4 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…
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…
GSNeRF: Generalizable Semantic Neural Radiance Fields with Enhanced 3D Scene Understanding
Zi-Ting Chou, Sheng-Yu Huang, I-Jieh Liu +1
Utilizing multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a popular research topic in 3D vision. In this work, we introduce a Gener…
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…