| Records |
| Author |
Mohammed Al Rawi; Ernest Valveny; Dimosthenis Karatzas |
| Title |
Can One Deep Learning Model Learn Script-Independent Multilingual Word-Spotting? |
Type |
Conference Article |
| Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
260-267 |
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| Abstract |
Word spotting has gained increased attention lately as it can be used to extract textual information from handwritten documents and scene-text images. Current word spotting approaches are designed to work on a single language and/or script. Building intelligent models that learn script-independent multilingual word-spotting is challenging due to the large variability of multilingual alphabets and symbols. We used ResNet-152 and the Pyramidal Histogram of Characters (PHOC) embedding to build a one-model script-independent multilingual word-spotting and we tested it on Latin, Arabic, and Bangla (Indian) languages. The one-model we propose performs on par with the multi-model language-specific word-spotting system, and thus, reduces the number of models needed for each script and/or language. |
| Address |
Sydney; Australia; September 2019 |
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ICDAR |
| Notes |
DAG; 600.129; 600.121 |
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no |
| Call Number |
Admin @ si @ RVK2019 |
Serial |
3337 |
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| Author |
Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guçlu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia Liem; Marcel A. J. Van Gerven; Rob Van Lier |
| Title |
Modeling, Recognizing, and Explaining Apparent Personality from Videos |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Affective Computing |
Abbreviated Journal |
TAC |
| Volume |
13 |
Issue |
2 |
Pages |
894-911 |
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| Abstract |
Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future. |
| Address |
1 April-June 2022 |
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| Notes |
HuPBA; no menciona |
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no |
| Call Number |
Admin @ si @ EKS2022 |
Serial |
3406 |
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| Author |
Esmitt Ramirez; Carles Sanchez; Debora Gil |
| Title |
Localizing Pulmonary Lesions Using Fuzzy Deep Learning |
Type |
Conference Article |
| Year |
2019 |
Publication |
21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
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Pages |
290-294 |
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| Abstract |
The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, Computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e.g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for Computer-aided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions. |
| Address |
Timisoara; Rumania; September 2019 |
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SYNASC |
| Notes |
IAM; 600.145; 600.140; 601.337; 601.323 |
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no |
| Call Number |
Admin @ si @ RSG2019 |
Serial |
3531 |
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| Author |
Edgar Riba; D. Mishkin; Daniel Ponsa; E. Rublee; G. Bradski |
| Title |
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch |
Type |
Conference Article |
| Year |
2020 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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| Address |
Aspen; Colorado; USA; March 2020 |
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WACV |
| Notes |
MSIAU; 600.122; 600.130 |
Approved |
no |
| Call Number |
Admin @ si @ RMP2020 |
Serial |
3291 |
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| Author |
Lorenzo Porzi; Markus Hofinger; Idoia Ruiz; Joan Serrat; Samuel Rota Bulo; Peter Kontschieder |
| Title |
Learning Multi-Object Tracking and Segmentation from Automatic Annotations |
Type |
Conference Article |
| Year |
2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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Issue |
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Pages |
6845-6854 |
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In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns raw street-level videos into high-fidelity MOTS training data, is scalable and overcomes the need of expensive and time-consuming manual annotation approaches. We leverage state-of-the-art instance segmentation results in combination with optical flow predictions, also trained on automatically harvested training data. Our second major contribution is MOTSNet – a deep learning, tracking-by-detection architecture for MOTS – deploying a novel mask-pooling layer for improved object association over time. Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1.9%/+7.5% on cars/pedestrians), and MOTSNet improves by +4.1% over previously best methods on the MOTSChallenge dataset. Our most impressive finding is that we can improve over previous best-performing works, even in complete absence of manually annotated MOTS training data. |
| Address |
virtual; June 2020 |
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Conference |
CVPR |
| Notes |
ADAS; 600.124; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ PHR2020 |
Serial |
3402 |
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| Author |
Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Esmitt Ramirez; Carles Sanchez |
| Title |
Intraoperative Extraction of Airways Anatomy in VideoBronchoscopy |
Type |
Journal Article |
| Year |
2020 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
8 |
Issue |
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Pages |
159696 - 159704 |
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| Abstract |
A main bottleneck in bronchoscopic biopsy sampling is to efficiently reach the lesion navigating across bronchial levels. Any guidance system should be able to localize the scope position during the intervention with minimal costs and alteration of clinical protocols. With the final goal of an affordable image-based guidance, this work presents a novel strategy to extract and codify the anatomical structure of bronchi, as well as, the scope navigation path from videobronchoscopy. Experiments using interventional data show that our method accurately identifies the bronchial structure. Meanwhile, experiments using simulated data verify that the extracted navigation path matches the 3D route. |
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| Notes |
IAM; 600.139; 600.145 |
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no |
| Call Number |
Admin @ si @ GEB2020 |
Serial |
3467 |
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| Author |
Albert Clapes; Julio C. S. Jacques Junior; Carla Morral; Sergio Escalera |
| Title |
ChaLearn LAP 2020 Challenge on Identity-preserved Human Detection: Dataset and Results |
Type |
Conference Article |
| Year |
2020 |
Publication |
15th IEEE International Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
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Issue |
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Pages |
801-808 |
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This paper summarizes the ChaLearn Looking at People 2020 Challenge on Identity-preserved Human Detection (IPHD). For the purpose, we released a large novel dataset containing more than 112K pairs of spatiotemporally aligned depth and thermal frames (and 175K instances of humans) sampled from 780 sequences. The sequences contain hundreds of non-identifiable people appearing in a mix of in-the-wild and scripted scenarios recorded in public and private places. The competition was divided into three tracks depending on the modalities exploited for the detection: (1) depth, (2) thermal, and (3) depth-thermal fusion. Color was also captured but only used to facilitate the groundtruth annotation. Still the temporal synchronization of three sensory devices is challenging, so bad temporal matches across modalities can occur. Hence, the labels provided should considered “weak”, although test frames were carefully selected to minimize this effect and ensure the fairest comparison of the participants’ results. Despite this added difficulty, the results got by the participants demonstrate current fully-supervised methods can deal with that and achieve outstanding detection performance when measured in terms of AP@0.50. |
| Address |
Virtual; November 2020 |
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FG |
| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ CJM2020 |
Serial |
3501 |
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| Author |
Zhengying Liu; Adrien Pavao; Zhen Xu; Sergio Escalera; Fabio Ferreira; Isabelle Guyon; Sirui Hong; Frank Hutter; Rongrong Ji; Julio C. S. Jacques Junior; Ge Li; Marius Lindauer; Zhipeng Luo; Meysam Madadi; Thomas Nierhoff; Kangning Niu; Chunguang Pan; Danny Stoll; Sebastien Treguer; Jin Wang; Peng Wang; Chenglin Wu; Youcheng Xiong; Arber Zela; Yang Zhang |
| Title |
Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019 |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
43 |
Issue |
9 |
Pages |
3108 - 3125 |
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| Abstract |
This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and all tasks were multi-label classification problems. Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly. In this setting, DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. Solutions relied on fine-tuned pre-trained networks, with architectures matching data modality. Post-challenge tests did not reveal improvements beyond the imposed time limit. While no component is particularly original or novel, a high level modular organization emerged featuring a “meta-learner”, “data ingestor”, “model selector”, “model/learner”, and “evaluator”. This modularity enabled ablation studies, which revealed the importance of (off-platform) meta-learning, ensembling, and efficient data management. Experiments on heterogeneous module combinations further confirm the (local) optimality of the winning solutions. Our challenge legacy includes an ever-lasting benchmark (http://autodl.chalearn.org), the open-sourced code of the winners, and a free “AutoDL self-service.” |
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HUPBA; no proj |
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no |
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Admin @ si @ LPX2021 |
Serial |
3587 |
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| Author |
Vishwesh Pillai; Pranav Mehar; Manisha Das; Deep Gupta; Petia Radeva |
| Title |
Integrated Hierarchical and Flat Classifiers for Food Image Classification using Epistemic Uncertainty |
Type |
Conference Article |
| Year |
2022 |
Publication |
IEEE International Conference on Signal Processing and Communications |
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The problem of food image recognition is an essential one in today’s context because health conditions such as diabetes, obesity, and heart disease require constant monitoring of a person’s diet. To automate this process, several models are available to recognize food images. Due to a considerable number of unique food dishes and various cuisines, a traditional flat classifier ceases to perform well. To address this issue, prediction schemes consisting of both flat and hierarchical classifiers, with the analysis of epistemic uncertainty are used to switch between the classifiers. However, the accuracy of the predictions made using epistemic uncertainty data remains considerably low. Therefore, this paper presents a prediction scheme using three different threshold criteria that helps to increase the accuracy of epistemic uncertainty predictions. The performance of the proposed method is demonstrated using several experiments performed on the MAFood-121 dataset. The experimental results validate the proposal performance and show that the proposed threshold criteria help to increase the overall accuracy of the predictions by correctly classifying the uncertainty distribution of the samples. |
| Address |
Bangalore; India; July 2022 |
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SPCOM |
| Notes |
MILAB; no menciona |
Approved |
no |
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Admin @ si @ PMD2022 |
Serial |
3796 |
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Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Federica Cruciani; Lorenza Brusini; Petia Radeva |
| Title |
Investigating Explainable Artificial Intelligence for MRI-based Classification of Dementia: a New Stability Criterion for Explainable Methods |
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Conference Article |
| Year |
2022 |
Publication |
29th IEEE International Conference on Image Processing |
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Image processing; Stability criteria; Machine learning; Robustness; Alzheimer's disease; Monitoring |
| Abstract |
Individuals diagnosed with Mild Cognitive Impairment (MCI) have shown an increased risk of developing Alzheimer’s Disease (AD). As such, early identification of dementia represents a key prognostic element, though hampered by complex disease patterns. Increasing efforts have focused on Machine Learning (ML) to build accurate classification models relying on a multitude of clinical/imaging variables. However, ML itself does not provide sensible explanations related to the model mechanism and feature contribution. Explainable Artificial Intelligence (XAI) represents the enabling technology in this framework, allowing to understand ML outcomes and derive human-understandable explanations. In this study, we aimed at exploring ML combined with MRI-based features and XAI to solve this classification problem and interpret the outcome. In particular, we propose a new method to assess the robustness of feature rankings provided by XAI methods, especially when multicollinearity exists. Our findings indicate that our method was able to disentangle the list of the informative features underlying dementia, with important implications for aiding personalized monitoring plans. |
| Address |
Bordeaux; France; October 2022 |
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ICIP |
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MILAB |
Approved |
no |
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Admin @ si @ SBC2022 |
Serial |
3789 |
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| Author |
Mickael Cormier; Andreas Specker; Julio C. S. Jacques; Lucas Florin; Jurgen Metzler; Thomas B. Moeslund; Kamal Nasrollahi; Sergio Escalera; Jurgen Beyerer |
| Title |
UPAR Challenge: Pedestrian Attribute Recognition and Attribute-based Person Retrieval – Dataset, Design, and Results |
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Conference Article |
| Year |
2023 |
Publication |
2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops |
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166-175 |
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In civilian video security monitoring, retrieving and tracking a person of interest often rely on witness testimony and their appearance description. Deployed systems rely on a large amount of annotated training data and are expected to show consistent performance in diverse areas and gen-eralize well between diverse settings w.r.t. different view-points, illumination, resolution, occlusions, and poses for indoor and outdoor scenes. However, for such generalization, the system would require a large amount of various an-notated data for training and evaluation. The WACV 2023 Pedestrian Attribute Recognition and Attributed-based Per-son Retrieval Challenge (UPAR-Challenge) aimed to spot-light the problem of domain gaps in a real-world surveil-lance context and highlight the challenges and limitations of existing methods. The UPAR dataset, composed of 40 important binary attributes over 12 attribute categories across four datasets, was extended with data captured from a low-flying UAV from the P-DESTRE dataset. To this aim, 0.6M additional annotations were manually labeled and vali-dated. Each track evaluated the robustness of the competing methods to domain shifts by training on limited data from a specific domain and evaluating using data from unseen do-mains. The challenge attracted 41 registered participants, but only one team managed to outperform the baseline on one track, emphasizing the task's difficulty. This work de-scribes the challenge design, the adopted dataset, obtained results, as well as future directions on the topic. |
| Address |
Waikoloa; Hawai; USA; January 2023 |
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WACVW |
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HUPBA |
Approved |
no |
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Admin @ si @ CSJ2023 |
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3902 |
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| Author |
Armin Mehri; Parichehr Behjati; Angel Sappa |
| Title |
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution |
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Journal Article |
| Year |
2023 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
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11 |
Issue |
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11529-11540 |
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Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications. It is important to emphasize that most state-of-the-art super resolution algorithms often use a single channel of input data for training and inference. However, this practice ignores the fact that the cost of acquiring high-resolution images in various spectral domains can differ a lot from one another. In this paper, we attempt to exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). We propose a dual stream Transformer-based super resolution approach that uses the visible image as a guide to super-resolve another spectral band image. To this end, we introduce Transformer in Transformer network for Guidance super resolution, named TnTViT-G, an efficient and effective method that extracts the features of input images via different streams and fuses them together at various stages. In addition, unlike other guidance super resolution approaches, TnTViT-G is not limited to a fixed upsample size and it can generate super-resolved images of any size. Extensive experiments on various datasets show that the proposed model outperforms other state-of-the-art super resolution approaches. TnTViT-G surpasses state-of-the-art methods by up to 0.19∼2.3dB , while it is memory efficient. |
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MSIAU |
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no |
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Admin @ si @ MBS2023 |
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3876 |
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Yifan Wang; Luka Murn; Luis Herranz; Fei Yang; Marta Mrak; Wei Zhang; Shuai Wan; Marc Gorriz Blanch |
| Title |
Efficient Super-Resolution for Compression Of Gaming Videos |
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Conference Article |
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2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
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Due to the increasing demand for game-streaming services, efficient compression of computer-generated video is more critical than ever, especially when the available bandwidth is low. This paper proposes a super-resolution framework that improves the coding efficiency of computer-generated gaming videos at low bitrates. Most state-of-the-art super-resolution networks generalize over a variety of RGB inputs and use a unified network architecture for frames of different levels of degradation, leading to high complexity and redundancy. Since games usually consist of a limited number of fixed scenarios, we specialize one model for each scenario and assign appropriate network capacities for different QPs to perform super-resolution under the guidance of reconstructed high-quality luma components. Experimental results show that our framework achieves a superior quality-complexity trade-off compared to the ESRnet baseline, saving at most 93.59% parameters while maintaining comparable performance. The compression efficiency compared to HEVC is also improved by more than 17% BD-rate gain. |
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ICASSP |
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LAMP; MACO |
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no |
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Admin @ si @ WMH2023 |
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3911 |
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Danna Xue; Luis Herranz; Javier Vazquez; Yanning Zhang |
| Title |
Burst Perception-Distortion Tradeoff: Analysis and Evaluation |
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Conference Article |
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2023 |
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IEEE International Conference on Acoustics, Speech and Signal Processing |
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Burst image restoration attempts to effectively utilize the complementary cues appearing in sequential images to produce a high-quality image. Most current methods use all the available images to obtain the reconstructed image. However, using more images for burst restoration is not always the best option regarding reconstruction quality and efficiency, as the images acquired by handheld imaging devices suffer from degradation and misalignment caused by the camera noise and shake. In this paper, we extend the perception-distortion tradeoff theory by introducing multiple-frame information. We propose the area of the unattainable region as a new metric for perception-distortion tradeoff evaluation and comparison. Based on this metric, we analyse the performance of burst restoration from the perspective of the perception-distortion tradeoff under both aligned bursts and misaligned bursts situations. Our analysis reveals the importance of inter-frame alignment for burst restoration and shows that the optimal burst length for the restoration model depends both on the degree of degradation and misalignment. |
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Rodhes Islands; Greece; June 2023 |
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CIC; MACO |
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Admin @ si @ XHV2023 |
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3909 |
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| Author |
Mingyi Yang; Luis Herranz; Fei Yang; Luka Murn; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang; Marta Mrak |
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Semantic Preprocessor for Image Compression for Machines |
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Conference Article |
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2023 |
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IEEE International Conference on Acoustics, Speech and Signal Processing |
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Visual content is being increasingly transmitted and consumed by machines rather than humans to perform automated content analysis tasks. In this paper, we propose an image preprocessor that optimizes the input image for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. To achieve a better trade-off between the accuracy of the machine analysis task and bitrate, we propose leveraging pre-extracted semantic information to improve the preprocessor’s ability to accurately identify and filter out task-irrelevant information. Furthermore, we propose a two-part loss function to optimize the preprocessor, consisted of a rate-task performance loss and a semantic distillation loss, which helps the reconstructed image obtain more information that contributes to the accuracy of the task. Experiments show that the proposed preprocessor can save up to 48.83% bitrate compared with the method without the preprocessor, and save up to 36.24% bitrate compared to existing preprocessors for machine vision. |
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Rodhes Islands; Greece; June 2023 |
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MACO; LAMP |
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Admin @ si @ YHY2023 |
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3912 |
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