Chetan Arora

Prototype-Guided Pseudo-Labeling with Neighborhood-Aware Consistency for Unsupervised Adaptation

In unsupervised adaptation for vision-language models such as CLIP, pseudo-labels derived from zero-shot predictions often exhibit significant noise, particularly under domain shifts or in visually complex scenarios. Conventional pseudo-label filtering approaches, which rely on fixed confidence thresholds, tend to be u...

RCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods

What Can Low Resource Languages Learn From Each Other?

This work proposes PSMC (Pre-train, Specialize, Merge, Merge, and Co-train), a data-efficient framework that capitalizes on a cross-script "transfer effect" to fuse language-specific experts into a unified, high-performance multilingual back- bone.