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Jul 1, 2023 · We propose ProbVLM, a probabilistic adapter that estimates probability distributions for the embeddings of pre-trained VLMs via inter/intra-modal alignment.
ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models
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A probabilistic adapter that estimates probability distributions for the embeddings of pre-trained VLMs via inter/intra-modal alignment in a post-hoc manner.
We propose ProbVLM, a post-hoc probabilistic adapter, the first method to convert the deterministic embeddings provided by a frozen large-scale vision-language ...
We discuss Equation 4 from the main paper and how we simplify the same to obtain a loss function suitable for training deep learning models.
Missing: ProbLEM: | Show results with:ProbLEM:
We propose ProbVLM, a probabilistic adapter that estimates probability distributions for the embeddings of pre-trained VLMs via inter/intra-modal alignment.
Jul 1, 2023 · We propose incorporating active learning into dense regression models to address this problem. Active learning allows models to select the ...
Jul 1, 2023 · ProbVLM, a probabilistic adapter that estimates probability distributions for the embeddings of pre-trained VLMs via inter/intra-modal ...
Missing: ProbLEM: | Show results with:ProbLEM:
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To this end, Upadhyay et al. [38] proposed a post-hoc method called ProbVLM that learns probabilistic embeddings from finetuned adapters on a frozen VLM ...
Oct 28, 2024 · We propose a novel framework that aligns human action knowledge and VLP knowledge in a probabilistic embedding space.
Dec 12, 2024 · Vision-language models (VLMs), such as CLIP and SigLIP, have found remarkable success in classification, retrieval, and generative tasks.