Post-hoc Probabilistic Vision-Language Models
Vision-language models (VLMs), such as CLIP and SigLIP, have found remarkable success
in classification, retrieval, and generative tasks. For this, VLMs deterministically map images …
in classification, retrieval, and generative tasks. For this, VLMs deterministically map images …
Active Prompt Learning with Vision-Language Model Priors
Vision-language models (VLMs) have demonstrated remarkable zero-shot performance
across various classification tasks. Nonetheless, their reliance on hand-crafted text prompts …
across various classification tasks. Nonetheless, their reliance on hand-crafted text prompts …
Parameter-Efficient Active Learning for Foundational models
Foundational vision transformer models have shown impressive few shot performance on
many vision tasks. This research presents a novel investigation into the application of …
many vision tasks. This research presents a novel investigation into the application of …
ACTIVE TEST TIME PROMPT LEARNING IN VISION-LANGUAGE MODELS
Test Time Optimisation is a setting where a model is made to learn new parameters on-the-
fly during inference with the help of those very samples it is supposed to be tested on …
fly during inference with the help of those very samples it is supposed to be tested on …
Probabilistic Active Few-Shot Learning in Vision-Language Models
Pre-trained vision-language models (VLMs) have shown to be an useful model class for
zero-and few-shot learning tasks. In this work, we investigate probabilistic active few-shot …
zero-and few-shot learning tasks. In this work, we investigate probabilistic active few-shot …