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Rishabh Lalla

2 accepted papers

2026

Foundation Model Priors Enhance Object Focus in Feature Space for Source-Free Object Detection

CVPR 2026

Current state-of-the-art approaches in Source-Free Object Detection (SFOD) typically rely on Mean-Teacher self-labeling. However, domain shift often reduces the detector's ability to maintain strong object-focused representations, causing high-confidence activations over background clutter and unrel

Cited by 0SourcecodeScholar
2024

Improving Unsupervised Domain Adaptation: A Pseudo-Candidate Set Approach

ECCV 2024poster

"Unsupervised domain adaptation (UDA) is a critical challenge in machine learning, aiming to transfer knowledge from a labeled source domain to an unlabeled target domain. In this work, we aim to improve target set accuracy in any existing UDA method by introducing an approach that utilizes pseudo-c…

Cited by 1SourcePDFScholar