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Shiqi Yang, Yaxing Wang, Kai Wang, Shangling Jui, & Joost Van de Weijer. (2022). One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift.
Abstract: In this paper, we investigate model adaptation under domain and category shift, where the final goal is to achieve
(SF-UNDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-UNDA setting, the model cannot access source data anymore during target adaptation, which aims to address data privacy concerns. We propose a novel training scheme to learn a (
+1)-way classifier to predict the
source classes and the unknown class, where samples of only known source categories are available for training. Furthermore, for target adaptation, we simply adopt a weighted entropy minimization to adapt the source pretrained model to the unlabeled target domain without source data. In experiments, we show:
After source training, the resulting source model can get excellent performance for
;
After target adaptation, our method surpasses current UNDA approaches which demand source data during adaptation. The versatility to several different tasks strongly proves the efficacy and generalization ability of our method.
When augmented with a closed-set domain adaptation approach during target adaptation, our source-free method further outperforms the current state-of-the-art UNDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA respectively.
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Silvio Giancola, Anthony Cioppa, Adrien Deliege, Floriane Magera, Vladimir Somers, Le Kang, et al. (2022). SoccerNet 2022 Challenges Results. In 5th International ACM Workshop on Multimedia Content Analysis in Sports (pp. 75–86).
Abstract: The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on this https URL. Baselines and development kits are available on this https URL.
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Smriti Joshi, Richard Osuala, Carlos Martin Isla, Victor M.Campello, Carla Sendra-Balcells, Karim Lekadir, et al. (2022). nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging. In International MICCAI Brainlesion Workshop (Vol. 12963, 540–551). LNCS.
Abstract: In recent years, deep learning models have considerably advanced the performance of segmentation tasks on Brain Magnetic Resonance Imaging (MRI). However, these models show a considerable performance drop when they are evaluated on unseen data from a different distribution. Since annotation is often a hard and costly task requiring expert supervision, it is necessary to develop ways in which existing models can be adapted to the unseen domains without any additional labelled information. In this work, we explore one such technique which extends the CycleGAN [2] architecture to generate label-preserving data in the target domain. The synthetic target domain data is used to train the nn-UNet [3] framework for the task of multi-label segmentation. The experiments are conducted and evaluated on the dataset [1] provided in the ‘Cross-Modality Domain Adaptation for Medical Image Segmentation’ challenge [23] for segmentation of vestibular schwannoma (VS) tumour and cochlea on contrast enhanced (ceT1) and high resolution (hrT2) MRI scans. In the proposed approach, our model obtains dice scores (DSC) 0.73 and 0.49 for tumour and cochlea respectively on the validation set of the dataset. This indicates the applicability of the proposed technique to real-world problems where data may be obtained by different acquisition protocols as in [1] where hrT2 images are more reliable, safer, and lower-cost alternative to ceT1.
Keywords: Domain adaptation; Vestibular schwannoma (VS); Deep learning; nn-UNet; CycleGAN
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Sonia Baeza, Debora Gil, I.Garcia Olive, M.Salcedo, J.Deportos, Carles Sanchez, et al. (2022). A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients. EJNMMI-PHYS - EJNMMI Physics, 9(1, Article 84), 1–17.
Abstract: Background: COVID-19 infection, especially in cases with pneumonia, is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic CTPA, perfusion single-photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnostic alternative. The goal of this study is to develop a radiomic diagnostic system to detect PE based only on the analysis of Q-SPECT/CT scans.
Methods: This radiomic diagnostic system is based on a local analysis of Q-SPECT/CT volumes that includes both CT and Q-SPECT values for each volume point. We present a combined approach that uses radiomic features extracted from each scan as input into a fully connected classifcation neural network that optimizes a weighted crossentropy loss trained to discriminate between three diferent types of image patterns (pixel sample level): healthy lungs (control group), PE and pneumonia. Four types of models using diferent confguration of parameters were tested.
Results: The proposed radiomic diagnostic system was trained on 20 patients (4,927 sets of samples of three types of image patterns) and validated in a group of 39 patients (4,410 sets of samples of three types of image patterns). In the training group, COVID-19 infection corresponded to 45% of the cases and 51.28% in the test group. In the test group, the best model for determining diferent types of image patterns with PE presented a sensitivity, specifcity, positive predictive value and negative predictive value of 75.1%, 98.2%, 88.9% and 95.4%, respectively. The best model for detecting
pneumonia presented a sensitivity, specifcity, positive predictive value and negative predictive value of 94.1%, 93.6%, 85.2% and 97.6%, respectively. The area under the curve (AUC) was 0.92 for PE and 0.91 for pneumonia. When the results obtained at the pixel sample level are aggregated into regions of interest, the sensitivity of the PE increases to 85%, and all metrics improve for pneumonia.
Conclusion: This radiomic diagnostic system was able to identify the diferent lung imaging patterns and is a frst step toward a comprehensive intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT.
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Spencer Low, Oliver Nina, Angel Sappa, Erik Blasch, & Nathan Inkawhich. (2022). Multi-Modal Aerial View Object Classification Challenge Results – PBVS 2022. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 350–358).
Abstract: This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal methods. The 2022 challenge uses the same UNIfied Coincident Optical and Radar for recognitioN (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two tasks, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform a Resnet-18 baseline. For SAR classification, the top team showed a 129% improvement over baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021.
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Utkarsh Porwal, Alicia Fornes, & Faisal Shafait (Eds.). (2022). Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition. 18th International Conference, ICFHR 2022 (Vol. 13639). LNCS. Springer.
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Vacit Oguz Yazici. (2022). Towards Smart Fashion: Visual Recognition of Products and Attributes (Joost Van de Weijer, & Arnau Ramisa, Eds.). Ph.D. thesis, IMPRIMA, .
Abstract: Artificial intelligence is innovating the fashion industry by proposing new applications and solutions to the problems encountered by researchers and engineers working in the industry. In this thesis, we address three of these problems. In the first part of the thesis, we tackle the problem of multi-label image classification which is very related to fashion attribute recognition. In the second part of the thesis, we address two problems that are specific to fashion. Firstly, we address the problem of main product detection which is the task of associating correct image parts (e.g. bounding boxes) with the fashion product being sold. Secondly, we address the problem of color naming for multicolored fashion items. The task of multi-label image classification consists in assigning various concepts such as objects or attributes to images. Usually, there are dependencies that can be learned between the concepts to capture label correlations (chair and table classes are more likely to co-exist than chair and giraffe).
If we treat the multi-label image classification problem as an orderless set prediction problem, we can exploit recurrent neural networks (RNN) to capture label correlations. However, RNNs are trained to predict ordered sequences of tokens, so if the order of the predicted sequence is different than the order of the ground truth sequence, there will be penalization although the predictions are correct. Therefore, in the first part of the thesis, we propose an orderless loss function which will order the labels in the ground truth sequence dynamically in a way that the minimum loss is achieved. This results in a significant improvement of RNN models on multi-label image classification over the previous methods.
However, RNNs suffer from long term dependencies when the cardinality of set grows bigger. The decoding process might stop early if the current hidden state cannot find any object and outputs the termination token. This would cause the remaining classes not to be predicted and lower recall metric. Transformers can be used to avoid the long term dependency problem exploiting their selfattention modules that process sequential data simultaneously. Consequently, we propose a novel transformer model for multi-label image classification which surpasses the state-of-the-art results by a large margin.
In the second part of thesis, we focus on two fashion-specific problems. Main product detection is the task of associating image parts with the fashion product that is being sold, generally using associated textual metadata (product title or description). Normally, in fashion e-commerces, products are represented by multiple images where a person wears the product along with other fashion items. If all the fashion items in the images are marked with bounding boxes, we can use the textual metadata to decide which item is the main product. The initial work treated each of these images independently, discarding the fact that they all belong to the same product. In this thesis, we represent the bounding boxes from all the images as nodes in a fully connected graph. This allows the algorithm to learn relations between the nodes during training and take the entire context into account for the final decision. Our algorithm results in a significant improvement of the state-ofthe-art.
Moreover, we address the problem of color naming for multicolored fashion items, which is a challenging task due to the external factors such as illumination changes or objects that act as clutter. In the context of multi-label classification, the vaguely defined lines between the classes in the color space cause ambiguity. For example, a shade of blue which is very close to green might cause the model to incorrectly predict the color blue and green at the same time. Based on this, models trained for color naming are expected to recognize the colors and their quantities in both single colored and multicolored fashion items. Therefore, in this thesis, we propose a novel architecture with an additional head that explicitly estimates the number of colors in fashion items. This removes the ambiguity problem and results in better color naming performance.
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Vacit Oguz Yazici, Joost Van de Weijer, & Longlong Yu. (2022). Visual Transformers with Primal Object Queries for Multi-Label Image Classification. In 26th International Conference on Pattern Recognition.
Abstract: Multi-label image classification is about predicting a set of class labels that can be considered as orderless sequential data. Transformers process the sequential data as a whole, therefore they are inherently good at set prediction. The first vision-based transformer model, which was proposed for the object detection task introduced the concept of object queries. Object queries are learnable positional encodings that are used by attention modules in decoder layers to decode the object classes or bounding boxes using the region of interests in an image. However, inputting the same set of object queries to different decoder layers hinders the training: it results in lower performance and delays convergence. In this paper, we propose the usage of primal object queries that are only provided at the start of the transformer decoder stack. In addition, we improve the mixup technique proposed for multi-label classification. The proposed transformer model with primal object queries improves the state-of-the-art class wise F1 metric by 2.1% and 1.8%; and speeds up the convergence by 79.0% and 38.6% on MS-COCO and NUS-WIDE datasets respectively.
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Vacit Oguz Yazici, Long Long Yu, Arnau Ramisa, Luis Herranz, & Joost Van de Weijer. (2022). Main Product Detection with Graph Networks for Fashion. MTAP - Multimedia Tools and Applications, .
Abstract: Computer vision has established a foothold in the online fashion retail industry. Main product detection is a crucial step of vision-based fashion product feed parsing pipelines, focused on identifying the bounding boxes that contain the product being sold in the gallery of images of the product page. The current state-of-the-art approach does not leverage the relations between regions in the image, and treats images of the same product independently, therefore not fully exploiting visual and product contextual information. In this paper, we propose a model that incorporates Graph Convolutional Networks (GCN) that jointly represent all detected bounding boxes in the gallery as nodes. We show that the proposed method is better than the state-of-the-art, especially, when we consider the scenario where title-input is missing at inference time and for cross-dataset evaluation, our method outperforms previous approaches by a large margin.
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Victor M. Campello, Carlos Martin-Isla, Cristian Izquierdo, Andrea Guala, Jose F. Rodriguez Palomares, David Vilades, et al. (2022). Minimising multi-centre radiomics variability through image normalisation: a pilot study. ScR - Scientific Reports, 12(1), 12532.
Abstract: Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image- and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n = 112) and hypertrophic cardiomyopathy (n = 106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features’ variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: (1) the amount of centre-related information encoded in normalised features (centre identification) and (2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of slightly degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification ( balanced accuracy in between 0.78 ± 0.08 and 0.79 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall ( balanced accuracy in between 0.45 ± 0.28 and 0.60 ± 0.22). In conclusion, centre-related information removal did not imply good generalisation ability for classification.
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