5 papers
Dynamic Context Adapters: Efficiently Infusing History into Vision-and-Language Models
Yuhang Song, Bor-Jiun Lin, Jiaxu Liu +3
Historical context integration presents a fundamental challenge for Vision-Language Models (VLMs) in sequential decision-making tasks. Current VLMs process visual inputs independen…
Landscape-Awareness for Geometric View Diffusion Model
Yan-Ting Chen, Hao-Wei Chen, Tsu-Ching Hsiao +1
Accurate camera viewpoint estimation under sparse-view conditions remains challenging, particularly in two-view scenarios. Recent approaches leverage diffusion models such as Zero1…
ALICE: A Multifaceted Evaluation Framework of Large Audio-Language Models' In-Context Learning Ability
Yen-Ting Piao, Jay Chiehen Liao, Wei-Tang Chien +5
While Large Audio-Language Models (LALMs) have been shown to exhibit degraded instruction-following capabilities, their ability to infer task patterns from in-context examples unde…
MVA 2025 Small Multi-Object Tracking for Spotting Birds Challenge: Dataset, Methods, and Results
Yuki Kondo, Norimichi Ukita, Riku Kanayama +21
Small Multi-Object Tracking (SMOT) is particularly challenging when targets occupy only a few dozen pixels, rendering detection and appearance-based association unreliable. Buildin…
EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling
Jia-Hua Lee, Bor-Jiun Lin, Wei-Fang Sun +1
World models represent a promising approach for training reinforcement learning agents with significantly improved sample efficiency. While most world model methods primarily rely…