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Andres Mafla, Sounak Dey, Ali Furkan Biten, Lluis Gomez, & Dimosthenis Karatzas. (2021). Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval. In IEEE Winter Conference on Applications of Computer Vision (pp. 4022–4032).
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Andres Mafla, Rafael S. Rezende, Lluis Gomez, Diana Larlus, & Dimosthenis Karatzas. (2021). StacMR: Scene-Text Aware Cross-Modal Retrieval. In IEEE Winter Conference on Applications of Computer Vision (pp. 2219–2229).
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Minesh Mathew, Dimosthenis Karatzas, & C.V. Jawahar. (2021). DocVQA: A Dataset for VQA on Document Images. In IEEE Winter Conference on Applications of Computer Vision (pp. 2200–2209).
Abstract: We present a new dataset for Visual Question Answering (VQA) on document images called DocVQA. The dataset consists of 50,000 questions defined on 12,000+ document images. Detailed analysis of the dataset in comparison with similar datasets for VQA and reading comprehension is presented. We report several baseline results by adopting existing VQA and reading comprehension models. Although the existing models perform reasonably well on certain types of questions, there is large performance gap compared to human performance (94.36% accuracy). The models need to improve specifically on questions where understanding structure of the document is crucial. The dataset, code and leaderboard are available at docvqa. org
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Cristina Palmero, Javier Selva, Sorina Smeureanu, Julio C. S. Jacques Junior, Albert Clapes, Alexa Mosegui, et al. (2021). Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset. In IEEE Winter Conference on Applications of Computer Vision (pp. 1–12).
Abstract: This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessions, recorded using multiple audiovisual and physiological sensors. Currently, it includes sociodemographic, self- and peer-reported personality, internal state, and relationship profiling from participants. As an initial analysis on UDIVA, we propose a
transformer-based method for self-reported personality inference in dyadic scenarios, which uses audiovisual data and different sources of context from both interlocutors to
regress a target person’s personality traits. Preliminary results from an incremental study show consistent improvements when using all available context information.
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Julio C. S. Jacques Junior, Agata Lapedriza, Cristina Palmero, Xavier Baro, & Sergio Escalera. (2021). Person Perception Biases Exposed: Revisiting the First Impressions Dataset. In IEEE Winter Conference on Applications of Computer Vision (pp. 13–21).
Abstract: This work revisits the ChaLearn First Impressions database, annotated for personality perception using pairwise comparisons via crowdsourcing. We analyse for the first time the original pairwise annotations, and reveal existing person perception biases associated to perceived attributes like gender, ethnicity, age and face attractiveness.
We show how person perception bias can influence data labelling of a subjective task, which has received little attention from the computer vision and machine learning communities by now. We further show that the mechanism used to convert pairwise annotations to continuous values may magnify the biases if no special treatment is considered. The findings of this study are relevant for the computer vision community that is still creating new datasets on subjective tasks, and using them for practical applications, ignoring these perceptual biases.
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Armin Mehri, Parichehr Behjati Ardakani, & Angel Sappa. (2021). MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. In IEEE Winter Conference on Applications of Computer Vision (pp. 2703–2712).
Abstract: Lightweight super resolution networks have extremely importance for real-world applications. In recent years several SR deep learning approaches with outstanding achievement have been introduced by sacrificing memory and computational cost. To overcome this problem, a novel lightweight super resolution network is proposed, which improves the SOTA performance in lightweight SR and performs roughly similar to computationally expensive networks. Multi-Path Residual Network designs with a set of Residual concatenation Blocks stacked with Adaptive Residual Blocks: ($i$) to adaptively extract informative features and learn more expressive spatial context information; ($ii$) to better leverage multi-level representations before up-sampling stage; and ($iii$) to allow an efficient information and gradient flow within the network. The proposed architecture also contains a new attention mechanism, Two-Fold Attention Module, to maximize the representation ability of the model. Extensive experiments show the superiority of our model against other SOTA SR approaches.
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Ajian Liu, Zichang Tan, Jun Wan, Sergio Escalera, Guodong Guo, & Stan Z. Li. (2021). CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-Ethnicity Face Anti-Spoofing. In IEEE Winter Conference on Applications of Computer Vision (pp. 1178–1186).
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.
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Idoia Ruiz, Lorenzo Porzi, Samuel Rota Bulo, Peter Kontschieder, & Joan Serrat. (2021). Weakly Supervised Multi-Object Tracking and Segmentation. In IEEE Winter Conference on Applications of Computer Vision Workshops (pp. 125–133).
Abstract: We introduce the problem of weakly supervised MultiObject Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation.
To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by
Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the
objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7 % for cars and pedestrians, respectively.
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