2 citations · 2 across the 3 of their papers we have counts for
3 papers
TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment
Bingyi Cao, Koert Chen, Kevis-Kokitsi Maninis +16
Recent progress in vision-language pretraining has enabled significant improvements to many downstream computer vision applications, such as classification, retrieval, segmentation…
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning
Dongmin Park, Zhaofang Qian, Guangxing Han +1
Mitigating hallucinations of Large Vision Language Models,(LVLMs) is crucial to enhance their reliability for general-purpose assistants. This paper shows that such hallucinations…
Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model
Shraman Pramanick, Guangxing Han, Rui Hou +6
The ability of large language models (LLMs) to process visual inputs has given rise to general-purpose vision systems, unifying various vision-language (VL) tasks by instruction tu…