| Records |
| Author |
Ajian Liu; Zichang Tan; Jun Wan; Sergio Escalera; Guodong Guo; Stan Z. Li |
| 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 |
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| Volume |
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Issue |
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Pages  |
1178-1186 |
| Keywords |
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| 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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Conference |
WACV |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ LTW2021 |
Serial |
3661 |
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| Author |
Javad Zolfaghari Bengar; Joost Van de Weijer; Bartlomiej Twardowski; Bogdan Raducanu |
| Title |
Reducing Label Effort: Self- Supervised Meets Active Learning |
Type |
Conference Article |
| Year |
2021 |
Publication |
International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1631-1639 |
| Keywords |
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| Abstract |
Active learning is a paradigm aimed at reducing the annotation effort by training the model on actively selected informative and/or representative samples. Another paradigm to reduce the annotation effort is self-training that learns from a large amount of unlabeled data in an unsupervised way and fine-tunes on few labeled samples. Recent developments in self-training have achieved very impressive results rivaling supervised learning on some datasets. The current work focuses on whether the two paradigms can benefit from each other. We studied object recognition datasets including CIFAR10, CIFAR100 and Tiny ImageNet with several labeling budgets for the evaluations. Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high. The performance gap between active learning trained either with self-training or from scratch diminishes as we approach to the point where almost half of the dataset is labeled. |
| Address |
October 2021 |
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ICCVW |
| Notes |
LAMP; |
Approved |
no |
| Call Number |
Admin @ si @ ZVT2021 |
Serial |
3672 |
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| Author |
Sudeep Katakol; Luis Herranz; Fei Yang; Marta Mrak |
| Title |
DANICE: Domain adaptation without forgetting in neural image compression |
Type |
Conference Article |
| Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1921-1925 |
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| Abstract |
Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC. |
| Address |
Virtual; June 2021 |
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CVPRW |
| Notes |
LAMP; 600.120; 600.141; 601.379 |
Approved |
no |
| Call Number |
Admin @ si @ KHY2021 |
Serial |
3568 |
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| Author |
Shun Yao; Fei Yang; Yongmei Cheng; Mikhail Mozerov |
| Title |
3D Shapes Local Geometry Codes Learning with SDF |
Type |
Conference Article |
| Year |
2021 |
Publication |
International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2110-2117 |
| Keywords |
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| Abstract |
A signed distance function (SDF) as the 3D shape description is one of the most effective approaches to represent 3D geometry for rendering and reconstruction. Our work is inspired by the state-of-the-art method DeepSDF [17] that learns and analyzes the 3D shape as the iso-surface of its shell and this method has shown promising results especially in the 3D shape reconstruction and compression domain. In this paper, we consider the degeneration problem of reconstruction coming from the capacity decrease of the DeepSDF model, which approximates the SDF with a neural network and a single latent code. We propose Local Geometry Code Learning (LGCL), a model that improves the original DeepSDF results by learning from a local shape geometry of the full 3D shape. We add an extra graph neural network to split the single transmittable latent code into a set of local latent codes distributed on the 3D shape. Mentioned latent codes are used to approximate the SDF in their local regions, which will alleviate the complexity of the approximation compared to the original DeepSDF. Furthermore, we introduce a new geometric loss function to facilitate the training of these local latent codes. Note that other local shape adjusting methods use the 3D voxel representation, which in turn is a problem highly difficult to solve or even is insolvable. In contrast, our architecture is based on graph processing implicitly and performs the learning regression process directly in the latent code space, thus make the proposed architecture more flexible and also simple for realization. Our experiments on 3D shape reconstruction demonstrate that our LGCL method can keep more details with a significantly smaller size of the SDF decoder and outperforms considerably the original DeepSDF method under the most important quantitative metrics. |
| Address |
VIRTUAL; October 2021 |
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ICCVW |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ YYC2021 |
Serial |
3681 |
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| Author |
Neelu Madan; Arya Farkhondeh; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund |
| Title |
Temporal Cues From Socially Unacceptable Trajectories for Anomaly Detection |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE/CVF International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2150-2158 |
| Keywords |
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| Abstract |
State-of-the-Art (SoTA) deep learning-based approaches to detect anomalies in surveillance videos utilize limited temporal information, including basic information from motion, e.g., optical flow computed between consecutive frames. In this paper, we compliment the SoTA methods by including long-range dependencies from trajectories for anomaly detection. To achieve that, we first created trajectories by running a tracker on two SoTA datasets, namely Avenue and Shanghai-Tech. We propose a prediction-based anomaly detection method using trajectories based on Social GANs, also called in this paper as temporal-based anomaly detection. Then, we hypothesize that late fusion of the result of this temporal-based anomaly detection system with spatial-based anomaly detection systems produces SoTA results. We verify this hypothesis on two spatial-based anomaly detection systems. We show that both cases produce results better than baseline spatial-based systems, indicating the usefulness of the temporal information coming from the trajectories for anomaly detection. We observe that the proposed approach depicts the maximum improvement in micro-level Area-Under-the-Curve (AUC) by 4.1% on CUHK Avenue and 3.4% on Shanghai-Tech over one of the baseline method. We also show a high performance on cross-data evaluation, where we learn the weights to combine spatial and temporal information on Shanghai-Tech and perform evaluation on CUHK Avenue and vice-versa. |
| Address |
Virtual; October 2021 |
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Conference |
ICCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MFN2021 |
Serial |
3649 |
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| Author |
Claudia Greco; Carmela Buono; Pau Buch-Cardona; Gennaro Cordasco; Sergio Escalera; Anna Esposito; Anais Fernandez; Daria Kyslitska; Maria Stylianou Kornes; Cristina Palmero; Jofre Tenorio Laranga; Anna Torp Johansen; Maria Ines Torres |
| Title |
Emotional Features of Interactions With Empathic Agents |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE/CVF International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2168-2176 |
| Keywords |
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| Abstract |
The current study is part of the EMPATHIC project, whose aim is to develop an Empathic Virtual Coach (VC) capable of promoting healthy and independent aging. To this end, the VC needs to be capable of perceiving the emotional states of users and adjusting its behaviour during the interactions according to what the users are experiencing in terms of emotions and comfort. Thus, the present work focuses on some sessions where elderly users of three different countries interact with a simulated system. Audio and video information extracted from these sessions were examined by external observers to assess participants' emotional experience with the EMPATHIC-VC in terms of categorical and dimensional assessment of emotions. Analyses were conducted on the emotional labels assigned by the external observers while participants were engaged in two different scenarios: a generic one, where the interaction was carried out with no intention to discuss a specific topic, and a nutrition one, aimed to accomplish a conversation on users' nutritional habits. Results of analyses performed on both audio and video data revealed that the EMPATHIC coach did not elicit negative feelings in the users. Indeed, users from all countries have shown relaxed and positive behavior when interacting with the simulated VC during both scenarios. Overall, the EMPATHIC-VC was capable to offer an enjoyable experience without eliciting negative feelings in the users. This supports the hypothesis that an Empathic Virtual Coach capable of considering users' expectations and emotional states could support elderly people in daily life activities and help them to remain independent. |
| Address |
VIRTUAL; October 2021 |
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Conference |
ICCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ GBB2021 |
Serial |
3647 |
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| Author |
Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde |
| Title |
Multi-scale decomposition-based CT-MR neurological image fusion using optimized bio-inspired spiking neural model with meta-heuristic optimization |
Type |
Journal Article |
| Year |
2021 |
Publication |
International Journal of Imaging Systems and Technology |
Abbreviated Journal |
IMA |
| Volume |
31 |
Issue |
4 |
Pages |
2170-2188 |
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| Abstract |
Multi-modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography-magnetic resonance (CT-MR) image fusion model is proposed using an optimized bio-inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low-frequency) and detail (high-frequency) layer components. Low-frequency subbands are fused using texture energy measures to capture the local energy, contrast, and small edges in the fused image. High-frequency coefficients are fused using firing maps obtained by pixel-activated neural model with the optimized parameters using three different optimization techniques such as differential evolution, cuckoo search, and gray wolf optimization, individually. In the optimization model, a fitness function is computed based on the edge index of resultant fused images, which helps to extract and preserve sharp edges available in the source CT and MR images. To validate the fusion performance, a detailed comparative analysis is presented among the proposed and state-of-the-art methods in terms of quantitative and qualitative measures along with computational complexity. Experimental results show that the proposed method produces a significantly better visual quality of fused images meanwhile outperforms the existing methods. |
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| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ DGR2021a |
Serial |
3630 |
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| Author |
David Curto; Albert Clapes; Javier Selva; Sorina Smeureanu; Julio C. S. Jacques Junior; David Gallardo-Pujol; Georgina Guilera; David Leiva; Thomas B. Moeslund; Sergio Escalera; Cristina Palmero |
| Title |
Dyadformer: A Multi-Modal Transformer for Long-Range Modeling of Dyadic Interactions |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE/CVF International Conference on Computer Vision Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2177-2188 |
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| Abstract |
Personality computing has become an emerging topic in computer vision, due to the wide range of applications it can be used for. However, most works on the topic have focused on analyzing the individual, even when applied to interaction scenarios, and for short periods of time. To address these limitations, we present the Dyadformer, a novel multi-modal multi-subject Transformer architecture to model individual and interpersonal features in dyadic interactions using variable time windows, thus allowing the capture of long-term interdependencies. Our proposed cross-subject layer allows the network to explicitly model interactions among subjects through attentional operations. This proof-of-concept approach shows how multi-modality and joint modeling of both interactants for longer periods of time helps to predict individual attributes. With Dyadformer, we improve state-of-the-art self-reported personality inference results on individual subjects on the UDIVA v0.5 dataset. |
| Address |
Virtual; October 2021 |
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ICCVW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ CCS2021 |
Serial |
3648 |
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| Author |
Minesh Mathew; Dimosthenis Karatzas; C.V. Jawahar |
| Title |
DocVQA: A Dataset for VQA on Document Images |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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Issue |
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Pages |
2200-2209 |
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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 |
| Address |
Virtual; January 2021 |
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WACV |
| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ MKJ2021 |
Serial |
3498 |
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| Author |
Andres Mafla; Rafael S. Rezende; Lluis Gomez; Diana Larlus; Dimosthenis Karatzas |
| Title |
StacMR: Scene-Text Aware Cross-Modal Retrieval |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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Issue |
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Pages |
2219-2229 |
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| Address |
Virtual; January 2021 |
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WACV |
| Notes |
DAG; 600.121 |
Approved |
no |
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Admin @ si @ MRG2021a |
Serial |
3492 |
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| Author |
Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan C. Moure |
| Title |
3D Perception With Slanted Stixels on GPU |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Transactions on Parallel and Distributed Systems |
Abbreviated Journal |
TPDS |
| Volume |
32 |
Issue |
10 |
Pages |
2434-2447 |
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Daniel Hernandez-Juarez; Antonio Espinosa; David Vazquez; Antonio M. Lopez; Juan C. Moure |
| Abstract |
This article presents a GPU-accelerated software design of the recently proposed model of Slanted Stixels, which represents the geometric and semantic information of a scene in a compact and accurate way. We reformulate the measurement depth model to reduce the computational complexity of the algorithm, relying on the confidence of the depth estimation and the identification of invalid values to handle outliers. The proposed massively parallel scheme and data layout for the irregular computation pattern that corresponds to a Dynamic Programming paradigm is described and carefully analyzed in performance terms. Performance is shown to scale gracefully on current generation embedded GPUs. We assess the proposed methods in terms of semantic and geometric accuracy as well as run-time performance on three publicly available benchmark datasets. Our approach achieves real-time performance with high accuracy for 2048 × 1024 image sizes and 4 × 4 Stixel resolution on the low-power embedded GPU of an NVIDIA Tegra Xavier. |
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| Notes |
ADAS; 600.124; 600.118 |
Approved |
no |
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Admin @ si @ HEV2021 |
Serial |
3561 |
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| Author |
Meysam Madadi; Hugo Bertiche; Sergio Escalera |
| Title |
Deep unsupervised 3D human body reconstruction from a sparse set of landmarks |
Type |
Journal Article |
| Year |
2021 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
| Volume |
129 |
Issue |
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Pages |
2499–2512 |
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| Abstract |
In this paper we propose the first deep unsupervised approach in human body reconstruction to estimate body surface from a sparse set of landmarks, so called DeepMurf. We apply a denoising autoencoder to estimate missing landmarks. Then we apply an attention model to estimate body joints from landmarks. Finally, a cascading network is applied to regress parameters of a statistical generative model that reconstructs body. Our set of proposed loss functions allows us to train the network in an unsupervised way. Results on four public datasets show that our approach accurately reconstructs the human body from real world mocap data. |
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HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MBE2021 |
Serial |
3654 |
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| Author |
Parichehr Behjati Ardakani; Pau Rodriguez; Armin Mehri; Isabelle Hupont; Carles Fernandez; Jordi Gonzalez |
| Title |
OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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Issue |
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Pages |
2693-2702 |
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| Abstract |
Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs). However, as the depth and width of the networks increase, CNN-based SR methods have been faced with the challenge of computational complexity in practice. More- over, most SR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. To address these limitations we introduce OverNet, a deep but lightweight convolutional network to solve SISR at arbitrary scale factors with a single model. We make the following contributions: first, we introduce a lightweight feature extractor that enforces efficient reuse of information through a novel recursive structure of skip and dense connections. Second, to maximize the performance of the feature extractor, we propose a model agnostic reconstruction module that generates accurate high-resolution images from overscaled feature maps obtained from any SR architecture. Third, we introduce a multi-scale loss function to achieve generalization across scales. Experiments show that our proposal outperforms previous state-of-the-art approaches in standard benchmarks, while maintaining relatively low computation and memory requirements. |
| Address |
Virtual; January 2021 |
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WACV |
| Notes |
ISE; 600.119; 600.098 |
Approved |
no |
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Admin @ si @ BRM2021 |
Serial |
3512 |
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| Author |
Armin Mehri; Parichehr Behjati Ardakani; Angel Sappa |
| Title |
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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2703-2712 |
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| 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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Virtual; January 2021 |
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MSIAU; 600.130; 600.122 |
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Admin @ si @ MAS2021b |
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3582 |
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Dorota Kaminska; Kadir Aktas; Davit Rizhinashvili; Danila Kuklyanov; Abdallah Hussein Sham; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Gholamreza Anbarjafari |
| Title |
Two-stage Recognition and Beyond for Compound Facial Emotion Recognition |
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Journal Article |
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2021 |
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Electronics |
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ELEC |
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10 |
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22 |
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2847 |
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compound emotion recognition; facial expression recognition; dominant and complementary emotion recognition; deep learning |
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Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels. |
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HUPBA; no proj |
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no |
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Admin @ si @ KAR2021 |
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3642 |
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