5 papers
Temporal Prompting Matters: Rethinking Referring Video Object Segmentation
Ci-Siang Lin, Min-Hung Chen, I-Jieh Liu +3
Referring Video Object Segmentation (RVOS) aims to segment the object referred to by the query sentence in the video. Most existing methods require end-to-end training with dense m…
LEAML: Label-Efficient Adaptation to Out-of-Distribution Visual Tasks for Multimodal Large Language Models
Ci-Siang Lin, Min-Hung Chen, Yu-Yang Sheng +1
Multimodal Large Language Models (MLLMs) have achieved strong performance on general visual benchmarks but struggle with out-of-distribution (OOD) tasks in specialized domains such…
Continual Personalization for Diffusion Models
Yu-Chien Liao, Jr-Jen Chen, Chi-Pin Huang +3
Updating diffusion models in an incremental setting would be practical in real-world applications yet computationally challenging. We present a novel learning strategy of Concept N…
Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation
Ci-Siang Lin, Chien-Yi Wang, Yu-Chiang Frank Wang +1
Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation models using image data with only image-level supervision. Since precise pixel-level annotations are not a…
GroPrompt: Efficient Grounded Prompting and Adaptation for Referring Video Object Segmentation
Ci-Siang Lin, I-Jieh Liu, Min-Hung Chen +3
Referring Video Object Segmentation (RVOS) aims to segment the object referred to by the query sentence throughout the entire video. Most existing methods require end-to-end traini…