2 citations · 5 across the 9 of their papers we have counts for
9 papers
GUIDED: Granular Understanding via Identification, Detection, and Discrimination for Fine-Grained Open-Vocabulary Object Detection
Jiaming Li, Zhijia Liang, Weikai Chen +2
Fine-grained open-vocabulary object detection (FG-OVD) aims to detect novel object categories described by attribute-rich texts. While existing open-vocabulary detectors show promi…
3D Weakly Supervised Semantic Segmentation via Class-Aware and Geometry-Guided Pseudo-Label Refinement
Xiaoxu Xu, Xuexun Liu, Jinlong Li +5
3D weakly supervised semantic segmentation (3D WSSS) aims to achieve semantic segmentation by leveraging sparse or low-cost annotated data, significantly reducing reliance on dense…
Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation
Jinlong Li, Dong Zhao, Qi Zang +3
Continual Test Time Adaptation (CTTA) is a task that requires a source pre-trained model to continually adapt to new scenarios with changing target distributions. Existing CTTA met…
LESS: Label-Efficient and Single-Stage Referring 3D Segmentation
Xuexun Liu, Xiaoxu Xu, Jinlong Li +4
Referring 3D Segmentation is a visual-language task that segments all points of the specified object from a 3D point cloud described by a sentence of query. Previous works perform…
VidCompress: Memory-Enhanced Temporal Compression for Video Understanding in Large Language Models
Xiaohan Lan, Yitian Yuan, Zequn Jie +1
Video-based multimodal large language models (Video-LLMs) possess significant potential for video understanding tasks. However, most Video-LLMs treat videos as a sequential set of…
3D Weakly Supervised Semantic Segmentation with 2D Vision-Language Guidance
Xiaoxu Xu, Yitian Yuan, Jinlong Li +6
In this paper, we propose 3DSS-VLG, a weakly supervised approach for 3D Semantic Segmentation with 2D Vision-Language Guidance, an alternative approach that a 3D model predicts den…
Making Large Language Models Better Planners with Reasoning-Decision Alignment
Zhijian Huang, Tao Tang, Shaoxiang Chen +5
Data-driven approaches for autonomous driving (AD) have been widely adopted in the past decade but are confronted with dataset bias and uninterpretability. Inspired by the knowledg…
OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion
Hao Wang, Pengzhen Ren, Zequn Jie +8
Open-vocabulary detection is a challenging task due to the requirement of detecting objects based on class names, including those not encountered during training. Existing methods…
Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization
Jinlong Li, Dong Zhao, Zequn Jie +3
Efficient fine-tuning of vision-language models (VLMs) like CLIP for specific downstream tasks is gaining significant attention. Previous works primarily focus on prompt learning t…