| Home | [121–130] << 131 132 133 134 135 136 137 138 139 140 >> [141–150] |
|
| Records | |||||
|---|---|---|---|---|---|
| Author | Francesc Tous; Maria Vanrell; Ramon Baldrich | ||||
Title  |
Exploring Colour Constancy Solutions. | Type | Miscellaneous | ||
| Year | 2004 | Publication | CGIV 2004 Second European Conference on Colour in Graphics, Imaging, and Vision, 24:29 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | |||||
| Address | Aachen (Germany) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ TVB2004 | Serial | 452 | ||
| Permanent link to this record | |||||
| Author | Aura Hernandez-Sabate | ||||
| Title |
Exploring Arterial Dynamics and Structures in IntraVascular Ultrasound Sequences | Type | Book Whole | ||
| Year | 2009 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Cardiovascular diseases are a leading cause of death in developed countries. Most of them are caused by arterial (specially coronary) diseases, mainly caused by plaque accumulation. Such pathology narrows blood flow (stenosis) and affects artery bio- mechanical elastic properties (atherosclerosis). In the last decades, IntraVascular UltraSound (IVUS) has become a usual imaging technique for the diagnosis and follow up of arterial diseases. IVUS is a catheter-based imaging technique which shows a sequence of cross sections of the artery under study. Inspection of a single image gives information about the percentage of stenosis. Meanwhile, inspection of longitudinal views provides information about artery bio-mechanical properties, which can prevent a fatal outcome of the cardiovascular disease. On one hand, dynamics of arteries (due to heart pumping among others) is a major artifact for exploring tissue bio-mechanical properties. On the other one, manual stenosis measurements require a manual tracing of vessel borders, which is a time-consuming task and might suffer from inter-observer variations. This PhD thesis proposes several image processing tools for exploring vessel dy- namics and structures. We present a physics-based model to extract, analyze and correct vessel in-plane rigid dynamics and to retrieve cardiac phase. Furthermore, we introduce a deterministic-statistical method for automatic vessel borders detection. In particular, we address adventitia layer segmentation. An accurate validation pro- tocol to ensure reliable clinical applicability of the methods is a crucial step in any proposal of an algorithm. In this thesis we take special care in designing a valida- tion protocol for each approach proposed and we contribute to the in vivo dynamics validation with a quantitative and objective score to measure the amount of motion suppressed. | ||||
| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Debora Gil | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-937261-6-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM; | Approved | no | ||
| Call Number | IAM @ iam @ Her2009 | Serial | 1543 | ||
| Permanent link to this record | |||||
| Author | Georg Langs; Petia Radeva; David Rotger; Francesc Carreras | ||||
| Title |
Explorative Building of 3D Vessel Tree Models | Type | Miscellaneous | ||
| Year | 2004 | Publication | “Digital Imaging in Media and Education”, 28th annual workshop of the Austrian Association for Pattern Recognition (OAGM/AAPR) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | |||||
| Address | Hagenberg (Austria) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ LRR2004a | Serial | 467 | ||
| Permanent link to this record | |||||
| Author | Soumick Chatterjee; Fatima Saad; Chompunuch Sarasaen; Suhita Ghosh; Rupali Khatun; Petia Radeva; Georg Rose; Sebastian Stober; Oliver Speck; Andreas Nürnberger | ||||
| Title |
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images | Type | Miscellaneous | ||
| Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | CoRR abs/2006.02570
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process. Thereby, the use of five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoniæ and healthy subjects using Chest X-Ray. Multi-label classification was performed to predict multiple pathologies for each patient, if present. Foremost, the interpretability of each of the networks was thoroughly studied using techniques like occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models. The qualitative results depicted the ResNets to be the most interpretable model. |
||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ CSS2020 | Serial | 3534 | ||
| Permanent link to this record | |||||
| Author | Xialei Liu; Joost Van de Weijer; Andrew Bagdanov | ||||
| Title |
Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 41 | Issue | 8 | Pages | 1862-1878 |
| Keywords | Task analysis;Training;Image quality;Visualization;Uncertainty;Labeling;Neural networks;Learning from rankings;image quality assessment;crowd counting;active learning | ||||
| Abstract | For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning. Self-supervised learning addresses this by positing an auxiliary task (different, but related to the supervised task) for which data is abundantly available. In this paper, we show how ranking can be used as a proxy task for some regression problems. As another contribution, we propose an efficient backpropagation technique for Siamese networks which prevents the redundant computation introduced by the multi-branch network architecture. We apply our framework to two regression problems: Image Quality Assessment (IQA) and Crowd Counting. For both we show how to automatically generate ranked image sets from unlabeled data. Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting. In addition, we show that measuring network uncertainty on the self-supervised proxy task is a good measure of informativeness of unlabeled data. This can be used to drive an algorithm for active learning and we show that this reduces labeling effort by up to 50 percent. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | LAMP; 600.109; 600.106; 600.120 | Approved | no | ||
| Call Number | LWB2019 | Serial | 3267 | ||
| Permanent link to this record | |||||
| Author | F.Guirado; Ana Ripoll; C.Roig; Aura Hernandez-Sabate; Emilio Luque | ||||
| Title |
Exploiting Throughput for Pipeline Execution in Streaming Image Processing Applications | Type | Book Chapter | ||
| Year | 2006 | Publication | Euro-Par 2006 Parallel Processing | Abbreviated Journal | LNCS |
| Volume | 4128 | Issue | Pages | 1095-1105 | |
| Keywords | 12th International Euro–Par Conference | ||||
| Abstract | There is a large range of image processing applications that act on an input sequence of image frames that are continuously received. Throughput is a key performance measure to be optimized when execu- ting them. In this paper we propose a new task replication methodology for optimizing throughput for an image processing application in the field of medicine. The results show that by applying the proposed methodo- logy we are able to achieve the desired throughput in all cases, in such a way that the input frames can be processed at any given rate. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springer-Verlag Berlin Heidelberg | Place of Publication | Dresden, Germany (European Union) | Editor | UAB; W, E.N.; et al. |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Lecture Notes In Computer Science | Abbreviated Series Title | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | Euro–Par | ||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ GRR2006a | Serial | 1542 | ||
| Permanent link to this record | |||||
| Author | Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui | ||||
| Title |
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation | Type | Conference Article | ||
| Year | 2021 | Publication | Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might no longer align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors, and propose a self regularization loss to decrease the negative impact of noisy neighbors. Furthermore, to aggregate information with more context, we consider expanded neighborhoods with small affinity values. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets. Code is available in https://github.com/Albert0147/SFDA_neighbors. | ||||
| Address | Online; December 7-10, 2021 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | NIPS | ||
| Notes | LAMP; 600.147; 600.141 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3691 | ||
| Permanent link to this record | |||||
| Author | Raul Gomez | ||||
| Title |
Exploiting the Interplay between Visual and Textual Data for Scene Interpretation | Type | Book Whole | ||
| Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Machine learning experimentation under controlled scenarios and standard datasets is necessary to compare algorithms performance by evaluating all of them in the same setup. However, experimentation on how those algorithms perform on unconstrained data and applied tasks to solve real world problems is also a must to ascertain how that research can contribute to our society.
In this dissertation we experiment with the latest computer vision and natural language processing algorithms applying them to multimodal scene interpretation. Particularly, we research on how image and text understanding can be jointly exploited to address real world problems, focusing on learning from Social Media data. We address several tasks that involve image and textual information, discuss their characteristics and offer our experimentation conclusions. First, we work on detection of scene text in images. Then, we work with Social Media posts, exploiting the captions associated to images as supervision to learn visual features, which we apply to multimodal semantic image retrieval. Subsequently, we work with geolocated Social Media images with associated tags, experimenting on how to use the tags as supervision, on location sensitive image retrieval and on exploiting location information for image tagging. Finally, we work on a specific classification problem of Social Media publications consisting on an image and a text: Multimodal hate speech classification. |
||||
| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Dimosthenis Karatzas;Lluis Gomez;Jaume Gibert | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-121011-7-1 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ Gom20 | Serial | 3479 | ||
| Permanent link to this record | |||||
| Author | Lluis Gomez | ||||
| Title |
Exploiting Similarity Hierarchies for Multi-script Scene Text Understanding | Type | Book Whole | ||
| Year | 2016 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | This thesis addresses the problem of automatic scene text understanding in unconstrained conditions. In particular, we tackle the tasks of multi-language and arbitrary-oriented text detection, tracking, and script identification in natural scenes.
For this we have developed a set of generic methods that build on top of the basic observation that text has always certain key visual and structural characteristics that are independent of the language or script in which it is written. Text instances in any language or script are always formed as groups of similar atomic parts, being them either individual characters, small stroke parts, or even whole words in the case of cursive text. This holistic (sumof-parts) and recursive perspective has lead us to explore different variants of the “segmentation and grouping” paradigm of computer vision. Scene text detection methodologies are usually based in classification of individual regions or patches, using a priory knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organization through which text emerges as a perceptually significant group of atomic objects. In this thesis, we argue that the text detection problem must be posed as the detection of meaningful groups of regions. We address the problem of text detection in natural scenes from a hierarchical perspective, making explicit use of the recursive nature of text, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypothese with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Within this generic framework, we design a text-specific object proposals algorithm that, contrary to existing generic object proposals methods, aims directly to the detection of text regions groupings. For this, we abandon the rigid definition of “what is text” of traditional specialized text detectors, and move towards more fuzzy perspective of grouping-based object proposals methods. Then, we present a hybrid algorithm for detection and tracking of scene text where the notion of region groupings plays also a central role. By leveraging the structural arrangement of text group components between consecutive frames we can improve the overall tracking performance of the system. Finally, since our generic detection framework is inherently designed for multi-language environments, we focus on the problem of script identification in order to build a multi-language end-toend reading system. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed size as in the typical use of holistic CNN classifiers, we propose a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme. |
||||
| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Place of Publication | Editor | Dimosthenis Karatzas | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ Gom2016 | Serial | 2891 | ||
| Permanent link to this record | |||||
| Author | Jordi Gonzalez; Josep M. Gonfaus; Carles Fernandez; Xavier Roca | ||||
| Title |
Exploiting Natural-Language Interaction in Video Surveillance Systems | Type | Conference Article | ||
| Year | 2011 | Publication | V&L Net Workshop on Vision and Language | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | |||||
| Address | Brighton, UK | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | VL | ||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ GGF2011 | Serial | 1813 | ||
| Permanent link to this record | |||||
| Author | Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez | ||||
| Title |
Exploiting Natural Language Generation in Scene Interpretation | Type | Book Chapter | ||
| Year | 2009 | Publication | Human–Centric Interfaces for Ambient Intelligence | Abbreviated Journal | |
| Volume | 4 | Issue | Pages | 71–93 | |
| Keywords | |||||
| Abstract | |||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Elsevier Science and Tech | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ FBR2009 | Serial | 1212 | ||
| Permanent link to this record | |||||
| Author | Ivan Huerta; Ariel Amato; Xavier Roca; Jordi Gonzalez | ||||
| Title |
Exploiting Multiple Cues in Motion Segmentation Based on Background Subtraction | Type | Journal Article | ||
| Year | 2013 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
| Volume | 100 | Issue | Pages | 183–196 | |
| Keywords | Motion segmentation; Shadow suppression; Colour segmentation; Edge segmentation; Ghost detection; Background subtraction | ||||
| Abstract | This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motionsegmentation. In our first contribution, a case analysis of motionsegmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motionsegmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Elsevier | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ HAR2013 | Serial | 1808 | ||
| Permanent link to this record | |||||
| Author | Marc Castello; Jordi Gonzalez; Ariel Amato; Pau Baiget; Carles Fernandez; Josep M. Gonfaus; Ramon Mollineda; Marco Pedersoli; Nicolas Perez de la Blanca; Xavier Roca | ||||
| Title |
Exploiting Multimodal Interaction Techniques for Video-Surveillance | Type | Book Chapter | ||
| Year | 2013 | Publication | Multimodal Interaction in Image and Video Applications Intelligent Systems Reference Library | Abbreviated Journal | |
| Volume | 48 | Issue | 8 | Pages | 135-151 |
| Keywords | |||||
| Abstract | In this paper we present an example of a video surveillance application that exploits Multimodal Interactive (MI) technologies. The main objective of the so-called VID-Hum prototype was to develop a cognitive artificial system for both the detection and description of a particular set of human behaviours arising from real-world events. The main procedure of the prototype described in this chapter entails: (i) adaptation, since the system adapts itself to the most common behaviours (qualitative data) inferred from tracking (quantitative data) thus being able to recognize abnormal behaviors; (ii) feedback, since an advanced interface based on Natural Language understanding allows end-users the communicationwith the prototype by means of conceptual sentences; and (iii) multimodality, since a virtual avatar has been designed to describe what is happening in the scene, based on those textual interpretations generated by the prototype. Thus, the MI methodology has provided an adequate framework for all these cooperating processes. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1868-4394 | ISBN | 978-3-642-35931-6 | Medium | |
| Area | Expedition | Conference | |||
| Notes | ISE; 605.203; 600.049 | Approved | no | ||
| Call Number | CGA2013 | Serial | 2222 | ||
| Permanent link to this record | |||||
| Author | Chuanming Tang; Kai Wang; Joost van de Weijer; Jianlin Zhang; Yongmei Huang | ||||
| Title |
Exploiting Image-Related Inductive Biases in Single-Branch Visual Tracking | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Despite achieving state-of-the-art performance in visual tracking, recent single-branch trackers tend to overlook the weak prior assumptions associated with the Vision Transformer (ViT) encoder and inference pipeline. Moreover, the effectiveness of discriminative trackers remains constrained due to the adoption of the dual-branch pipeline. To tackle the inferior effectiveness of the vanilla ViT, we propose an Adaptive ViT Model Prediction tracker (AViTMP) to bridge the gap between single-branch network and discriminative models. Specifically, in the proposed encoder AViT-Enc, we introduce an adaptor module and joint target state embedding to enrich the dense embedding paradigm based on ViT. Then, we combine AViT-Enc with a dense-fusion decoder and a discriminative target model to predict accurate location. Further, to mitigate the limitations of conventional inference practice, we present a novel inference pipeline called CycleTrack, which bolsters the tracking robustness in the presence of distractors via bidirectional cycle tracking verification. Lastly, we propose a dual-frame update inference strategy that adeptively handles significant challenges in long-term scenarios. In the experiments, we evaluate AViTMP on ten tracking benchmarks for a comprehensive assessment, including LaSOT, LaSOTExtSub, AVisT, etc. The experimental results unequivocally establish that AViTMP attains state-of-the-art performance, especially on long-time tracking and robustness. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ TWW2023 | Serial | 3978 | ||
| Permanent link to this record | |||||
| Author | Alejandro Ariza-Casabona; Bartlomiej Twardowski; Tri Kurniawan Wijaya | ||||
| Title |
Exploiting Graph Structured Cross-Domain Representation for Multi-domain Recommendation | Type | Conference Article | ||
| Year | 2023 | Publication | European Conference on Information Retrieval – ECIR 2023: Advances in Information Retrieval | Abbreviated Journal | |
| Volume | 13980 | Issue | Pages | 49–65 | |
| Keywords | |||||
| Abstract | Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying dedicated training regimes. At the same time, treating domains as separate input sources becomes a limitation as it does not capture the interplay that naturally exists between domains. In this work, we efficiently learn multi-domain representation of sequential users’ interactions using graph neural networks. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec (short for Multi-dom Ain Graph-based Recommender). To better capture all relations in a multi-domain setting, we learn two graph-based sequential representations simultaneously: domain-guided for recent user interest, and general for long-term interest. This approach helps to mitigate the negative knowledge transfer problem from multiple domains and improve overall representation. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods. Furthermore, we provide an ablation study and discuss further extensions of our method. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECIR | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ ATK2023 | Serial | 3933 | ||
| Permanent link to this record | |||||