5 citations · 8 across the 23 of their papers we have counts for
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cs.CV2023★ 1 cited
Enhancing Multimodal Compositional Reasoning of Visual Language Models with Generative Negative Mining
Ugur Sahin, Hang Li, Qadeer Khan +2
Contemporary large-scale visual language models (VLMs) exhibit strong representation capacities, making them ubiquitous for enhancing image and text understanding tasks. They are o…
cs.CV2023★ 5 cited
Can Multimodal Large Language Models Truly Perform Multimodal In-Context Learning?
Shuo Chen, Zhen Han, Bailan He +6
Large Language Models (LLMs) with in-context learning (ICL) ability can quickly adapt to a specific context given a few demonstrations (demos). Recently, Multimodal Large Language…
cs.CV2023★ 1 cited
Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation
Hang Li, Chengzhi Shen, Philip Torr +2
Diffusion-based models have gained significant popularity for text-to-image generation due to their exceptional image-generation capabilities. A risk with these models is the poten…