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Author Joakim Bruslund Haurum; Meysam Madadi; Sergio Escalera; Thomas B. Moeslund edit   pdf
url  doi
openurl 
  Title Multi-Task Classification of Sewer Pipe Defects and Properties Using a Cross-Task Graph Neural Network Decoder Type Conference Article
  Year 2022 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 2806-2817  
  Keywords Vision Systems; Applications Multi-Task Classification  
  Abstract The sewerage infrastructure is one of the most important and expensive infrastructures in modern society. In order to efficiently manage the sewerage infrastructure, automated sewer inspection has to be utilized. However, while sewer
defect classification has been investigated for decades, little attention has been given to classifying sewer pipe properties such as water level, pipe material, and pipe shape, which are needed to evaluate the level of sewer pipe deterioration.
In this work we classify sewer pipe defects and properties concurrently and present a novel decoder-focused multi-task classification architecture Cross-Task Graph Neural Network (CT-GNN), which refines the disjointed per-task predictions using cross-task information. The CT-GNN architecture extends the traditional disjointed task-heads decoder, by utilizing a cross-task graph and unique class node embeddings. The cross-task graph can either be determined a priori based on the conditional probability between the task classes or determined dynamically using self-attention.
CT-GNN can be added to any backbone and trained end-toend at a small increase in the parameter count. We achieve state-of-the-art performance on all four classification tasks in the Sewer-ML dataset, improving defect classification and
water level classification by 5.3 and 8.0 percentage points, respectively. We also outperform the single task methods as well as other multi-task classification approaches while introducing 50 times fewer parameters than previous modelfocused approaches.		  
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  Area Expedition Conference (down) WACV  
  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ BME2022 Serial 3638  
Permanent link to this record
 
 
Author Ajian Liu; Zichang Tan; Jun Wan; Sergio Escalera; Guodong Guo; Stan Z. Li edit  url
doi  openurl
  Title CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-Ethnicity Face Anti-Spoofing Type Conference Article
  Year 2021 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 1178-1186  
  Keywords  
  Abstract The issue of ethnic bias has proven to affect the performance of face recognition in previous works, while it still remains to be vacant in face anti-spoofing. Therefore, in order to study the ethnic bias for face anti-spoofing, we introduce the largest CASIA-SURF Cross-ethnicity Face Anti-spoofing (CeFA) dataset, covering 3 ethnicities, 3 modalities, 1,607 subjects, and 2D plus 3D attack types. Five protocols are introduced to measure the affect under varied evaluation conditions, such as cross-ethnicity, unknown spoofs or both of them. As our knowledge, CASIA-SURF CeFA is the first dataset including explicit ethnic labels in current released datasets. Then, we propose a novel multi-modal fusion method as a strong baseline to alleviate the ethnic bias, which employs a partially shared fusion strategy to learn complementary information from multiple modalities. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability for other existing datasets, i.e., CASIA-SURF, OULU-NPU and SiW datasets. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2020?authuser=0.  
  Address Virtual; January 2021  
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  Area Expedition Conference (down) WACV  
  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ LTW2021 Serial 3661  
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Author Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning Type Conference Article
  Year 2022 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 1381-1390  
  Keywords Measurement; Training; Visualization; Analytical models; Computer vision; Computational modeling; Training data  
  Abstract Explaining an image with missing or non-existent objects is known as object bias (hallucination) in image captioning. This behaviour is quite common in the state-of-the-art captioning models which is not desirable by humans. To decrease the object hallucination in captioning, we propose three simple yet efficient training augmentation method for sentences which requires no new training data or increase
in the model size. By extensive analysis, we show that the proposed methods can significantly diminish our models’ object bias on hallucination metrics. Moreover, we experimentally demonstrate that our methods decrease the dependency on the visual features. All of our code, configuration files and model weights are available online.		  
  Address Virtual; Waikoloa; Hawai; USA; January 2022  
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  Notes DAG; 600.155; 302.105 Approved no  
  Call Number Admin @ si @ BGK2022 Serial 3662  
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Author Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching Type Conference Article
  Year 2022 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 1391-1400  
  Keywords Measurement; Training; Integrated circuits; Annotations; Semantics; Training data; Semisupervised learning  
  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.  
  Address Virtual; Waikoloa; Hawai; USA; January 2022  
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  Area Expedition Conference (down) WACV  
  Notes DAG; 600.155; 302.105; Approved no  
  Call Number Admin @ si @ BMG2022 Serial 3663  
Permanent link to this record
 
 
Author Javad Zolfaghari Bengar; Joost Van de Weijer; Laura Lopez-Fuentes; Bogdan Raducanu edit   pdf
url  doi
openurl 
  Title Class-Balanced Active Learning for Image Classification Type Conference Article
  Year 2022 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract 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.		  
  Address Virtual; Waikoloa; Hawai; USA; January 2022  
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  Area Expedition Conference (down) WACV  
  Notes LAMP; 602.200; 600.147; 600.120 Approved no  
  Call Number Admin @ si @ ZWL2022 Serial 3703  
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Author Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; CV Jawahar edit   pdf
url  openurl
  Title Watching the News: Towards VideoQA Models that can Read Type Conference Article
  Year 2023 Publication Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Video Question Answering methods focus on commonsense reasoning and visual cognition of objects or persons and their interactions over time. Current VideoQA approaches ignore the textual information present in the video. Instead, we argue that textual information is complementary to the action and provides essential contextualisation cues to the reasoning process. To this end, we propose a novel VideoQA task that requires reading and understanding the text in the video. To explore this direction, we focus on news videos and require QA systems to comprehend and answer questions about the topics presented by combining visual and textual cues in the video. We introduce the ``NewsVideoQA'' dataset that comprises more than 8,600 QA pairs on 3,000+ news videos obtained from diverse news channels from around the world. We demonstrate the limitations of current Scene Text VQA and VideoQA methods and propose ways to incorporate scene text information into VideoQA methods.  
  Address Waikoloa; Hawai; USA; January 2023  
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  Area Expedition Conference (down) WACV  
  Notes DAG Approved no  
  Call Number Admin @ si @ JMK2023 Serial 3899  
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Author Marcos V Conde; Florin Vasluianu; Javier Vazquez; Radu Timofte edit   pdf
url  openurl
  Title Perceptual image enhancement for smartphone real-time applications Type Conference Article
  Year 2023 Publication Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 1848-1858  
  Keywords  
  Abstract Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartphone cameras, we commonly find artifacts or degradation in the processed images. The most common unpleasant effects are noise artifacts, diffraction artifacts, blur, and HDR overexposure. Deep learning methods for image restoration can successfully remove these artifacts. However, most approaches are not suitable for real-time applications on mobile devices due to their heavy computation and memory requirements. In this paper, we propose LPIENet, a lightweight network for perceptual image enhancement, with the focus on deploying it on smartphones. Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks. Moreover, to prove the efficiency and reliability of our approach, we deployed the model directly on commercial smartphones and evaluated its performance. Our model can process 2K resolution images under 1 second in mid-level commercial smartphones.  
  Address Waikoloa; Hawai; USA; January 2023  
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  Notes MACO; CIC Approved no  
  Call Number Admin @ si @ CVV2023 Serial 3900  
Permanent link to this record
 
 
Author Dipam Goswami; J Schuster; Joost Van de Weijer; Didier Stricker edit   pdf
url  openurl
  Title Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation Type Conference Article
  Year 2023 Publication Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 3195-3204  
  Keywords  
  Abstract Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation. D Goswami, R Schuster, J van de Weijer, D Stricker. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 3195-3204  
  Address Waikoloa; Hawai; USA; January 2023  
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  Area Expedition Conference (down) WACV  
  Notes LAMP Approved no  
  Call Number Admin @ si @ GSW2023 Serial 3901  
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Author Alloy Das; Sanket Biswas; Ayan Banerjee; Josep Llados; Umapada Pal; Saumik Bhattacharya edit   pdf
url  openurl
  Title Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance Type Conference Article
  Year 2024 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 718-728  
  Keywords  
  Abstract 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.  
  Address Waikoloa; Hawai; USA; January 2024  
  Corporate Author Thesis  
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  Area Expedition Conference (down) WACV  
  Notes DAG Approved no  
  Call Number Admin @ si @ DBB2024 Serial 3986  
Permanent link to this record
 
 
Author Alex Gomez-Villa; Bartlomiej Twardowski; Kai Wang; Joost van de Weijer edit   pdf
url  openurl
  Title Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning Type Conference Article
  Year 2024 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 1690-1700  
  Keywords  
  Abstract 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.  
  Address Waikoloa; Hawai; USA; January 2024  
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  Publisher Place of Publication Editor  
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  ISSN ISBN Medium  
  Area Expedition Conference (down) WACV  
  Notes LAMP Approved no  
  Call Number Admin @ si @ GTW2024 Serial 3989  
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