29 citations · 31 across the 11 of their papers we have counts for
12 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…
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…
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…
Language-Guided Transformer for Federated Multi-Label Classification
I-Jieh Liu, Ci-Siang Lin, Fu-En Yang +1
Federated Learning (FL) is an emerging paradigm that enables multiple users to collaboratively train a robust model in a privacy-preserving manner without sharing their private dat…
Frequency-Aware Self-Supervised Long-Tailed Learning
Ci-Siang Lin, Min-Hung Chen, Yu-Chiang Frank Wang
Data collected from the real world typically exhibit long-tailed distributions, where frequent classes contain abundant data while rare ones have only a limited number of samples.…
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains
Yuan-Chia Cheng, Ci-Siang Lin, Fu-En Yang +1
Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest. Though several approaches have been proposed, most existing…
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation
Zu-Yun Shiau, Wei-Wei Lin, Ci-Siang Lin +1
How to handle domain shifts when recognizing or segmenting visual data across domains has been studied by learning and vision communities. In this paper, we address domain generali…
Semantics-Guided Representation Learning with Applications to Visual Synthesis
Jia-Wei Yan, Ci-Siang Lin, Fu-En Yang +2
Learning interpretable and interpolatable latent representations has been an emerging research direction, allowing researchers to understand and utilize the derived latent space fo…
Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning
Ci-Siang Lin, Yuan-Chia Cheng, Yu-Chiang Frank Wang
Aiming at recognizing images of the same person across distinct camera views, person re-identification (re-ID) has been among active research topics in computer vision. Most existi…
Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation
Yu-Jhe Li, Ci-Siang Lin, Yan-Bo Lin +1
Person re-identification (re-ID) aims at recognizing the same person from images taken across different cameras. To address this challenging task, existing re-ID models typically r…