79 citations · 92 across the 14 of their papers we have counts for
4 papers · 1 filter
ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models
Yi-Lin Sung, Jaehong Yoon, Mohit Bansal
Large Vision-Language Models (LVLMs) can understand the world comprehensively by integrating rich information from different modalities, achieving remarkable advancements on variou…
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy
Pingzhi Li, Zhenyu Zhang, Prateek Yadav +4
Sparsely activated Mixture-of-Experts (SMoE) has shown promise to scale up the learning capacity of neural networks, however, they have issues like (a) High Memory Usage, due to du…
Unified Coarse-to-Fine Alignment for Video-Text Retrieval
Ziyang Wang, Yi-Lin Sung, Feng Cheng +2
The canonical approach to video-text retrieval leverages a coarse-grained or fine-grained alignment between visual and textual information. However, retrieving the correct video ac…
An Empirical Study of Multimodal Model Merging
Yi-Lin Sung, Linjie Li, Kevin Lin +3
Model merging (e.g., via interpolation or task arithmetic) fuses multiple models trained on different tasks to generate a multi-task solution. The technique has been proven success…