19 citations · 41 across the 19 of their papers we have counts for
19 papers
CrACK: Adversarial Attacks on Cross-Model Consistency in Collaborative Vision Foundation Models
Feifei Liu, Jintao Cheng, Chi Man Vong +1
Training-free collaborative pipelines that integrate Vision Foundation Models such as CLIP, SAM, and DINO achieve strong open-vocabulary dense prediction and are increasingly deplo…
Test-Time Prototype Adaptation for Open-Vocabulary Semantic Segmentation
Haozhe Wang, Jintao Cheng, Weibin Li +1
Open-vocabulary semantic segmentation (OVSS) repurposes a pretrained CLIP encoder for dense prediction without additional labeled supervision. Existing methods improve CLIP's spati…
SIPTraj: Map-Free End-to-End Trajectory Prediction via Physics-Guided Scene Interaction
Feifei Liu, Zejun Wei, Haozhe Wang +6
Trajectory prediction of surrounding agents is a prerequisite for safe planning and decision making in autonomous driving. Without high-definition (HD) maps, sensor-derived bird's-…
OptiSAR-Net++: A Large-Scale Benchmark and Transformer-Free Framework for Cross-Domain Remote Sensing Visual Grounding
Xiaoyu Tang, Jun Dong, Jintao Cheng +1
Remote sensing visual grounding (RSVG) aims to localize specific targets in remote sensing images using natural language expressions. However, existing methods are restricted to si…
Training-Free Interaction-Aligned Visual Token Pruning for Efficient Embodied Manipulation
Jintao Cheng, Weibin Li, Haozhe Wang +7
Efficient visual representation is a central image-processing challenge in embodied manipulation, where policies repeatedly process dense visual-token sequences during closed-loop…
Decoding Ambiguous Emotions with Test-Time Scaling in Audio-Language Models
Hong Jia, Weibin Li, Jingyao Wu +6
Emotion recognition from human speech is a critical enabler for socially aware conversational AI. However, while most prior work frames emotion recognition as a categorical classif…
Scale, Don't Fine-tune: Guiding Multimodal LLMs for Efficient Visual Place Recognition at Test-Time
Jintao Cheng, Weibin Li, Jiehao Luo +5
Visual Place Recognition (VPR) has evolved from handcrafted descriptors to deep learning approaches, yet significant challenges remain. Current approaches, including Vision Foundat…
A Pseudo Global Fusion Paradigm-Based Cross-View Network for LiDAR-Based Place Recognition
Jintao Cheng, Jiehao Luo, Xieyuanli Chen +4
LiDAR-based Place Recognition (LPR) remains a critical task in Embodied Artificial Intelligence (AI) and Autonomous Driving, primarily addressing localization challenges in GPS-den…
KDMOS:Knowledge Distillation for Motion Segmentation
Chunyu Cao, Jintao Cheng, Zeyu Chen +4
Motion Object Segmentation (MOS) is crucial for autonomous driving, as it enhances localization, path planning, map construction, scene flow estimation, and future state prediction…
Single-Frame Point-Pixel Registration via Supervised Cross-Modal Feature Matching
Yu Han, Zhiwei Huang, Yanting Zhang +5
Point-pixel registration between LiDAR point clouds and camera images is a fundamental yet challenging task in autonomous driving and robotic perception. A key difficulty lies in t…
You Sense Only Once Beneath: Ultra-Light Real-Time Underwater Object Detection
Jun Dong, Wenli Wu, Jintao Cheng +1
Despite the remarkable achievements in object detection, the model's accuracy and efficiency still require further improvement under challenging underwater conditions, such as low…
Real-Time AIoT for AAV Antenna Interference Detection via Edge-Cloud Collaboration
Jun Dong, Jintao Cheng, Jin Wu +3
In the fifth-generation (5G) era, eliminating communication interference sources is crucial for maintaining network performance. Interference often originates from unauthorized or…
CV-MOS: A Cross-View Model for Motion Segmentation
Xiaoyu Tang, Zeyu Chen, Jintao Cheng +3
In autonomous driving, accurately distinguishing between static and moving objects is crucial for the autonomous driving system. When performing the motion object segmentation (MOS…
MV-MOS: Multi-View Feature Fusion for 3D Moving Object Segmentation
Jintao Cheng, Xingming Chen, Jinxin Liang +3
Effectively summarizing dense 3D point cloud data and extracting motion information of moving objects (moving object segmentation, MOS) is crucial to autonomous driving and robotic…
OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition
Qiuchi Xiang, Jintao Cheng, Jiehao Luo +4
Place recognition is the foundation for enabling autonomous systems to achieve independent decision-making and safe operations. It is also crucial in tasks such as loop closure det…
Speech Emotion Recognition Via CNN-Transformer and Multidimensional Attention Mechanism
Xiaoyu Tang, Yixin Lin, Ting Dang +2
Speech Emotion Recognition (SER) is crucial in human-machine interactions. Mainstream approaches utilize Convolutional Neural Networks or Recurrent Neural Networks to learn local e…
YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional and Large Kernel Design for Antenna Interference Source Detection
Xiaoyu Tang, Xingming Chen, Jintao Cheng +4
In the era of 5G communication, removing interference sources that affect communication is a resource-intensive task. The rapid development of computer vision has enabled unmanned…
PALoc: Advancing SLAM Benchmarking with Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation
Xiangcheng Hu, Linwei Zheng, Jin Wu +7
Accurately generating ground truth (GT) trajectories is essential for Simultaneous Localization and Mapping (SLAM) evaluation, particularly under varying environmental conditions.…
MF-MOS: A Motion-Focused Model for Moving Object Segmentation
Jintao Cheng, Kang Zeng, Zhuoxu Huang +5
Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field. Dynamic capture is a…