211 citations · 490 across the 13 of their papers we have counts for
9 papers · 1 filter
GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradients
Sima Behpour, Thang Doan, Xin Li +3
Detecting out-of-distribution (OOD) data is crucial for ensuring the safe deployment of machine learning models in real-world applications. However, existing OOD detection approach…
UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models
Xin Li, Sima Behpour, Thang Doan +3
In this study, we investigate the task of data pre-selection, which aims to select instances for labeling from an unlabeled dataset through a single pass, thereby optimizing perfor…
Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detection
Thang Doan, Xin Li, Sima Behpour +3
Open World Object Detection (OWOD) is a challenging and realistic task that extends beyond the scope of standard Object Detection task. It involves detecting both known and unknown…
CLIP-S: Language-Guided Self-Supervised Semantic Segmentation
Wenbin He, Suphanut Jamonnak, Liang Gou +1
Existing semantic segmentation approaches are often limited by costly pixel-wise annotations and predefined classes. In this work, we present CLIP-S that leverages self-supervi…
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation
Yuyan Li, Zhixin Yan, Ye Duan +1
Omnidirectional 3D information is essential for a wide range of applications such as Virtual Reality, Autonomous Driving, Robotics, etc. In this paper, we propose a novel, model-ag…
Novelty-based Generalization Evaluation for Traffic Light Detection
Arvind Kumar Shekar, Laureen Lake, Liang Gou +1
The advent of Convolutional Neural Networks (CNNs) has led to their application in several domains. One noteworthy application is the perception system for autonomous driving that…