate spin up to 500 rps. It is also confirmed that the proposed method can estimate spin in the data obtained from actual sports games.

关键词： Estimation Lighting Games Visual systems Cameras Vectors pattern recognition

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DiffTED: One-shot Audio-driven TED Talk Video Generation with Diffusion-based Co-speech Gestures

DiffTED: One-shot Audio-driven TED Talk Video Generation wit...

引用

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

作者： Steven Hogue Chenxu Zhang Hamza Daruger Yapeng Tian Xiaohu Guo University of Texas at Dallas

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Audio-driven talking video generation has advanced significantly, but existing methods often depend on video-to-video translation techniques and traditional generative networks like GANs and they typically generate taking heads and co-speech gestures separately, leading to less coherent outputs. Furthermore, the gestures produced by these methods often appear overly smooth or subdued, lacking in diversity, and many gesture-centric approaches do not integrate talking head generation. To address these limitations, we introduce DiffTED, a new approach for one-shot audio-driven TED-style talking video generation from a single image. Specifically, we leverage a diffusion model to generate sequences of keypoints for a Thin-Plate Spline motion model, precisely controlling the avatar’s animation while ensuring temporally coherent and diverse gestures. This innovative approach utilizes classifier-free guidance, empowering the gestures to flow naturally with the audio input without relying on pre-trained classifiers. Experiments demonstrate that DiffTED generates temporally coherent talking videos with diverse co-speech gestures.

关键词： computer vision Head conferences Diffusion models Rendering (computer graphics) Animation pattern recognition

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A stroke of genius: Predicting the next move in badminton

A stroke of genius: Predicting the next move in badminton

引用

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

作者： Magnus Ibh Stella Graßhof Dan Witzner Hansen Machine Learning Group IT University of Copenhagen

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents, RallyTemPose, a transformer encoder-decoder model for predicting future badminton strokes based on previous rally actions. The model uses court position, skeleton poses, and player-specific embeddings to learn stroke and player-specific latent representations in a spatiotemporal encoder module. The representations are then used to condition the subsequent strokes in a decoder module through rally-aware fusion blocks, which provide additional relevant strategic and technical considerations to make more informed predictions. RallyTemPose shows improved forecasting accuracy compared to traditional sequential methods on two real-world badminton datasets. The performance boost can also be attributed to the inclusion of improved stroke embeddings extracted from the latent representation of a pre-trained large-language model subjected to detailed text descriptions of stroke descriptions. In the discussion, the latent representations learned by the encoder module show useful properties regarding player analysis and comparisons. The code can be found at: This https url.

关键词： computer vision conferences Predictive models Transformers Skeleton Data models Spatiotemporal phenomena

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End-to-end Solution for Tenebrio Molitor Rearing Monitoring with Uncertainty Estimation and Domain Shift Detection

End-to-end Solution for Tenebrio Molitor Rearing Monitoring ...

引用

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

作者： Paweł Majewski Piotr Lampa Robert Burduk Jacek Reiner Wrocław University of Science and Technology Poland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The large-scale rearing of edible insects, of which Tenebrio Molitor is a representative, requires monitoring using vision systems to control the process and to detect anomalies. Previously proposed solutions by researchers relied on multiple modules related to specific tasks (calculated coefficients) and specific types of models (instance segmentation, semantic segmentation). Long processing times and difficulties in maintaining and updating modules encourage the search for a more condensed solution as an end-to-end model. This paper proposed a modified YOLOv8 architecture extended with additional heads related to specific tasks. Heads were trained on problem-oriented small datasets, which significantly reduced the time spent on sample annotation. The proposed solution also included estimation of prediction uncertainty based on variation among predictions in model ensemble and detection of domain shift phenomenon. Quantitative results from the conducted experiments confirmed the potential of the developed solution.

关键词： Instance segmentation Uncertainty Semantic segmentation Machine vision Insects Estimation Process control

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DFM4SFM - Dense Feature Matching for Structure from Motion

DFM4SFM - Dense Feature Matching for Structure from Motion

引用

2023 ieee International conference on Image Processing Challenges and workshops, ICIPCW 2023

作者： Seibt, Simon Von Rymon Lipinski, Bartosz Chang, Thomas Latoschik, Marc Erich Nuremberg Institute of Technology Game Tech Lab Faculty of Computer Science Nuremberg Germany Institute of Computer Science University of Wuerzburg Human-Computer Interaction Group Wuerzburg Germany

ISBN: (纸本)9798350302585

Structure from motion (SfM) is a fundamental task in computer vision and allows recovering the 3D structure of a stationary scene from an image set. Finding robust and accurate feature matches plays a crucial role in the early stages of SfM. So in this work, we propose a novel method for computing image correspondences based on dense feature matching (DFM) using homographic decomposition: The underlying pipeline provides refinement of existing matches through iterative rematching, detection of occlusions and extrapolation of additional matches in critical image areas between image pairs. Our main contributions are improvements of DFM specifically for SfM, resulting in global refinement and global extrapolation of image correspondences between related views. Furthermore, we propose an iterative version of the Delaunay-triangulation-based outlier detection algorithm for robust processing of repeated image patterns. Through experiments, we demonstrate that the proposed method significantlv improves the reconstruction accuracy. © 2023 ieee.

关键词： computer vision

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ChaLearn LAP Large Scale Signer Independent Isolated Sign Language recognition Challenge: Design, Results and Future Research

ChaLearn LAP Large Scale Signer Independent Isolated Sign La...

引用

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

作者： Sincan, Ozge Mercanoglu Jacques Junior, Julio C. S. Escalera, Sergio Keles, Hacer Yalim Ankara Univ Ankara Turkey Comp Vis Ctr Barcelona Spain Univ Barcelona Barcelona Spain

ISBN: (纸本)9781665448994

The performances of Sign Language recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories.

关键词： computer vision Tracking Assistive technology Computational modeling Transfer learning Semantics Gesture recognition

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M2DAR: Multi-View Multi-Scale Driver Action recognition with vision Transformer

M2DAR: Multi-View Multi-Scale Driver Action Recognition with...

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

作者： Yunsheng Ma Liangqi Yuan Amr Abdelraouf Kyungtae Han Rohit Gupta Zihao Li Ziran Wang College of Engineering Purdue University Toyota Motor North America InfoTech Labs

Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed videos, namely M 2 DAR, with a particular focus on detecting distracted driving behaviors. Our system features a weight-sharing, multi-scale Transformer-based action recognition network that learns robust hierarchical representations. Furthermore, we propose a new election algorithm consisting of aggregation, filtering, merging, and selection processes to refine the preliminary results from the action recognition module across multiple views. Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset demonstrate the effectiveness of our approach, where we achieved an overlap score of 0.5921 on the A2 test set. Our source code is available at https://***/PurdueDigitalTwin/M2DAR.

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