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Author |
Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching |
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2022 |
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Winter Conference on Applications of Computer Vision |
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1391-1400 |
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Measurement; Training; Integrated circuits; Annotations; Semantics; Training data; Semisupervised learning |
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Abstract |
The task of image-text matching aims to map representations from different modalities into a common joint visual-textual embedding. However, the most widely used datasets for this task, MSCOCO and Flickr30K, are actually image captioning datasets that offer a very limited set of relationships between images and sentences in their ground-truth annotations. This limited ground truth information forces us to use evaluation metrics based on binary relevance: given a sentence query we consider only one image as relevant. However, many other relevant images or captions may be present in the dataset. In this work, we propose two metrics that evaluate the degree of semantic relevance of retrieved items, independently of their annotated binary relevance. Additionally, we incorporate a novel strategy that uses an image captioning metric, CIDEr, to define a Semantic Adaptive Margin (SAM) to be optimized in a standard triplet loss. By incorporating our formulation to existing models, a large improvement is obtained in scenarios where available training data is limited. We also demonstrate that the performance on the annotated image-caption pairs is maintained while improving on other non-annotated relevant items when employing the full training set. The code for our new metric can be found at github. com/furkanbiten/ncsmetric and the model implementation at github. com/andrespmd/semanticadaptive_margin. |
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Virtual; Waikoloa; Hawai; USA; January 2022 |
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WACV |
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DAG; 600.155; 302.105; |
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Admin @ si @ BMG2022 |
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3663 |
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Javad Zolfaghari Bengar; Joost Van de Weijer; Laura Lopez-Fuentes; Bogdan Raducanu |
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Title |
Class-Balanced Active Learning for Image Classification |
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Conference Article |
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2022 |
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Winter Conference on Applications of Computer Vision |
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Active learning aims to reduce the labeling effort that is required to train algorithms by learning an acquisition function selecting the most relevant data for which a label should be requested from a large unlabeled data pool. Active learning is generally studied on balanced datasets where an equal amount of images per class is available. However, real-world datasets suffer from severe imbalanced classes, the so called long-tail distribution. We argue that this further complicates the active learning process, since the imbalanced data pool can result in suboptimal classifiers. To address this problem in the context of active learning, we proposed a general optimization framework that explicitly takes class-balancing into account. Results on three datasets showed that the method is general (it can be combined with most existing active learning algorithms) and can be effectively applied to boost the performance of both informative and representative-based active learning methods. In addition, we showed that also on balanced datasets
our method 1 generally results in a performance gain. |
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Virtual; Waikoloa; Hawai; USA; January 2022 |
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WACV |
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LAMP; 602.200; 600.147; 600.120 |
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Admin @ si @ ZWL2022 |
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3703 |
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Alloy Das; Sanket Biswas; Ayan Banerjee; Josep Llados; Umapada Pal; Saumik Bhattacharya |
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Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance |
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2024 |
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Winter Conference on Applications of Computer Vision |
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718-728 |
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The adaptation capability to a wide range of domains is crucial for scene text spotting models when deployed to real-world conditions. However, existing state-of-the-art (SOTA) approaches usually incorporate scene text detection and recognition simply by pretraining on natural scene text datasets, which do not directly exploit the intermediate feature representations between multiple domains. Here, we investigate the problem of domain-adaptive scene text spotting, i.e., training a model on multi-domain source data such that it can directly adapt to target domains rather than being specialized for a specific domain or scenario. Further, we investigate a transformer baseline called Swin-TESTR to focus on solving scene-text spotting for both regular and arbitrary-shaped scene text along with an exhaustive evaluation. The results clearly demonstrate the potential of intermediate representations to achieve significant performance on text spotting benchmarks across multiple domains (e.g. language, synth-to-real, and documents). both in terms of accuracy and efficiency. |
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Waikoloa; Hawai; USA; January 2024 |
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WACV |
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DAG |
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Admin @ si @ DBB2024 |
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3986 |
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Author |
Alex Gomez-Villa; Bartlomiej Twardowski; Kai Wang; Joost van de Weijer |
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Title |
Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning |
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2024 |
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Winter Conference on Applications of Computer Vision |
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1690-1700 |
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Continuous unsupervised representation learning (CURL) research has greatly benefited from improvements in self-supervised learning (SSL) techniques. As a result, existing CURL methods using SSL can learn high-quality representations without any labels, but with a notable performance drop when learning on a many-tasks data stream. We hypothesize that this is caused by the regularization losses that are imposed to prevent forgetting, leading to a suboptimal plasticity-stability trade-off: they either do not adapt fully to the incoming data (low plasticity), or incur significant forgetting when allowed to fully adapt to a new SSL pretext-task (low stability). In this work, we propose to train an expert network that is relieved of the duty of keeping the previous knowledge and can focus on performing optimally on the new tasks (optimizing plasticity). In the second phase, we combine this new knowledge with the previous network in an adaptation-retrospection phase to avoid forgetting and initialize a new expert with the knowledge of the old network. We perform several experiments showing that our proposed approach outperforms other CURL exemplar-free methods in few- and many-task split settings. Furthermore, we show how to adapt our approach to semi-supervised continual learning (Semi-SCL) and show that we surpass the accuracy of other exemplar-free Semi-SCL methods and reach the results of some others that use exemplars. |
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Waikoloa; Hawai; USA; January 2024 |
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WACV |
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LAMP |
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no |
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Admin @ si @ GTW2024 |
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3989 |
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Author |
Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol |
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Title |
STEP – Towards Structured Scene-Text Spotting |
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Conference Article |
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2024 |
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Winter Conference on Applications of Computer Vision |
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883-892 |
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We introduce the structured scene-text spotting task, which requires a scene-text OCR system to spot text in the wild according to a query regular expression. Contrary to generic scene text OCR, structured scene-text spotting seeks to dynamically condition both scene text detection and recognition on user-provided regular expressions. To tackle this task, we propose the Structured TExt sPotter (STEP), a model that exploits the provided text structure to guide the OCR process. STEP is able to deal with regular expressions that contain spaces and it is not bound to detection at the word-level granularity. Our approach enables accurate zero-shot structured text spotting in a wide variety of real-world reading scenarios and is solely trained on publicly available data. To demonstrate the effectiveness of our approach, we introduce a new challenging test dataset that contains several types of out-of-vocabulary structured text, reflecting important reading applications of fields such as prices, dates, serial numbers, license plates etc. We demonstrate that STEP can provide specialised OCR performance on demand in all tested scenarios. |
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Waikoloa; Hawai; USA; January 2024 |
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WACV |
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DAG |
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no |
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Admin @ si @ GKR2024 |
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3992 |
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Author |
Hunor Laczko; Meysam Madadi; Sergio Escalera; Jordi Gonzalez |
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Title |
A Generative Multi-Resolution Pyramid and Normal-Conditioning 3D Cloth Draping |
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Conference Article |
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2024 |
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Winter Conference on Applications of Computer Vision |
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8709-8718 |
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RGB cloth generation has been deeply studied in the related literature, however, 3D garment generation remains an open problem. In this paper, we build a conditional variational autoencoder for 3D garment generation and draping. We propose a pyramid network to add garment details progressively in a canonical space, i.e. unposing and unshaping the garments w.r.t. the body. We study conditioning the network on surface normal UV maps, as an intermediate representation, which is an easier problem to optimize than 3D coordinates. Our results on two public datasets, CLOTH3D and CAPE, show that our model is robust, controllable in terms of detail generation by the use of multi-resolution pyramids, and achieves state-of-the-art results that can highly generalize to unseen garments, poses, and shapes even when training with small amounts of data. |
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Waikoloa; Hawai; USA; January 2024 |
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ISE; HUPBA |
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Admin @ si @ LME2024 |
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3996 |
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Author |
Antonio Lopez |
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Title |
Pedestrian Detection Systems |
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2018 |
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Wiley Encyclopedia of Electrical and Electronics Engineering |
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Pedestrian detection is a highly relevant topic for both advanced driver assistance systems (ADAS) and autonomous driving. In this entry, we review the ideas behind pedestrian detection systems from the point of view of perception based on computer vision and machine learning. |
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ADAS; 600.118 |
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Admin @ si @ Lop2018 |
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3230 |
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Joan Serrat; J. Argemi; Juan J. Villanueva |
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Title |
Automatization of TW2 method using a knowledge-based image analysis system. |
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1991 |
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VIth International Congress of Auxology. |
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Madrid |
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ADAS @ adas @ SAV1991 |
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Lluis Gomez; Anguelos Nicolaou; Marçal Rusiñol; Dimosthenis Karatzas |
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12 years of ICDAR Robust Reading Competitions: The evolution of reading systems for unconstrained text understanding |
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2020 |
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Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis |
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K. Alahari; C.V. Jawahar |
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Series on Advances in Computer Vision and Pattern Recognition |
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DAG; 600.121 |
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GNR2020 |
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3494 |
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Lluis Gomez; Dena Bazazian; Dimosthenis Karatzas |
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Title |
Historical review of scene text detection research |
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2020 |
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Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis |
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DAG; 600.121 |
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Admin @ si @ GBK2020 |
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3495 |
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