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cs.CL2024
Addressing Blind Guessing: Calibration of Selection Bias in Multiple-Choice Question Answering by Video Language Models
Olga Loginova, Oleksandr Bezrukov, Ravi Shekhar +1
Evaluating Video Language Models (VLMs) is a challenging task. Due to its transparency, Multiple-Choice Question Answering (MCQA) is widely used to measure the performance of these…
cs.CV2024
CLIP Adaptation by Intra-modal Overlap Reduction
Alexey Kravets, Vinay Namboodiri
Numerous methods have been proposed to adapt a pre-trained foundational CLIP model for few-shot classification. As CLIP is trained on a large corpus, it generalises well through ad…
cs.CV2024
Zero-Shot Class Unlearning in CLIP with Synthetic Samples
A. Kravets, V. Namboodiri
Machine unlearning is a crucial area of research. It is driven by the need to remove sensitive information from models to safeguard individuals' right to be forgotten under rigorou…