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Author | ChuanMing Fang; Kai Wang; Joost Van de Weijer | ||||
Title | IterInv: Iterative Inversion for Pixel-Level T2I Models | Type | Conference Article | ||
Year | 2023 | Publication | 37th Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
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Abstract | Large-scale text-to-image diffusion models have been a ground-breaking development in generating convincing images following an input text prompt. The goal of image editing research is to give users control over the generated images by modifying the text prompt. Current image editing techniques are relying on DDIM inversion as a common practice based on the Latent Diffusion Models (LDM). However, the large pretrained T2I models working on the latent space as LDM suffer from losing details due to the first compression stage with an autoencoder mechanism. Instead, another mainstream T2I pipeline working on the pixel level, such as Imagen and DeepFloyd-IF, avoids this problem. They are commonly composed of several stages, normally with a text-to-image stage followed by several super-resolution stages. In this case, the DDIM inversion is unable to find the initial noise to generate the original image given that the super-resolution diffusion models are not compatible with the DDIM technique. According to our experimental findings, iteratively concatenating the noisy image as the condition is the root of this problem. Based on this observation, we develop an iterative inversion (IterInv) technique for this stream of T2I models and verify IterInv with the open-source DeepFloyd-IF model. By combining our method IterInv with a popular image editing method, we prove the application prospects of IterInv. The code will be released at \url{this https URL}. | ||||
Address | New Orleans; USA; December 2023 | ||||
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Area | Expedition | Conference | NEURIPS | ||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ FWW2023 | Serial | 3936 | ||
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Author | Zahra Raisi-Estabragh; Carlos Martin-Isla; Louise Nissen; Liliana Szabo; Victor M. Campello; Sergio Escalera; Simon Winther; Morten Bottcher; Karim Lekadir; and Steffen E. Petersen | ||||
Title | Radiomics analysis enhances the diagnostic performance of CMR stress perfusion: a proof-of-concept study using the Dan-NICAD dataset | Type | Journal Article | ||
Year | 2023 | Publication | Frontiers in Cardiovascular Medicine | Abbreviated Journal | FCM |
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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ RMN2023 | Serial | 3937 | ||
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Author | Albin Soutif; Antonio Carta; Andrea Cossu; Julio Hurtado; Hamed Hemati; Vincenzo Lomonaco; Joost Van de Weijer | ||||
Title | A Comprehensive Empirical Evaluation on Online Continual Learning | Type | Conference Article | ||
Year | 2023 | Publication | Visual Continual Learning (ICCV-W) | Abbreviated Journal | |
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Abstract | Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream. In this empirical evaluation, we evaluate various methods from the literature that tackle online continual learning. More specifically, we focus on the class-incremental setting in the context of image classification, where the learner must learn new classes incrementally from a stream of data. We compare these methods on the Split-CIFAR100 and Split-TinyImagenet benchmarks, and measure their average accuracy, forgetting, stability, and quality of the representations, to evaluate various aspects of the algorithm at the end but also during the whole training period. We find that most methods suffer from stability and underfitting issues. However, the learned representations are comparable to i.i.d. training under the same computational budget. No clear winner emerges from the results and basic experience replay, when properly tuned and implemented, is a very strong baseline. We release our modular and extensible codebase at this https URL based on the avalanche framework to reproduce our results and encourage future research. | ||||
Address | Paris; France; October 2023 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ SCC2023 | Serial | 3938 | ||
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Author | Joakim Bruslund Haurum; Sergio Escalera; Graham W. Taylor; Thomas B. | ||||
Title | Which Tokens to Use? Investigating Token Reduction in Vision Transformers | Type | Conference Article | ||
Year | 2023 | Publication | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
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Abstract | Since the introduction of the Vision Transformer (ViT), researchers have sought to make ViTs more efficient by removing redundant information in the processed tokens. While different methods have been explored to achieve this goal, we still lack understanding of the resulting reduction patterns and how those patterns differ across token reduction methods and datasets. To close this gap, we set out to understand the reduction patterns of 10 different token reduction methods using four image classification datasets. By systematically comparing these methods on the different classification tasks, we find that the Top-K pruning method is a surprisingly strong baseline. Through in-depth analysis of the different methods, we determine that: the reduction patterns are generally not consistent when varying the capacity of the backbone model, the reduction patterns of pruning-based methods significantly differ from fixed radial patterns, and the reduction patterns of pruning-based methods are correlated across classification datasets. Finally we report that the similarity of reduction patterns is a moderate-to-strong proxy for model performance. Project page at https://vap.aau.dk/tokens. | ||||
Address | Paris; France; October 2023 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ BET2023 | Serial | 3940 | ||
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Author | Xavier Soria; Yachuan Li; Mohammad Rouhani; Angel Sappa | ||||
Title | Tiny and Efficient Model for the Edge Detection Generalization | Type | Conference Article | ||
Year | 2023 | Publication | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
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Abstract | Most high-level computer vision tasks rely on low-level image operations as their initial processes. Operations such as edge detection, image enhancement, and super-resolution, provide the foundations for higher level image analysis. In this work we address the edge detection considering three main objectives: simplicity, efficiency, and generalization since current state-of-the-art (SOTA) edge detection models are increased in complexity for better accuracy. To achieve this, we present Tiny and Efficient Edge Detector (TEED), a light convolutional neural network with only 58K parameters, less than 0:2% of the state-of-the-art models. Training on the BIPED dataset takes less than 30 minutes, with each epoch requiring less than 5 minutes. Our proposed model is easy to train and it quickly converges within very first few epochs, while the predicted edge-maps are crisp and of high quality. Additionally, we propose a new dataset to test the generalization of edge detection, which comprises samples from popular images used in edge detection and image segmentation. The source code is available in https://github.com/xavysp/TEED. | ||||
Address | Paris; France; October 2023 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | MSIAU | Approved | no | ||
Call Number | Admin @ si @ SLR2023 | Serial | 3941 | ||
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Author | Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; CV Jawahar | ||||
Title | Understanding Video Scenes Through Text: Insights from Text-Based Video Question Answering | Type | Conference Article | ||
Year | 2023 | Publication | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
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Abstract | Researchers have extensively studied the field of vision and language, discovering that both visual and textual content is crucial for understanding scenes effectively. Particularly, comprehending text in videos holds great significance, requiring both scene text understanding and temporal reasoning. This paper focuses on exploring two recently introduced datasets, NewsVideoQA and M4-ViteVQA, which aim to address video question answering based on textual content. The NewsVideoQA dataset contains question-answer pairs related to the text in news videos, while M4- ViteVQA comprises question-answer pairs from diverse categories like vlogging, traveling, and shopping. We provide an analysis of the formulation of these datasets on various levels, exploring the degree of visual understanding and multi-frame comprehension required for answering the questions. Additionally, the study includes experimentation with BERT-QA, a text-only model, which demonstrates comparable performance to the original methods on both datasets, indicating the shortcomings in the formulation of these datasets. Furthermore, we also look into the domain adaptation aspect by examining the effectiveness of training on M4-ViteVQA and evaluating on NewsVideoQA and vice-versa, thereby shedding light on the challenges and potential benefits of out-of-domain training. | ||||
Address | Paris; France; October 2023 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ JMK2023 | Serial | 3946 | ||
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Author | Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez | ||||
Title | Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules | Type | Conference Article | ||
Year | 2023 | Publication | 37th International Congress and Exhibition is organized by Computer Assisted Radiology and Surgery | Abbreviated Journal | |
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Abstract | Pòster | ||||
Address | Munich; Germany; June 2023 | ||||
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Area | Expedition | Conference | CARS | ||
Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ TGR2023a | Serial | 3950 | ||
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Author | Sonia Baeza; Debora Gil; Carles Sanchez; Guillermo Torres; Ignasi Garcia Olive; Ignasi Guasch; Samuel Garcia Reina; Felipe Andreo; Jose Luis Mate; Jose Luis Vercher; Antonio Rosell | ||||
Title | Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung | Type | Conference Article | ||
Year | 2023 | Publication | SEPAR | Abbreviated Journal | |
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Abstract | Pòster | ||||
Address | Granada; Spain; June 2023 | ||||
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Area | Expedition | Conference | SEPAR | ||
Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ BGS2023 | Serial | 3951 | ||
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Author | Debora Gil; Guillermo Torres; Carles Sanchez | ||||
Title | Transforming radiomic features into radiological words | Type | Conference Article | ||
Year | 2023 | Publication | IEEE International Symposium on Biomedical Imaging | Abbreviated Journal | |
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Abstract | Pòster | ||||
Address | Cartagena de Indias; Colombia; April 2023 | ||||
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Area | Expedition | Conference | ISBI | ||
Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ GTS2023 | Serial | 3952 | ||
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Author | Pau Cano; Debora Gil; Eva Musulen | ||||
Title | Towards automatic detection of helicobacter pylori in histological samples of gastric tissue | Type | Conference Article | ||
Year | 2023 | Publication | IEEE International Symposium on Biomedical Imaging | Abbreviated Journal | |
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Address | Cartagena de Indias; Colombia; April 2023 | ||||
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Area | Expedition | Conference | ISBI | ||
Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ CGM2023 | Serial | 3953 | ||
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Author | Guillermo Torres; Debora Gil; Antonio Rosell; Sonia Baeza; Carles Sanchez | ||||
Title | A radiomic biopsy for virtual histology of pulmonary nodules | Type | Conference Article | ||
Year | 2023 | Publication | IEEE International Symposium on Biomedical Imaging | Abbreviated Journal | |
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Abstract | Pòster | ||||
Address | Cartagena de Indias; Colombia; April 2023 | ||||
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Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | ISBI | ||
Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ TGR2023b | Serial | 3954 | ||
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Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li | ||||
Title | Advances in Face Presentation Attack Detection | Type | Book Whole | ||
Year | 2023 | Publication | Advances in Face Presentation Attack Detection | Abbreviated Journal | |
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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ WGE2023a | Serial | 3955 | ||
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Author | Armin Mehri | ||||
Title | Deep learning based architectures for cross-domain image processing | Type | Book Whole | ||
Year | 2023 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Human vision is restricted to the visual-optical spectrum. Machine vision is not.
Cameras sensitive to diverse infrared spectral bands can improve the capacities of autonomous systems and provide a comprehensive view. Relevant scene content can be made visible, particularly in situations when sensors of other modalities, such as a visual-optical camera, require a source of illumination. As a result, increasing the level of automation not only avoids human errors but also reduces machine-induced errors. Furthermore, multi-spectral sensor systems with infrared imagery as one modality are a rich source of information and can conceivably increase the robustness of many autonomous systems. Robotics, automobiles, biometrics, security, surveillance, and the military are some examples of fields that can profit from the use of infrared imagery in their respective applications. Although multimodal spectral sensors have come a long way, there are still several bottlenecks that prevent us from combining their output information and using them as comprehensive images. The primary issue with infrared imaging is the lack of potential benefits due to their cost influence on sensor resolution, which grows exponentially with greater resolution. Due to the more costly sensor technology required for their development, their resolutions are substantially lower than thoseof regular digital cameras. This thesis aims to improve beyond-visible-spectrum machine vision by integrating multi-modal spectral sensors. The emphasis is on transforming the produced images to enhance their resolution to match expected human perception, bring the color representation close to human understanding of natural color, and improve machine vision application performance. This research focuses mainly on two tasks, image Colorization and Image Super resolution for both single- and cross-domain problems. We first start with an extensive review of the state of the art in both tasks, point out the shortcomings of existing approaches, and then present our solutions to address their limitations. Our solutions demonstrate that low-cost channel information (i.e., visible image) can be used to improve expensive channel information (i.e., infrared image), resulting in images with higher quality and closer to human perception at a lower cost than a high-cost infrared camera. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | IMPRIMA | Place of Publication | Editor | Angel Sappa | |
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ISSN | ISBN | 978-84-126409-1-5 | Medium | ||
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Notes | MSIAU | Approved | no | ||
Call Number | Admin @ si @ Meh2023 | Serial | 3959 | ||
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Author | Chenshen Wu | ||||
Title | Going beyond Classification Problems for the Continual Learning of Deep Neural Networks | Type | Book Whole | ||
Year | 2023 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Deep learning has made tremendous progress in the last decade due to the explosion of training data and computational power. Through end-to-end training on a
large dataset, image representations are more discriminative than the previously used hand-crafted features. However, for many real-world applications, training and testing on a single dataset is not realistic, as the test distribution may change over time. Continuous learning takes this situation into account, where the learner must adapt to a sequence of tasks, each with a different distribution. If you would naively continue training the model with a new task, the performance of the model would drop dramatically for the previously learned data. This phenomenon is known as catastrophic forgetting. Many approaches have been proposed to address this problem, which can be divided into three main categories: regularization-based approaches, rehearsal-based approaches, and parameter isolation-based approaches. However, most of the existing works focus on image classification tasks and many other computer vision tasks have not been well-explored in the continual learning setting. Therefore, in this thesis, we study continual learning for image generation, object re-identification, and object counting. For the image generation problem, since the model can generate images from the previously learned task, it is free to apply rehearsal without any limitation. We developed two methods based on generative replay. The first one uses the generated image for joint training together with the new data. The second one is based on output pixel-wise alignment. We extensively evaluate these methods on several benchmarks. Next, we study continual learning for object Re-Identification (ReID). Although most state-of-the-art methods of ReID and continual ReID use softmax-triplet loss, we found that it is better to solve the ReID problem from a meta-learning perspective because continual learning of reID can benefit a lot from the generalization of metalearning. We also propose a distillation loss and found that the removal of the positive pairs before the distillation loss is critical. Finally, we study continual learning for the counting problem. We study the mainstream method based on density maps and propose a new approach for density map distillation. We found that fixing the counter head is crucial for the continual learning of object counting. To further improve results, we propose an adaptor to adapt the changing feature extractor for the fixed counter head. Extensive evaluation shows that this results in improved continual learning performance. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | IMPRIMA | Place of Publication | Editor | Joost Van de Weijer;Bogdan Raducanu | |
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-126409-0-8 | Medium | ||
Area | Expedition | Conference | |||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ Wu2023 | Serial | 3960 | ||
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Author | Jose Luis Gomez | ||||
Title | Synth-to-real semi-supervised learning for visual tasks | Type | Book Whole | ||
Year | 2023 | Publication | Going beyond Classification Problems for the Continual Learning of Deep Neural Networks | Abbreviated Journal | |
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Abstract | The curse of data labeling is a costly bottleneck in supervised deep learning, where large amounts of labeled data are needed to train intelligent systems. In onboard perception for autonomous driving, this cost corresponds to the labeling of raw data from sensors such as cameras, LiDARs, RADARs, etc. Therefore, synthetic data with automatically generated ground truth (labels) has aroused as a reliable alternative for training onboard perception models.
However, synthetic data commonly suffers from synth-to-real domain shift, i.e., models trained on the synthetic domain do not show their achievable accuracy when performing in the real world. This shift needs to be addressed by techniques falling in the realm of domain adaptation (DA). The semi-supervised learning (SSL) paradigm can be followed to address DA. In this case, a model is trained using source data with labels (here synthetic) and leverages minimal knowledge from target data (here the real world) to generate pseudo-labels. These pseudo-labels help the training process to reduce the gap between the source and the target domains. In general, we can assume accessing both, pseudo-labels and a few amounts of human-provided labels for the target-domain data. However, the most interesting and challenging setting consists in assuming that we do not have human-provided labels at all. This setting is known as unsupervised domain adaptation (UDA). This PhD focuses on applying SSL to the UDA setting, for onboard visual tasks related to autonomous driving. We start by addressing the synth-to-real UDA problem on onboard vision-based object detection (pedestrians and cars), a critical task for autonomous driving and driving assistance. In particular, we propose to apply an SSL technique known as co-training, which we adapt to work with deep models that process a multi-modal input. The multi-modality consists of the visual appearance of the images (RGB) and their monocular depth estimation. The synthetic data we use as the source domain contains both, object bounding boxes and depth information. This prior knowledge is the starting point for the co-training technique, which iteratively labels unlabeled real-world data and uses such pseudolabels (here bounding boxes with an assigned object class) to progressively improve the labeling results. Along this process, two models collaborate to automatically label the images, in a way that one model compensates for the errors of the other, so avoiding error drift. While this automatic labeling process is done offline, the resulting pseudolabels can be used to train object detection models that must perform in real-time onboard a vehicle. We show that multi-modal co-training improves the labeling results compared to single-modal co-training, remaining competitive compared to human labeling. Given the success of co-training in the context of object detection, we have also adapted this technique to a more crucial and challenging visual task, namely, onboard semantic segmentation. In fact, providing labels for a single image can take from 30 to 90 minutes for a human labeler, depending on the content of the image. Thus, developing automatic labeling techniques for this visual task is of great interest to the automotive industry. In particular, the new co-training framework addresses synth-to-real UDA by an initial stage of self-training. Intermediate models arising from this stage are used to start the co-training procedure, for which we have elaborated an accurate collaboration policy between the two models performing the automatic labeling. Moreover, our co-training seamlessly leverages datasets from different synthetic domains. In addition, the co-training procedure is agnostic to the loss function used to train the semantic segmentation models which perform the automatic labeling. We achieve state-of-the-art results on publicly available benchmark datasets, again, remaining competitive compared to human labeling. Finally, on the ground of our previous experience, we have designed and implemented a new SSL technique for UDA in the context of visual semantic segmentation. In this case, we mimic the labeling methodology followed by human labelers. In particular, rather than labeling full images at a time, categories of semantic classes are defined and only those are labeled in a labeling pass. In fact, different human labelers can become specialists in labeling different categories. Afterward, these per-category-labeled layers are combined to provide fully labeled images. Our technique is inspired by this methodology since we perform synth-to-real UDA per category, using the self-training stage previously developed as part of our co-training framework. The pseudo-labels obtained for each category are finally fused to obtain fully automatically labeled images. In this context, we have also contributed to the development of a new photo-realistic synthetic dataset based on path-tracing rendering. Our new SSL technique seamlessly leverages publicly available synthetic datasets as well as this new one to obtain state-of-the-art results on synth-to-real UDA for semantic segmentation. We show that the new dataset allows us to reach better labeling accuracy than previously existing datasets, at the same time that it complements well them when combined. Moreover, we also show that the new human-inspired SSL technique outperforms co-training. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | IMPRIMA | Place of Publication | Editor | Antonio Lopez | |
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Gom2023 | Serial | 3961 | ||
Permanent link to this record |