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Author Aymen Azaza; Joost Van de Weijer; Ali Douik; Marc Masana edit   pdf
url  openurl
  Title Context Proposals for Saliency Detection Type Journal Article
  Year 2018 Publication Computer Vision and Image Understanding Abbreviated Journal (up) CVIU  
  Volume 174 Issue Pages 1-11  
  Keywords  
  Abstract One of the fundamental properties of a salient object region is its contrast
with the immediate context. The problem is that numerous object regions
exist which potentially can all be salient. One way to prevent an exhaustive
search over all object regions is by using object proposal algorithms. These
return a limited set of regions which are most likely to contain an object. Several saliency estimation methods have used object proposals. However, they focus on the saliency of the proposal only, and the importance of its immediate context has not been evaluated.
In this paper, we aim to improve salient object detection. Therefore, we extend object proposal methods with context proposals, which allow to incorporate the immediate context in the saliency computation. We propose several saliency features which are computed from the context proposals. In the experiments, we evaluate five object proposal methods for the task of saliency segmentation, and find that Multiscale Combinatorial Grouping outperforms the others. Furthermore, experiments show that the proposed context features improve performance, and that our method matches results on the FT datasets and obtains competitive results on three other datasets (PASCAL-S, MSRA-B and ECSSD).		  
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  Notes LAMP; 600.109; 600.109; 600.120 Approved no  
  Call Number Admin @ si @ AWD2018 Serial 3241  
Permanent link to this record
 
 
Author Yaxing Wang; Abel Gonzalez-Garcia; Luis Herranz; Joost Van de Weijer edit   pdf
url  openurl
  Title Controlling biases and diversity in diverse image-to-image translation Type Journal Article
  Year 2021 Publication Computer Vision and Image Understanding Abbreviated Journal (up) CVIU  
  Volume 202 Issue Pages 103082  
  Keywords  
  Abstract JCR 2019 Q2, IF=3.121
The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT), an even more challenging setting in which an image can have multiple plausible translations. This is normally achieved by explicitly disentangling content and style in the latent representation and sampling different styles codes while maintaining the image content. Despite the success of current DIT models, they are prone to suffer from bias. In this paper, we study the problem of bias in image-to-image translation. Biased datasets may add undesired changes (e.g. change gender or race in face images) to the output translations as a consequence of the particular underlying visual distribution in the target domain. In order to alleviate the effects of this problem we propose the use of semantic constraints that enforce the preservation of desired image properties. Our proposed model is a step towards unbiased diverse image-to-image translation (UDIT), and results in less unwanted changes in the translated images while still performing the wanted transformation. Experiments on several heavily biased datasets show the effectiveness of the proposed techniques in different domains such as faces, objects, and scenes.		  
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  Notes LAMP; 600.141; 600.109; 600.147 Approved no  
  Call Number Admin @ si @ WGH2021 Serial 3464  
Permanent link to this record
 
 
Author Shiqi Yang; Yaxing Wang; Luis Herranz; Shangling Jui; Joost Van de Weijer edit  url
openurl 
  Title Casting a BAIT for offline and online source-free domain adaptation Type Journal Article
  Year 2023 Publication Computer Vision and Image Understanding Abbreviated Journal (up) CVIU  
  Volume 234 Issue Pages 103747  
  Keywords  
  Abstract We address the source-free domain adaptation (SFDA) problem, where only the source model is available during adaptation to the target domain. We consider two settings: the offline setting where all target data can be visited multiple times (epochs) to arrive at a prediction for each target sample, and the online setting where the target data needs to be directly classified upon arrival. Inspired by diverse classifier based domain adaptation methods, in this paper we introduce a second classifier, but with another classifier head fixed. When adapting to the target domain, the additional classifier initialized from source classifier is expected to find misclassified features. Next, when updating the feature extractor, those features will be pushed towards the right side of the source decision boundary, thus achieving source-free domain adaptation. Experimental results show that the proposed method achieves competitive results for offline SFDA on several benchmark datasets compared with existing DA and SFDA methods, and our method surpasses by a large margin other SFDA methods under online source-free domain adaptation setting.  
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  Notes LAMP; MACO Approved no  
  Call Number Admin @ si @ YWH2023 Serial 3874  
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Author Idoia Ruiz; Bogdan Raducanu; Rakesh Mehta; Jaume Amores edit   pdf
url  openurl
  Title Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation Type Journal Article
  Year 2020 Publication Engineering Applications of Artificial Intelligence Abbreviated Journal (up) EAAI  
  Volume 87 Issue Pages 103309  
  Keywords Person re-identification; Network distillation; Image retrieval; Model compression; Surveillance  
  Abstract Finding a person across a camera network plays an important role in video surveillance. For a real-world person re-identification application, in order to guarantee an optimal time response, it is crucial to find the balance between accuracy and speed. We analyse this trade-off, comparing a classical method, that comprises hand-crafted feature description and metric learning, in particular, LOMO and XQDA, to deep learning based techniques, using image classification networks, ResNet and MobileNets. Additionally, we propose and analyse network distillation as a learning strategy to reduce the computational cost of the deep learning approach at test time. We evaluate both methods on the Market-1501 and DukeMTMC-reID large-scale datasets, showing that distillation helps reducing the computational cost at inference time while even increasing the accuracy performance.  
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  Notes LAMP; 600.109; 600.120 Approved no  
  Call Number Admin @ si @ RRM2020 Serial 3401  
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Author Qingshan Chen; Zhenzhen Quan; Yujun Li; Chao Zhai; Mikhail Mozerov edit  url
doi  openurl
  Title An Unsupervised Domain Adaption Approach for Cross-Modality RGB-Infrared Person Re-Identification Type Journal Article
  Year 2023 Publication IEEE Sensors Journal Abbreviated Journal (up) IEEE-SENS  
  Volume 23 Issue 24 Pages  
  Keywords Q. Chen, Z. Quan, Y. Li, C. Zhai and M. G. Mozerov  
  Abstract Dual-camera systems commonly employed in surveillance serve as the foundation for RGB-infrared (IR) cross-modality person re-identification (ReID). However, significant modality differences give rise to inferior performance compared to single-modality scenarios. Furthermore, most existing studies in this area rely on supervised training with meticulously labeled datasets. Labeling RGB-IR image pairs is more complex than labeling conventional image data, and deploying pretrained models on unlabeled datasets can lead to catastrophic performance degradation. In contrast to previous solutions that focus solely on cross-modality or domain adaptation issues, this article presents an end-to-end unsupervised domain adaptation (UDA) framework for the cross-modality person ReID, which can simultaneously address both of these challenges. This model employs source domain classes, target domain clusters, and unclustered instance samples for the training, maximizing the comprehensive use of the dataset. Moreover, it addresses the problem of mismatched clustering labels between the two modalities in the target domain by incorporating a label matching module that reassigns reliable clusters with labels, ensuring correspondence between different modality labels. We construct the loss function by incorporating distinctiveness loss and multiplicity loss, both of which are determined by the similarity of neighboring features in the predicted feature space and the difference between distant features. This approach enables efficient feature clustering and cluster class assignment to occur concurrently. Eight UDA cross-modality person ReID experiments are conducted on three real datasets and six synthetic datasets. The experimental results unequivocally demonstrate that the proposed model outperforms the existing state-of-the-art algorithms to a significant degree. Notably, in RegDB → RegDB_light, the Rank-1 accuracy exhibits a remarkable improvement of 8.24%.  
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  Notes LAMP Approved no  
  Call Number Admin @ si @ CQL2023 Serial 3884  
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