16 citations · 35 across the 12 of their papers we have counts for
13 papers
Certainty and Uncertainty Guided Active Domain Adaptation
Bardia Safaei, Vibashan VS, Vishal M. Patel
Active Domain Adaptation (ADA) adapts models to target domains by selectively labeling a few target samples. Existing ADA methods prioritize uncertain samples but overlook confiden…
Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models
Zhiqi Li, Guo Chen, Shilong Liu +24
Recently, promising progress has been made by open-source vision-language models (VLMs) in bringing their capabilities closer to those of proprietary frontier models. However, most…
Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models
Yasiru Ranasinghe, Vibashan VS, James Uplinger +2
Automatic target recognition (ATR) plays a critical role in tasks such as navigation and surveillance, where safety and accuracy are paramount. In extreme use cases, such as milita…
SegFace: Face Segmentation of Long-Tail Classes
Kartik Narayan, Vibashan VS, Vishal M. Patel
Face parsing refers to the semantic segmentation of human faces into key facial regions such as eyes, nose, hair, etc. It serves as a prerequisite for various advanced applications…
PosSAM: Panoptic Open-vocabulary Segment Anything
Vibashan VS, Shubhankar Borse, Hyojin Park +4
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…
FaceXFormer: A Unified Transformer for Facial Analysis
Kartik Narayan, Vibashan VS, Rama Chellappa +1
In this work, we introduce FaceXFormer, an end-to-end unified transformer model capable of performing ten facial analysis tasks within a single framework. These tasks include face…
Entropic Open-set Active Learning
Bardia Safaei, Vibashan VS, Celso M. de Melo +1
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 perfo…
Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations
Vibashan VS, Ning Yu, Chen Xing +5
Existing instance segmentation models learn task-specific information using manual mask annotations from base (training) categories. These mask annotations require tremendous human…
On-the-Fly Test-time Adaptation for Medical Image Segmentation
Jeya Maria Jose Valanarasu, Pengfei Guo, Vibashan VS +1
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 tr…
Target and Task specific Source-Free Domain Adaptive Image Segmentation
Vibashan VS, Jeya Maria Jose Valanarasu, Vishal M. Patel
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 p…
Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning
Vibashan VS, Domenick Poster, Suya You +2
Object detectors trained on large-scale RGB datasets are being extensively employed in real-world applications. However, these RGB-trained models suffer a performance drop under ad…
Image Fusion Transformer
Vibashan VS, Jeya Maria Jose Valanarasu, Poojan Oza +1
In image fusion, images obtained from different sensors are fused to generate a single image with enhanced information. In recent years, state-of-the-art methods have adopted Convo…
MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection
Vibashan VS, Vikram Gupta, Poojan Oza +2
Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training. While these methods achieve reasonable improvements in per…