10 papers
DroneFINE: Domain-Aware Parameter-Efficient Fine-Tuning of Vision-Language Detectors for Drone Images
Ke Wu, Yanan Zhang, Yingjie Gao +5
Object detection for Unmanned Aerial Vehicles (UAVs) working in open and dynamic environments is a highly challenging task. While Vision-Language Models (VLMs) have offered a power…
TreeGaussian: Tree-Guided Cascaded Contrastive Learning for Hierarchical Consistent 3D Gaussian Scene Segmentation and Understanding
Jingbin You, Zehao Li, Hao Jiang +8
3D Gaussian Splatting (3DGS) has emerged as a real-time, differentiable representation for neural scene understanding. However, existing 3DGS-based methods struggle to represent hi…
Towards Unbiased Source-Free Object Detection via Vision Foundation Models
Zhi Cai, Yingjie Gao, Yanan Zhang +2
Source-Free Object Detection (SFOD) has garnered much attention in recent years by eliminating the need of source-domain data in cross-domain tasks, but existing SFOD methods suffe…
Towards an Effective Action-Region Tracking Framework for Fine-grained Video Action Recognition
Baoli Sun, Yihan Wang, Xinzhu Ma +3
Fine-grained action recognition (FGAR) aims to identify subtle and distinctive differences among fine-grained action categories. However, current recognition methods often capture…
Referring Video Object Segmentation with Cross-Modality Proxy Queries
Baoli Sun, Xinzhu Ma, Ning Wang +2
Referring video object segmentation (RVOS) is an emerging cross-modality task that aims to generate pixel-level maps of the target objects referred by given textual expressions. Th…
Propagating Sparse Depth via Depth Foundation Model for Out-of-Distribution Depth Completion
Shenglun Chen, Xinzhu Ma, Hong Zhang +2
Depth completion is a pivotal challenge in computer vision, aiming at reconstructing the dense depth map from a sparse one, typically with a paired RGB image. Existing learning bas…