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Author |
Lu Yu; Xialei Liu; Joost Van de Weijer |
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Title |
Self-Training for Class-Incremental Semantic Segmentation |
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Journal Article |
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Year |
2022 |
Publication |
IEEE Transactions on Neural Networks and Learning Systems |
Abbreviated Journal |
TNNLS |
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Keywords |
Class-incremental learning; Self-training; Semantic segmentation. |
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In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains. The experiments demonstrate the state-of-the-art results: obtaining a relative gain of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K compared to previous state-of-the-art methods. |
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LAMP; 600.147; 611.008; |
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Admin @ si @ YLW2022 |
Serial |
3745 |
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Author |
Arnau Baro |
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Title |
Reading Music Systems: From Deep Optical Music Recognition to Contextual Methods |
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Book Whole |
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Year |
2022 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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The transcription of sheet music into some machine-readable format can be carried out manually. However, the complexity of music notation inevitably leads to burdensome software for music score editing, which makes the whole process
very time-consuming and prone to errors. Consequently, automatic transcription
systems for musical documents represent interesting tools.
Document analysis is the subject that deals with the extraction and processing
of documents through image and pattern recognition. It is a branch of computer
vision. Taking music scores as source, the field devoted to address this task is
known as Optical Music Recognition (OMR). Typically, an OMR system takes an
image of a music score and automatically extracts its content into some symbolic
structure such as MEI or MusicXML.
In this dissertation, we have investigated different methods for recognizing a
single staff section (e.g. scores for violin, flute, etc.), much in the same way as most text recognition research focuses on recognizing words appearing in a given line image. These methods are based in two different methodologies. On the one hand, we present two methods based on Recurrent Neural Networks, in particular, the
Long Short-Term Memory Neural Network. On the other hand, a method based on Sequence to Sequence models is detailed.
Music context is needed to improve the OMR results, just like language models
and dictionaries help in handwriting recognition. For example, syntactical rules
and grammars could be easily defined to cope with the ambiguities in the rhythm.
In music theory, for example, the time signature defines the amount of beats per
bar unit. Thus, in the second part of this dissertation, different methodologies
have been investigated to improve the OMR recognition. We have explored three
different methods: (a) a graphic tree-structure representation, Dendrograms, that
joins, at each level, its primitives following a set of rules, (b) the incorporation of Language Models to model the probability of a sequence of tokens, and (c) graph neural networks to analyze the music scores to avoid meaningless relationships between music primitives.
Finally, to train all these methodologies, and given the method-specificity of
the datasets in the literature, we have created four different music datasets. Two of them are synthetic with a modern or old handwritten appearance, whereas the
other two are real handwritten scores, being one of them modern and the other
old. |
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Ph.D. thesis |
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IMPRIMA |
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Alicia Fornes |
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978-84-124793-8-6 |
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DAG; |
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no |
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Call Number |
Admin @ si @ Bar2022 |
Serial |
3754 |
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Author |
Ali Furkan Biten |
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Title |
A Bitter-Sweet Symphony on Vision and Language: Bias and World Knowledge |
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Book Whole |
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Year |
2022 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Vision and Language are broadly regarded as cornerstones of intelligence. Even though language and vision have different aims – language having the purpose of communication, transmission of information and vision having the purpose of constructing mental representations around us to navigate and interact with objects – they cooperate and depend on one another in many tasks we perform effortlessly. This reliance is actively being studied in various Computer Vision tasks, e.g. image captioning, visual question answering, image-sentence retrieval, phrase grounding, just to name a few. All of these tasks share the inherent difficulty of the aligning the two modalities, while being robust to language
priors and various biases existing in the datasets. One of the ultimate goal for vision and language research is to be able to inject world knowledge while getting rid of the biases that come with the datasets. In this thesis, we mainly focus on two vision and language tasks, namely Image Captioning and Scene-Text Visual Question Answering (STVQA).
In both domains, we start by defining a new task that requires the utilization of world knowledge and in both tasks, we find that the models commonly employed are prone to biases that exist in the data. Concretely, we introduce new tasks and discover several problems that impede performance at each level and provide remedies or possible solutions in each chapter: i) We define a new task to move beyond Image Captioning to Image Interpretation that can utilize Named Entities in the form of world knowledge. ii) We study the object hallucination problem in classic Image Captioning systems and develop an architecture-agnostic solution. iii) We define a sub-task of Visual Question Answering that requires reading the text in the image (STVQA), where we highlight the limitations of current models. iv) We propose an architecture for the STVQA task that can point to the answer in the image and show how to combine it with classic VQA models. v) We show how far language can get us in STVQA and discover yet another bias which causes the models to disregard the image while doing Visual Question Answering. |
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Thesis |
Ph.D. thesis |
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IMPRIMA |
Place of Publication |
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Editor |
Dimosthenis Karatzas;Lluis Gomez |
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978-84-124793-5-5 |
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DAG |
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no |
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Call Number |
Admin @ si @ Bit2022 |
Serial |
3755 |
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Author |
Andres Mafla |
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Title |
Leveraging Scene Text Information for Image Interpretation |
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Book Whole |
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Year |
2022 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Until recently, most computer vision models remained illiterate, largely ignoring the semantically rich and explicit information contained in scene text. Recent progress in scene text detection and recognition has recently allowed exploring its role in a diverse set of open computer vision problems, e.g. image classification, image-text retrieval, image captioning, and visual question answering to name a few. The explicit semantics of scene text closely requires specific modeling similar to language. However, scene text is a particular signal that has to be interpreted according to a comprehensive perspective that encapsulates all the visual cues in an image. Incorporating this information is a straightforward task for humans, but if we are unfamiliar with a language or scripture, achieving a complete world understanding is impossible (e.a. visiting a foreign country with a different alphabet). Despite the importance of scene text, modeling it requires considering the several ways in which scene text interacts with an image, processing and fusing an additional modality. In this thesis, we mainly focus
on two tasks, scene text-based fine-grained image classification, and cross-modal retrieval. In both studied tasks we identify existing limitations in current approaches and propose plausible solutions. Concretely, in each chapter: i) We define a compact way to embed scene text that generalizes to unseen words at training time while performing in real-time. ii) We incorporate the previously learned scene text embedding to create an image-level descriptor that overcomes optical character recognition (OCR) errors which is well-suited to the fine-grained image classification task. iii) We design a region-level reasoning network that learns the interaction through semantics among salient visual regions and scene text instances. iv) We employ scene text information in image-text matching and introduce the Scene Text Aware Cross-Modal retrieval StacMR task. We gather a dataset that incorporates scene text and design a model suited for the newly studied modality. v) We identify the drawbacks of current retrieval metrics in cross-modal retrieval. An image captioning metric is proposed as a way of better evaluating semantics in retrieved results. Ample experimentation shows that incorporating such semantics into a model yields better semantic results while
requiring significantly less data to converge. |
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Thesis |
Ph.D. thesis |
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IMPRIMA |
Place of Publication |
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Editor |
Dimosthenis Karatzas;Lluis Gomez |
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978-84-124793-6-2 |
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DAG |
Approved |
no |
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Call Number |
Admin @ si @ Maf2022 |
Serial |
3756 |
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Author |
Mohamed Ali Souibgui |
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Title |
Document Image Enhancement and Recognition in Low Resource Scenarios: Application to Ciphers and Handwritten Text |
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Book Whole |
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Year |
2022 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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In this thesis, we propose different contributions with the goal of enhancing and recognizing historical handwritten document images, especially the ones with rare scripts, such as cipher documents.
In the first part, some effective end-to-end models for Document Image Enhancement (DIE) using deep learning models were presented. First, Generative Adversarial Networks (cGAN) for different tasks (document clean-up, binarization, deblurring, and watermark removal) were explored. Next, we further improve the results by recovering the degraded document images into a clean and readable form by integrating a text recognizer into the cGAN model to promote the generated document image to be more readable. Afterward, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion.
The second part of the thesis addresses Handwritten Text Recognition (HTR) in low resource scenarios, i.e. when only few labeled training data is available. We propose novel methods for recognizing ciphers with rare scripts. First, a few-shot object detection based method was proposed. Then, we incorporate a progressive learning strategy that automatically assignspseudo-labels to a set of unlabeled data to reduce the human labor of annotating few pages while maintaining the good performance of the model. Secondly, a data generation technique based on Bayesian Program Learning (BPL) is proposed to overcome the lack of data in such rare scripts. Thirdly, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE). This latter self-supervised model is designed to tackle two tasks, text recognition and document image enhancement. The proposed model does not exhibit limitations of previous state-of-the-art methods based on contrastive losses, while at the same time, it requires substantially fewer data samples to converge.
In the third part of the thesis, we analyze, from the user perspective, the usage of HTR systems in low resource scenarios. This contrasts with the usual research on HTR, which often focuses on technical aspects only and rarely devotes efforts on implementing software tools for scholars in Humanities. |
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Ph.D. thesis |
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IMPRIMA |
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Editor |
Alicia Fornes;Yousri Kessentini |
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978-84-124793-8-6 |
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DAG |
Approved |
no |
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Call Number |
Admin @ si @ Sou2022 |
Serial |
3757 |
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Author |
Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer |
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Title |
Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation |
Type |
Conference Article |
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Year |
2022 |
Publication |
36th Conference on Neural Information Processing Systems |
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We propose a simple but effective source-free domain adaptation (SFDA) method.
Treating SFDA as an unsupervised clustering problem and following the intuition
that local neighbors in feature space should have more similar predictions than
other features, we propose to optimize an objective of prediction consistency. This
objective encourages local neighborhood features in feature space to have similar
predictions while features farther away in feature space have dissimilar predictions, leading to efficient feature clustering and cluster assignment simultaneously. For efficient training, we seek to optimize an upper-bound of the objective resulting in two simple terms. Furthermore, we relate popular existing methods in domain adaptation, source-free domain adaptation and contrastive learning via the perspective of discriminability and diversity. The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method. |
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Virtual; November 2022 |
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NEURIPS |
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Notes |
LAMP; 600.147 |
Approved |
no |
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Call Number |
Admin @ si @ YWW2022a |
Serial |
3792 |
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Author |
Saiping Zhang; Luis Herranz; Marta Mrak; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang |
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Title |
DCNGAN: A Deformable Convolution-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video |
Type |
Conference Article |
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Year |
2022 |
Publication |
47th International Conference on Acoustics, Speech, and Signal Processing |
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In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. |
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Virtual; May 2022 |
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ICASSP |
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Notes |
MACO; 600.161; 601.379 |
Approved |
no |
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Call Number |
Admin @ si @ ZHM2022a |
Serial |
3765 |
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Author |
Guillem Martinez; Maya Aghaei; Martin Dijkstra; Bhalaji Nagarajan; Femke Jaarsma; Jaap van de Loosdrecht; Petia Radeva; Klaas Dijkstra |
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Title |
Hyper-Spectral Imaging for Overlapping Plastic Flakes Segmentation |
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Conference Article |
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Year |
2022 |
Publication |
47th International Conference on Acoustics, Speech, and Signal Processing |
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Keywords |
Hyper-spectral imaging; plastic sorting; multi-label segmentation; bitfield encoding |
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Abstract |
In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. |
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Singapore; May 2022 |
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ICASSP |
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MILAB; no proj |
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no |
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Call Number |
Admin @ si @ MAD2022 |
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3767 |
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Author |
Aitor Alvarez-Gila; Joost Van de Weijer; Yaxing Wang; Estibaliz Garrote |
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Title |
MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation |
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Conference Article |
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Year |
2022 |
Publication |
29th IEEE International Conference on Image Processing |
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multi-view; cross-view; semantic segmentation; synthetic dataset |
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We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116,000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere. MVMO comprises photorealistic, path-traced image renders, together with semantic segmentation ground truth for every view. Unlike existing multi-view datasets, MVMO features wide baselines between cameras and high density of objects, which lead to large disparities, heavy occlusions and view-dependent object appearance. Single view semantic segmentation is hindered by self and inter-object occlusions that could benefit from additional viewpoints. Therefore, we expect that MVMO will propel research in multi-view semantic segmentation and cross-view semantic transfer. We also provide baselines that show that new research is needed in such fields to exploit the complementary information of multi-view setups 1 . |
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Bordeaux; France; October2022 |
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ICIP |
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LAMP |
Approved |
no |
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Call Number |
Admin @ si @ AWW2022 |
Serial |
3781 |
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Author |
Nil Ballus; Bhalaji Nagarajan; Petia Radeva |
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Title |
Opt-SSL: An Enhanced Self-Supervised Framework for Food Recognition |
Type |
Conference Article |
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Year |
2022 |
Publication |
10th Iberian Conference on Pattern Recognition and Image Analysis |
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13256 |
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Keywords |
Self-supervised; Contrastive learning; Food recognition |
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Abstract |
Self-supervised Learning has been showing upbeat performance in several computer vision tasks. The popular contrastive methods make use of a Siamese architecture with different loss functions. In this work, we go deeper into two very recent state of the art frameworks, namely, SimSiam and Barlow Twins. Inspired by them, we propose a new self-supervised learning method we call Opt-SSL that combines both image and feature contrasting. We validate the proposed method on the food recognition task, showing that our proposed framework enables the self-learning networks to learn better visual representations. |
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Aveiro; Portugal; May 2022 |
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LNCS |
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IbPRIA |
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Notes |
MILAB; no menciona |
Approved |
no |
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Call Number |
Admin @ si @ BNR2022 |
Serial |
3782 |
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Author |
Francesco Pelosin; Saurav Jha; Andrea Torsello; Bogdan Raducanu; Joost Van de Weijer |
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Title |
Towards exemplar-free continual learning in vision transformers: an account of attention, functional and weight regularization |
Type |
Conference Article |
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2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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Learning systems; Weight measurement; Image recognition; Surgery; Benchmark testing; Transformers; Stability analysis |
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In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their ability to capture well-scaled representations with respect to the values, and (b) the prescaled attention maps, for carrying value-independent global contextual information. We depict the perks of each distilling strategy on two image recognition benchmarks (CIFAR100 and ImageNet-32) – while (a) leads to a better overall accuracy, (b) helps enhance the rigidity by maintaining competitive performances. Furthermore, we identify the limitation imposed by the symmetric nature of regularization losses. To alleviate this, we propose an asymmetric variant and apply it to the pooled output distillation (POD) loss adapted for ViTs. Our experiments confirm that introducing asymmetry to POD boosts its plasticity while retaining stability across (a) and (b). Moreover, we acknowledge low forgetting measures for all the compared methods, indicating that ViTs might be naturally inclined continual learners. 1 |
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New Orleans; USA; June 2022 |
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Admin @ si @ PJT2022 |
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3784 |
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Hector Laria Mantecon; Yaxing Wang; Joost Van de Weijer; Bogdan Raducanu |
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Transferring Unconditional to Conditional GANs With Hyper-Modulation |
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2022 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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GANs have matured in recent years and are able to generate high-resolution, realistic images. However, the computational resources and the data required for the training of high-quality GANs are enormous, and the study of transfer learning of these models is therefore an urgent topic. Many of the available high-quality pretrained GANs are unconditional (like StyleGAN). For many applications, however, conditional GANs are preferable, because they provide more control over the generation process, despite often suffering more training difficulties. Therefore, in this paper, we focus on transferring from high-quality pretrained unconditional GANs to conditional GANs. This requires architectural adaptation of the pretrained GAN to perform the conditioning. To this end, we propose hyper-modulated generative networks that allow for shared and complementary supervision. To prevent the additional weights of the hypernetwork to overfit, with subsequent mode collapse on small target domains, we introduce a self-initialization procedure that does not require any real data to initialize the hypernetwork parameters. To further improve the sample efficiency of the transfer, we apply contrastive learning in the discriminator, which effectively works on very limited batch sizes. In extensive experiments, we validate the efficiency of the hypernetworks, self-initialization and contrastive loss for knowledge transfer on standard benchmarks. |
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New Orleans; USA; June 2022 |
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LAMP; 600.147; 602.200 |
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LWW2022a |
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3785 |
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Vacit Oguz Yazici; Joost Van de Weijer; Longlong Yu |
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Visual Transformers with Primal Object Queries for Multi-Label Image Classification |
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2022 |
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26th International Conference on Pattern Recognition |
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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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Montreal; Quebec; Canada; August 2022 |
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LAMP; 600.147; 601.309 |
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Admin @ si @ YWY2022 |
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3786 |
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Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados |
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TWD: A New Deep E2E Model for Text Watermark Detection in Video Images |
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2022 |
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26th International Conference on Pattern Recognition |
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Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection |
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Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge |
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Montreal; Quebec; Canada; August 2022 |
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Admin @ si @ BSA2022 |
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3788 |
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Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Federica Cruciani; Lorenza Brusini; Petia Radeva |
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Investigating Explainable Artificial Intelligence for MRI-based Classification of Dementia: a New Stability Criterion for Explainable Methods |
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2022 |
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29th IEEE International Conference on Image Processing |
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Image processing; Stability criteria; Machine learning; Robustness; Alzheimer's disease; Monitoring |
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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. |
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Bordeaux; France; October 2022 |
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ICIP |
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MILAB |
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Admin @ si @ SBC2022 |
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3789 |
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