5 papers
Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding
Chaoyou Fu, Haozhi Yuan, Yuhao Dong +16
With the rapid advancement of video understanding, existing benchmarks are becoming increasingly saturated, exposing a critical discrepancy between inflated leaderboard scores and…
CrossEarth-SAR: A SAR-Centric and Billion-Scale Geospatial Foundation Model for Domain Generalizable Semantic Segmentation
Ziqi Ye, Ziyang Gong, Ning Liao +10
Synthetic Aperture Radar (SAR) enables global, all-weather earth observation. However, owing to diverse imaging mechanisms, domain shifts across sensors and regions severely hinder…
ProCLIP: Progressive Vision-Language Alignment via LLM-based Embedder
Xiaoxing Hu, Kaicheng Yang, Ziyang Gong +6
The original CLIP text encoder is limited by a maximum input length of 77 tokens, which hampers its ability to effectively process long texts and perform fine-grained semantic unde…
Decoupled Global-Local Alignment for Improving Compositional Understanding
Xiaoxing Hu, Kaicheng Yang, Jun Wang +3
Contrastive Language-Image Pre-training (CLIP) has achieved success on multiple downstream tasks by aligning image and text modalities. However, the nature of global contrastive le…
Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency Adaptation
Xiaoxing Hu, Ziyang Gong, Yupei Wang +8
Parameter-Efficient Fine-Tuning (PEFT) is a technique that allows us to adapt powerful Foundation Models (FMs) to diverse downstream tasks while preserving and unleashing their inh…