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Christian Keilstrup Ingwersen; Artur Xarles; Albert Clapes; Meysam Madadi; Janus Nortoft Jensen; Morten Rieger Hannemose; Anders Bjorholm Dahl; Sergio Escalera |
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Title |
Video-based Skill Assessment for Golf: Estimating Golf Handicap |
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Conference Article |
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2023 |
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Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports |
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31-39 |
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Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large database of video recordings of golf swings. We investigate different regression, ranking and classification based methods and compare to a simple baseline approach. The performance is evaluated using mean squared error (MSE) as well as computing the percentages of correctly ranked pairs based on the Kendall correlation. Our results demonstrate an improvement over the baseline, with a 35% lower mean squared error and 68% correctly ranked pairs. However, achieving fine-grained skill assessment remains challenging. This work contributes to the development of AI-driven coaching systems and advances the understanding of video-based skill determination in the context of golf. |
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Otawa; Canada; October 2023 |
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MMSports |
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Admin @ si @ KXC2023 |
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3929 |
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Artur Xarles; Sergio Escalera; Thomas B. Moeslund; Albert Clapes |
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Title |
ASTRA: An Action Spotting TRAnsformer for Soccer Videos |
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2023 |
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Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports |
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93–102 |
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In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the presence of a long-tail data distribution, non-visibility in certain actions, and inherent label noise. To do so, ASTRA incorporates (a) a Transformer encoder-decoder architecture to achieve the desired output temporal resolution and to produce precise predictions, (b) a balanced mixup strategy to handle the long-tail distribution of the data, (c) an uncertainty-aware displacement head to capture the label variability, and (d) input audio signal to enhance detection of non-visible actions. Results demonstrate the effectiveness of ASTRA, achieving a tight Average-mAP of 66.82 on the test set. Moreover, in the SoccerNet 2023 Action Spotting challenge, we secure the 3rd position with an Average-mAP of 70.21 on the challenge set. |
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Otawa; Canada; October 2023 |
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MMSports |
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HUPBA |
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Admin @ si @ XEM2023 |
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3970 |
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David Berga; Xavier Otazu |
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Computations of top-down attention by modulating V1 dynamics |
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2020 |
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Computational and Mathematical Models in Vision |
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St. Pete Beach; Florida; May 2020 |
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MODVIS |
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NEUROBIT |
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Admin @ si @ BeO2020a |
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3376 |
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Santiago Segui; Michal Drozdzal; Ekaterina Zaytseva; Carolina Malagelada; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
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A new image centrality descriptor for wrinkle frame detection in WCE videos |
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2013 |
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13th IAPR Conference on Machine Vision Applications |
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Small bowel motility dysfunctions are a widespread functional disorder characterized by abdominal pain and altered bowel habits in the absence of specific and unique organic pathology. Current methods of diagnosis are complex and can only be conducted at some highly specialized referral centers. Wireless Video Capsule Endoscopy (WCE) could be an interesting diagnostic alternative that presents excellent clinical advantages, since it is non-invasive and can be conducted by non specialists. The purpose of this work is to present a new method for the detection of wrinkle frames in WCE, a critical characteristic to detect one of the main motility events: contractions. The method goes beyond the use of one of the classical image feature, the Histogram |
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Kyoto; Japan; May 2013 |
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MVA |
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OR; MILAB; 600.046;MV |
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Admin @ si @ SDZ2013 |
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2239 |
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Victor Borjas; Jordi Vitria; Petia Radeva |
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Gradient Histogram Background Modeling for People Detection in Stationary Camera Environments |
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2013 |
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13th IAPR Conference on Machine Vision Applications |
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Best Poster AwardOne of the big challenges of today person detectors is the decreasing of the false positive rate. In this paper, we propose a novel framework to customize person detectors in static camera scenarios in order to reduce this rate. This scheme includes background modeling for subtraction based on gradient histograms and Mean-Shift clustering. Our experiments show that the detection improved compared to using only the output from the pedestrian detector reducing 87% of the false positives and therefore the overall precision of the detection
was increased signi cantly. |
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Kyoto; Japan; May 2013 |
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MVA |
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OR; MILAB;MV |
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BVR2013 |
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2238 |
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M. Cruz; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
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Cross-spectral image registration and fusion: an evaluation study |
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2015 |
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2nd International Conference on Machine Vision and Machine Learning |
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multispectral imaging; image registration; data fusion; infrared and visible spectra |
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This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. |
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Barcelona; July 2015 |
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MVML |
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ADAS; 600.076 |
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Admin @ si @ CAV2015 |
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2629 |
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Riccardo Del Chiaro; Bartlomiej Twardowski; Andrew Bagdanov; Joost Van de Weijer |
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Recurrent attention to transient tasks for continual image captioning |
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2020 |
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34th Conference on Neural Information Processing Systems |
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Research on continual learning has led to a variety of approaches to mitigating catastrophic forgetting in feed-forward classification networks. Until now surprisingly little attention has been focused on continual learning of recurrent models applied to problems like image captioning. In this paper we take a systematic look at continual learning of LSTM-based models for image captioning. We propose an attention-based approach that explicitly accommodates the transient nature of vocabularies in continual image captioning tasks -- i.e. that task vocabularies are not disjoint. We call our method Recurrent Attention to Transient Tasks (RATT), and also show how to adapt continual learning approaches based on weight egularization and knowledge distillation to recurrent continual learning problems. We apply our approaches to incremental image captioning problem on two new continual learning benchmarks we define using the MS-COCO and Flickr30 datasets. Our results demonstrate that RATT is able to sequentially learn five captioning tasks while incurring no forgetting of previously learned ones. |
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virtual; December 2020 |
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LAMP; 600.120 |
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Admin @ si @ CTB2020 |
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3484 |
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Author |
Yaxing Wang; Lu Yu; Joost Van de Weijer |
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DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs |
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2020 |
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34th Conference on Neural Information Processing Systems |
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Image-to-image translation has recently achieved remarkable results. But despite current success, it suffers from inferior performance when translations between classes require large shape changes. We attribute this to the high-resolution bottlenecks which are used by current state-of-the-art image-to-image methods. Therefore, in this work, we propose a novel deep hierarchical Image-to-Image Translation method, called DeepI2I. We learn a model by leveraging hierarchical features: (a) structural information contained in the shallow layers and (b) semantic information extracted from the deep layers. To enable the training of deep I2I models on small datasets, we propose a novel transfer learning method, that transfers knowledge from pre-trained GANs. Specifically, we leverage the discriminator of a pre-trained GANs (i.e. BigGAN or StyleGAN) to initialize both the encoder and the discriminator and the pre-trained generator to initialize the generator of our model. Applying knowledge transfer leads to an alignment problem between the encoder and generator. We introduce an adaptor network to address this. On many-class image-to-image translation on three datasets (Animal faces, Birds, and Foods) we decrease mFID by at least 35% when compared to the state-of-the-art. Furthermore, we qualitatively and quantitatively demonstrate that transfer learning significantly improves the performance of I2I systems, especially for small datasets. Finally, we are the first to perform I2I translations for domains with over 100 classes. |
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virtual; December 2020 |
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LAMP; 600.120 |
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Admin @ si @ WYW2020 |
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3485 |
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Zhengying Liu; Zhen Xu; Shangeth Rajaa; Meysam Madadi; Julio C. S. Jacques Junior; Sergio Escalera; Adrien Pavao; Sebastien Treguer; Wei-Wei Tu; Isabelle Guyon |
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Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019 |
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2020 |
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Proceedings of Machine Learning Research |
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123 |
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242-252 |
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We present the design and results of recent competitions in Automated Deep Learning (AutoDL). In the AutoDL challenge series 2019, we organized 5 machine learning challenges: AutoCV, AutoCV2, AutoNLP, AutoSpeech and AutoDL. The first 4 challenges concern each a specific application domain, such as computer vision, natural language processing and speech recognition. At the time of March 2020, the last challenge AutoDL is still on-going and we only present its design. Some highlights of this work include: (1) a benchmark suite of baseline AutoML solutions, with emphasis on domains for which Deep Learning methods have had prior success (image, video, text, speech, etc); (2) a novel any-time learning framework, which opens doors for further theoretical consideration; (3) a repository of around 100 datasets (from all above domains) over half of which are released as public datasets to enable research on meta-learning; (4) analyses revealing that winning solutions generalize to new unseen datasets, validating progress towards universal AutoML solution; (5) open-sourcing of the challenge platform, the starting kit, the dataset formatting toolkit, and all winning solutions (All information available at {autodl.chalearn.org}). |
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Admin @ si @ LXR2020 |
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3500 |
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Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer |
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Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation |
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2022 |
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36th Conference on Neural Information Processing Systems |
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We propose a simple but effective source-free domain adaptation (SFDA) method.
Treating SFDA as an unsupervised clustering problem and following the intuition
that local neighbors in feature space should have more similar predictions than
other features, we propose to optimize an objective of prediction consistency. This
objective encourages local neighborhood features in feature space to have similar
predictions while features farther away in feature space have dissimilar predictions, leading to efficient feature clustering and cluster assignment simultaneously. For efficient training, we seek to optimize an upper-bound of the objective resulting in two simple terms. Furthermore, we relate popular existing methods in domain adaptation, source-free domain adaptation and contrastive learning via the perspective of discriminability and diversity. The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method. |
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Virtual; November 2022 |
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LAMP; 600.147 |
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Admin @ si @ YWW2022a |
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3792 |
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Dipam Goswami; Yuyang Liu ; Bartlomiej Twardowski; Joost Van de Weijer |
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FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning |
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2023 |
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37th Annual Conference on Neural Information Processing Systems |
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New Orleans; USA; December 2023 |
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Admin @ si @ GLT2023 |
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3934 |
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Kai Wang; Fei Yang; Shiqi Yang; Muhammad Atif Butt; Joost Van de Weijer |
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Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing |
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2023 |
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37th Annual Conference on Neural Information Processing Systems |
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New Orleans; USA; December 2023 |
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Admin @ si @ WYY2023 |
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3935 |
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ChuanMing Fang; Kai Wang; Joost Van de Weijer |
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IterInv: Iterative Inversion for Pixel-Level T2I Models |
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2023 |
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37th Annual Conference on Neural Information Processing Systems |
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Large-scale text-to-image diffusion models have been a ground-breaking development in generating convincing images following an input text prompt. The goal of image editing research is to give users control over the generated images by modifying the text prompt. Current image editing techniques are relying on DDIM inversion as a common practice based on the Latent Diffusion Models (LDM). However, the large pretrained T2I models working on the latent space as LDM suffer from losing details due to the first compression stage with an autoencoder mechanism. Instead, another mainstream T2I pipeline working on the pixel level, such as Imagen and DeepFloyd-IF, avoids this problem. They are commonly composed of several stages, normally with a text-to-image stage followed by several super-resolution stages. In this case, the DDIM inversion is unable to find the initial noise to generate the original image given that the super-resolution diffusion models are not compatible with the DDIM technique. According to our experimental findings, iteratively concatenating the noisy image as the condition is the root of this problem. Based on this observation, we develop an iterative inversion (IterInv) technique for this stream of T2I models and verify IterInv with the open-source DeepFloyd-IF model. By combining our method IterInv with a popular image editing method, we prove the application prospects of IterInv. The code will be released at \url{this https URL}. |
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New Orleans; USA; December 2023 |
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Admin @ si @ FWW2023 |
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3936 |
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Diego Porres |
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Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks |
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2021 |
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Machine Learning for Creativity and Design, Neurips Workshop |
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Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and stabilizing training so that outputs of the Generator network have the highest possible fidelity, but little has gone into using the Discriminator after training is complete. In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. We name this method Discriminator Dreaming, and the full code can be found at this https URL. |
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Virtual; December 2021 |
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ADAS; 601.365 |
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Admin @ si @ Por2021 |
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3597 |
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Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell |
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Portmanteau Vocabularies for Multi-Cue Image Representation |
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2011 |
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25th Annual Conference on Neural Information Processing Systems |
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We describe a novel technique for feature combination in the bag-of-words model of image classification. Our approach builds discriminative compound words from primitive cues learned independently from training images. Our main observation is that modeling joint-cue distributions independently is more statistically robust for typical classification problems than attempting to empirically estimate the dependent, joint-cue distribution directly. We use Information theoretic vocabulary compression to find discriminative combinations of cues and the resulting vocabulary of portmanteau words is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. State-of-the-art results on both the Oxford Flower-102 and Caltech-UCSD Bird-200 datasets demonstrate the effectiveness of our technique compared to other, significantly more complex approaches to multi-cue image representation |
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CIC |
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Admin @ si @ KWB2011 |
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