Zobrazeno 1 - 10
of 32
pro vyhledávání: '"VS, Vibashan"'
In this work, we introduce FaceXformer, an end-to-end unified transformer model for a comprehensive range of facial analysis tasks such as face parsing, landmark detection, head pose estimation, attributes recognition, and estimation of age, gender,
Externí odkaz:
http://arxiv.org/abs/2403.12960
Autor:
VS, Vibashan, Borse, Shubhankar, Park, Hyojin, Das, Debasmit, Patel, Vishal, Hayat, Munawar, Porikli, Fatih
In this paper, we introduce an open-vocabulary panoptic segmentation model that effectively unifies the strengths of the Segment Anything Model (SAM) with the vision-language CLIP model in an end-to-end framework. While SAM excels in generating spati
Externí odkaz:
http://arxiv.org/abs/2403.09620
Active Learning (AL) aims to enhance the performance of deep models by selecting the most informative samples for annotation from a pool of unlabeled data. Despite impressive performance in closed-set settings, most AL methods fail in real-world scen
Externí odkaz:
http://arxiv.org/abs/2312.14126
Autor:
VS, Vibashan, Yu, Ning, Xing, Chen, Qin, Can, Gao, Mingfei, Niebles, Juan Carlos, Patel, Vishal M., Xu, Ran
Existing instance segmentation models learn task-specific information using manual mask annotations from base (training) categories. These mask annotations require tremendous human effort, limiting the scalability to annotate novel (new) categories.
Externí odkaz:
http://arxiv.org/abs/2303.16891
Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors. ATR algorithms are extensively used in real-world scenarios such as military and surveillance
Externí odkaz:
http://arxiv.org/abs/2211.05883
Existing object detection models assume both the training and test data are sampled from the same source domain. This assumption does not hold true when these detectors are deployed in real-world applications, where they encounter new visual domain.
Externí odkaz:
http://arxiv.org/abs/2204.05289
Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift. Specifically, UDA methods try to align the source and target representations to improve the generalization on the target domain. Further, UDA methods w
Externí odkaz:
http://arxiv.org/abs/2203.15793
Solving the domain shift problem during inference is essential in medical imaging, as most deep-learning based solutions suffer from it. In practice, domain shifts are tackled by performing Unsupervised Domain Adaptation (UDA), where a model is adapt
Externí odkaz:
http://arxiv.org/abs/2203.15792
One major problem in deep learning-based solutions for medical imaging is the drop in performance when a model is tested on a data distribution different from the one that it is trained on. Adapting the source model to target data distribution at tes
Externí odkaz:
http://arxiv.org/abs/2203.05574
Representation learning of the task-oriented attention while tracking instrument holds vast potential in image-guided robotic surgery. Incorporating cognitive ability to automate the camera control enables the surgeon to concentrate more on dealing w
Externí odkaz:
http://arxiv.org/abs/2112.08189