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| Author | Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer | ||||
| Title | Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation | Type | Conference Article | ||
| Year | 2022 | Publication | 36th Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Abstract | 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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| Address | Virtual; November 2022 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | NEURIPS | ||
| Notes | LAMP; 600.147 | Approved | no | ||
Call Number  |
Admin @ si @ YWW2022a | Serial | 3792 | ||
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| Author | Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer | ||||
| Title | Local Prediction Aggregation: A Frustratingly Easy Source-free Domain Adaptation Method | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | 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. Code is available in this https URL. | ||||
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| Area | Expedition | Conference | |||
| Notes | LAMP; 600.147 | Approved | no | ||
| Call Number |
Admin @ si @ YWW2022b | Serial | 3815 | ||
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| Author | Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer | ||||
| Title | One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | In this paper, we investigate model adaptation under domain and category shift, where the final goal is to achieve
(SF-UNDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-UNDA setting, the model cannot access source data anymore during target adaptation, which aims to address data privacy concerns. We propose a novel training scheme to learn a ( +1)-way classifier to predict the source classes and the unknown class, where samples of only known source categories are available for training. Furthermore, for target adaptation, we simply adopt a weighted entropy minimization to adapt the source pretrained model to the unlabeled target domain without source data. In experiments, we show: After source training, the resulting source model can get excellent performance for ; After target adaptation, our method surpasses current UNDA approaches which demand source data during adaptation. The versatility to several different tasks strongly proves the efficacy and generalization ability of our method. When augmented with a closed-set domain adaptation approach during target adaptation, our source-free method further outperforms the current state-of-the-art UNDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA respectively. |
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| Area | Expedition | Conference | |||
| Notes | LAMP; no proj | Approved | no | ||
| Call Number |
Admin @ si @ YWW2022c | Serial | 3818 | ||
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| Author | Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui; Jian Yang | ||||
| Title | Trust Your Good Friends: Source-Free Domain Adaptation by Reciprocal Neighborhood Clustering | Type | Journal Article | ||
| Year | 2023 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 45 | Issue | 12 | Pages | 15883-15895 |
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| 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 not 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. To aggregate information with more context, we consider expanded neighborhoods with small affinity values. Furthermore, we consider the density around each target sample, which can alleviate the negative impact of potential outliers. 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. | ||||
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| Notes | LAMP; MACO | Approved | no | ||
| Call Number |
Admin @ si @ YWW2023 | Serial | 3889 | ||
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| Author | Vacit Oguz Yazici; Joost Van de Weijer; Longlong Yu | ||||
| Title | Visual Transformers with Primal Object Queries for Multi-Label Image Classification | Type | Conference Article | ||
| Year | 2022 | Publication | 26th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Multi-label image classification is about predicting a set of class labels that can be considered as orderless sequential data. Transformers process the sequential data as a whole, therefore they are inherently good at set prediction. The first vision-based transformer model, which was proposed for the object detection task introduced the concept of object queries. Object queries are learnable positional encodings that are used by attention modules in decoder layers to decode the object classes or bounding boxes using the region of interests in an image. However, inputting the same set of object queries to different decoder layers hinders the training: it results in lower performance and delays convergence. In this paper, we propose the usage of primal object queries that are only provided at the start of the transformer decoder stack. In addition, we improve the mixup technique proposed for multi-label classification. The proposed transformer model with primal object queries improves the state-of-the-art class wise F1 metric by 2.1% and 1.8%; and speeds up the convergence by 79.0% and 38.6% on MS-COCO and NUS-WIDE datasets respectively. | ||||
| Address | Montreal; Quebec; Canada; August 2022 | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes | LAMP; 600.147; 601.309 | Approved | no | ||
| Call Number |
Admin @ si @ YWY2022 | Serial | 3786 | ||
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| Author | Shun Yao; Fei Yang; Yongmei Cheng; Mikhail Mozerov | ||||
| Title | 3D Shapes Local Geometry Codes Learning with SDF | Type | Conference Article | ||
| Year | 2021 | Publication | International Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2110-2117 | ||
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| Abstract | A signed distance function (SDF) as the 3D shape description is one of the most effective approaches to represent 3D geometry for rendering and reconstruction. Our work is inspired by the state-of-the-art method DeepSDF [17] that learns and analyzes the 3D shape as the iso-surface of its shell and this method has shown promising results especially in the 3D shape reconstruction and compression domain. In this paper, we consider the degeneration problem of reconstruction coming from the capacity decrease of the DeepSDF model, which approximates the SDF with a neural network and a single latent code. We propose Local Geometry Code Learning (LGCL), a model that improves the original DeepSDF results by learning from a local shape geometry of the full 3D shape. We add an extra graph neural network to split the single transmittable latent code into a set of local latent codes distributed on the 3D shape. Mentioned latent codes are used to approximate the SDF in their local regions, which will alleviate the complexity of the approximation compared to the original DeepSDF. Furthermore, we introduce a new geometric loss function to facilitate the training of these local latent codes. Note that other local shape adjusting methods use the 3D voxel representation, which in turn is a problem highly difficult to solve or even is insolvable. In contrast, our architecture is based on graph processing implicitly and performs the learning regression process directly in the latent code space, thus make the proposed architecture more flexible and also simple for realization. Our experiments on 3D shape reconstruction demonstrate that our LGCL method can keep more details with a significantly smaller size of the SDF decoder and outperforms considerably the original DeepSDF method under the most important quantitative metrics. | ||||
| Address | VIRTUAL; October 2021 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | LAMP | Approved | no | ||
| Call Number |
Admin @ si @ YYC2021 | Serial | 3681 | ||
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| Author | Lu Yu; Vacit Oguz Yazici; Xialei Liu; Joost Van de Weijer; Yongmei Cheng; Arnau Ramisa | ||||
| Title | Learning Metrics from Teachers: Compact Networks for Image Embedding | Type | Conference Article | ||
| Year | 2019 | Publication | 32nd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2907-2916 | ||
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| Abstract | Metric learning networks are used to compute image embeddings, which are widely used in many applications such as image retrieval and face recognition. In this paper, we propose to use network distillation to efficiently compute image embeddings with small networks. Network distillation has been successfully applied to improve image classification, but has hardly been explored for metric learning. To do so, we propose two new loss functions that model the
communication of a deep teacher network to a small student network. We evaluate our system in several datasets, including CUB-200-2011, Cars-196, Stanford Online Products and show that embeddings computed using small student networks perform significantly better than those computed using standard networks of similar size. Results on a very compact network (MobileNet-0.25), which can be used on mobile devices, show that the proposed method can greatly improve Recall@1 results from 27.5% to 44.6%. Furthermore, we investigate various aspects of distillation for embeddings, including hint and attention layers, semisupervised learning and cross quality distillation. |
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| Address | Long beach; California; june 2019 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | LAMP; 600.109; 600.120 | Approved | no | ||
| Call Number |
Admin @ si @ YYL2019 | Serial | 3281 | ||
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| Author | Mingyi Yang; Fei Yang; Luka Murn; Marc Gorriz Blanch; Juil Sock; Shuai Wan; Fuzheng Yang; Luis Herranz | ||||
| Title | Task-Switchable Pre-Processor for Image Compression for Multiple Machine Vision Tasks | Type | Journal Article | ||
| Year | 2024 | Publication | IEEE Transactions on Circuits and Systems for Video Technology | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | M Yang, F Yang, L Murn, MG Blanch, J Sock, S Wan, F Yang, L Herranz | ||||
| Abstract | Visual content is increasingly being processed by machines for various automated content analysis tasks instead of being consumed by humans. Despite the existence of several compression methods tailored for machine tasks, few consider real-world scenarios with multiple tasks. In this paper, we aim to address this gap by proposing a task-switchable pre-processor that optimizes input images specifically for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. The proposed task-switchable pre-processor adeptly maintains relevant semantic information based on the specific characteristics of different downstream tasks, while effectively suppressing irrelevant information to reduce bitrate. To enhance the processing of semantic information for diverse tasks, we leverage pre-extracted semantic features to modulate the pixel-to-pixel mapping within the pre-processor. By switching between different modulations, multiple tasks can be seamlessly incorporated into the system. Extensive experiments demonstrate the practicality and simplicity of our approach. It significantly reduces the number of parameters required for handling multiple tasks while still delivering impressive performance. Our method showcases the potential to achieve efficient and effective compression for machine vision tasks, supporting the evolving demands of real-world applications. | ||||
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| Notes | xxx | Approved | no | ||
| Call Number |
Admin @ si @ YYM2024 | Serial | 4007 | ||
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| Author | Vacit Oguz Yazici; Longlong Yu; Arnau Ramisa; Luis Herranz; Joost Van de Weijer | ||||
| Title | Main product detection with graph networks for fashion | Type | Journal Article | ||
| Year | 2024 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 83 | Issue | Pages | 3215–3231 | |
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| Abstract | Computer vision has established a foothold in the online fashion retail industry. Main product detection is a crucial step of vision-based fashion product feed parsing pipelines, focused on identifying the bounding boxes that contain the product being sold in the gallery of images of the product page. The current state-of-the-art approach does not leverage the relations between regions in the image, and treats images of the same product independently, therefore not fully exploiting visual and product contextual information. In this paper, we propose a model that incorporates Graph Convolutional Networks (GCN) that jointly represent all detected bounding boxes in the gallery as nodes. We show that the proposed method is better than the state-of-the-art, especially, when we consider the scenario where title-input is missing at inference time and for cross-dataset evaluation, our method outperforms previous approaches by a large margin. | ||||
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| Notes | LAMP; MACO; 600.147; 600.167; 600.164; 600.161; 600.141; 601.309 | Approved | no | ||
| Call Number |
Admin @ si @ YYR2024 | Serial | 4017 | ||
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| Author | Lu Yu; Lichao Zhang; Joost Van de Weijer; Fahad Shahbaz Khan; Yongmei Cheng; C. Alejandro Parraga | ||||
| Title | Beyond Eleven Color Names for Image Understanding | Type | Journal Article | ||
| Year | 2018 | Publication | Machine Vision and Applications | Abbreviated Journal | MVAP |
| Volume | 29 | Issue | 2 | Pages | 361-373 |
| Keywords | Color name; Discriminative descriptors; Image classification; Re-identification; Tracking | ||||
| Abstract | Color description is one of the fundamental problems of image understanding. One of the popular ways to represent colors is by means of color names. Most existing work on color names focuses on only the eleven basic color terms of the English language. This could be limiting the discriminative power of these representations, and representations based on more color names are expected to perform better. However, there exists no clear strategy to choose additional color names. We collect a dataset of 28 additional color names. To ensure that the resulting color representation has high discriminative power we propose a method to order the additional color names according to their complementary nature with the basic color names. This allows us to compute color name representations with high discriminative power of arbitrary length. In the experiments we show that these new color name descriptors outperform the existing color name descriptor on the task of visual tracking, person re-identification and image classification. | ||||
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| Notes | LAMP; NEUROBIT; 600.068; 600.109; 600.120 | Approved | no | ||
| Call Number |
Admin @ si @ YYW2018 | Serial | 3087 | ||
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| Author | Wenwen Yu; Chengquan Zhang; Haoyu Cao; Wei Hua; Bohan Li; Huang Chen; Mingyu Liu; Mingrui Chen; Jianfeng Kuang; Mengjun Cheng; Yuning Du; Shikun Feng; Xiaoguang Hu; Pengyuan Lyu; Kun Yao; Yuechen Yu; Yuliang Liu; Wanxiang Che; Errui Ding; Cheng-Lin Liu; Jiebo Luo; Shuicheng Yan; Min Zhang; Dimosthenis Karatzas; Xing Sun; Jingdong Wang; Xiang Bai | ||||
| Title | ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | 14188 | Issue | Pages | 536–552 | |
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| Abstract | Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, and the corresponding evaluation protocols usually focus on the submodules of the structured text extraction scheme. In order to eliminate these problems, we organized the ICDAR 2023 competition on Structured text extraction from Visually-Rich Document images (SVRD). We set up two tracks for SVRD including Track 1: HUST-CELL and Track 2: Baidu-FEST, where HUST-CELL aims to evaluate the end-to-end performance of Complex Entity Linking and Labeling, and Baidu-FEST focuses on evaluating the performance and generalization of Zero-shot/Few-shot Structured Text extraction from an end-to-end perspective. Compared to the current document benchmarks, our two tracks of competition benchmark enriches the scenarios greatly and contains more than 50 types of visually-rich document images (mainly from the actual enterprise applications). The competition opened on 30th December, 2022 and closed on 24th March, 2023. There are 35 participants and 91 valid submissions received for Track 1, and 15 participants and 26 valid submissions received for Track 2. In this report we will presents the motivation, competition datasets, task definition, evaluation protocol, and submission summaries. According to the performance of the submissions, we believe there is still a large gap on the expected information extraction performance for complex and zero-shot scenarios. It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI. | ||||
| Address | San Jose; CA; USA; August 2023 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number |
Admin @ si @ YZC2023 | Serial | 3896 | ||
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| Author | Ekaterina Zaytseva; Jordi Vitria | ||||
| Title | A search based approach to non maximum suppression in face detection | Type | Conference Article | ||
| Year | 2012 | Publication | 19th IEEE International Conference on Image Processing | Abbreviated Journal | |
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| Abstract | Poster
paper TA.P5.12 Face detectors typically produce a large number of false positives and this leads to the need to have a further non maximum suppression stage to eliminate multiple and spurious responses. This stage is based on considering spatial heuristics: true positive responses are selected by implicitly considering several restrictions on the spatial distribution of detector responses in natural images. In this paper we analyze the limitations of this approach and propose an efficient search method to overcome them. Results show how the application of this new non-maximum suppression approach to a simple face detector boosts its performance to state of the art results. |
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| Address | Orlando; USA; September 2012 | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1522-4880 | ISBN | 978-1-4673-2534-9 | Medium | |
| Area | Expedition | Conference | ICIP | ||
| Notes | OR;MV | Approved | no | ||
| Call Number |
Admin @ si @ ZaV2012 | Serial | 2060 | ||
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| Author | G. Zahnd; Simone Balocco; A. Serusclat; P. Moulin; M. Orkisz; D. Vray | ||||
| Title | Progressive attenuation of the longitudinal kinetics in the common carotid artery: preliminary in vivo assessment Ultrasound in Medicine and Biology | Type | Journal Article | ||
| Year | 2015 | Publication | Ultrasound in Medicine and Biology | Abbreviated Journal | UMB |
| Volume | 41 | Issue | 1 | Pages | 339-345 |
| Keywords | Arterial stiffness; Atherosclerosis; Common carotid artery; Longitudinal kinetics; Motion tracking; Ultrasound imaging | ||||
| Abstract | Longitudinal kinetics (LOKI) of the arterial wall consists of the shearing motion of the intima-media complex over the adventitia layer in the direction parallel to the blood flow during the cardiac cycle. The aim of this study was to investigate the local variability of LOKI amplitude along the length of the vessel. By use of a previously validated motion-estimation framework, 35 in vivo longitudinal B-mode ultrasound cine loops of healthy common carotid arteries were analyzed. Results indicated that LOKI amplitude is progressively attenuated along the length of the artery, as it is larger in regions located on the proximal side of the image (i.e., toward the heart) and smaller in regions located on the distal side of the image (i.e., toward the head), with an average attenuation coefficient of -2.5 ± 2.0%/mm. Reported for the first time in this study, this phenomenon is likely to be of great importance in improving understanding of atherosclerosis mechanisms, and has the potential to be a novel index of arterial stiffness. | ||||
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| Notes | MILAB | Approved | no | ||
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Admin @ si @ ZBS2014 | Serial | 2556 | ||
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| Author | Benjia Zhou; Zhigang Chen; Albert Clapes; Jun Wan; Yanyan Liang; Sergio Escalera; Zhen Lei; Du Zhang | ||||
| Title | Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining | Type | Conference Article | ||
| Year | 2023 | Publication | IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
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| Abstract | Sign Language Translation (SLT) is a challenging task due to its cross-domain nature, involving the translation of visual-gestural language to text. Many previous methods employ an intermediate representation, i.e., gloss sequences, to facilitate SLT, thus transforming it into a two-stage task of sign language recognition (SLR) followed by sign language translation (SLT). However, the scarcity of gloss-annotated sign language data, combined with the information bottleneck in the mid-level gloss representation, has hindered the further development of the SLT task. To address this challenge, we propose a novel Gloss-Free SLT based on Visual-Language Pretraining (GFSLT-VLP), which improves SLT by inheriting language-oriented prior knowledge from pre-trained models, without any gloss annotation assistance. Our approach involves two stages: (i) integrating Contrastive Language-Image Pre-training (CLIP) with masked self-supervised learning to create pre-tasks that bridge the semantic gap between visual and textual representations and restore masked sentences, and (ii) constructing an end-to-end architecture with an encoder-decoder-like structure that inherits the parameters of the pre-trained Visual Encoder and Text Decoder from the first stage. The seamless combination of these novel designs forms a robust sign language representation and significantly improves gloss-free sign language translation. In particular, we have achieved unprecedented improvements in terms of BLEU-4 score on the PHOENIX14T dataset (>+5) and the CSL-Daily dataset (>+3) compared to state-of-the-art gloss-free SLT methods. Furthermore, our approach also achieves competitive results on the PHOENIX14T dataset when compared with most of the gloss-based methods. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; | Approved | no | ||
| Call Number |
Admin @ si @ ZCC2023 | Serial | 3839 | ||
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| Author | Lichao Zhang; Martin Danelljan; Abel Gonzalez-Garcia; Joost Van de Weijer; Fahad Shahbaz Khan | ||||
| Title | Multi-Modal Fusion for End-to-End RGB-T Tracking | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE International Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2252-2261 | ||
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| Abstract | We propose an end-to-end tracking framework for fusing the RGB and TIR modalities in RGB-T tracking. Our baseline tracker is DiMP (Discriminative Model Prediction), which employs a carefully designed target prediction network trained end-to-end using a discriminative loss. We analyze the effectiveness of modality fusion in each of the main components in DiMP, i.e. feature extractor, target estimation network, and classifier. We consider several fusion mechanisms acting at different levels of the framework, including pixel-level, feature-level and response-level. Our tracker is trained in an end-to-end manner, enabling the components to learn how to fuse the information from both modalities. As data to train our model, we generate a large-scale RGB-T dataset by considering an annotated RGB tracking dataset (GOT-10k) and synthesizing paired TIR images using an image-to-image translation approach. We perform extensive experiments on VOT-RGBT2019 dataset and RGBT210 dataset, evaluating each type of modality fusing on each model component. The results show that the proposed fusion mechanisms improve the performance of the single modality counterparts. We obtain our best results when fusing at the feature-level on both the IoU-Net and the model predictor, obtaining an EAO score of 0.391 on VOT-RGBT2019 dataset. With this fusion mechanism we achieve the state-of-the-art performance on RGBT210 dataset. | ||||
| Address | Seul; Corea; October 2019 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | LAMP; 600.109; 600.141; 600.120 | Approved | no | ||
| Call Number |
Admin @ si @ ZDG2019 | Serial | 3279 | ||
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