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20212025
most citedLST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning

79 citations · 92 across the 14 of their papers we have counts for

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7 papers · 1 filter

cs.CV2024

DAM: Dynamic Adapter Merging for Continual Video QA Learning

Feng Cheng, Ziyang Wang, Yi-Lin Sung +3

We present a parameter-efficient method for continual video question-answering (VidQA) learning. Our method, named DAM, uses the proposed Dynamic Adapter Merging to (i) mitigate ca…

cs.CV2024

SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data

Jialu Li, Jaemin Cho, Yi-Lin Sung +2

Recent text-to-image (T2I) generation models have demonstrated impressive capabilities in creating images from text descriptions. However, these T2I generation models often fall sh…

cs.CV2023★ 1 cited

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…

cs.CV2023★ 2 cited

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…

cs.CV2023★ 1 cited

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…

cs.CV2022★ 2 cited

Vision Transformers are Parameter-Efficient Audio-Visual Learners

Yan-Bo Lin, Yi-Lin Sung, Jie Lei +2

Vision transformers (ViTs) have achieved impressive results on various computer vision tasks in the last several years. In this work, we study the capability of frozen ViTs, pretra…

cs.CV2021

VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks

Yi-Lin Sung, Jaemin Cho, Mohit Bansal

Recently, fine-tuning language models pre-trained on large text corpora have provided huge improvements on vision-and-language (V&L) tasks as well as on pure language tasks. Howeve…