12 citations · 23 across the 6 of their papers we have counts for
6 papers
Unsqueeze [CLS] Bottleneck to Learn Rich Representations
Qing Su, Shihao Ji
Distillation-based self-supervised learning typically leads to more compressed representations due to its radical clustering process and the implementation of a sharper target dist…
FLSL: Feature-level Self-supervised Learning
Qing Su, Anton Netchaev, Hai Li +1
Current self-supervised learning (SSL) methods (e.g., SimCLR, DINO, VICReg,MOCOv3) target primarily on representations at instance level and do not generalize well to dense predict…
Towards Bridging the Performance Gaps of Joint Energy-based Models
Xiulong Yang, Qing Su, Shihao Ji
Can we train a hybrid discriminative-generative model within a single network? This question has recently been answered in the affirmative, introducing the field of Joint Energy-ba…
ChiTransformer:Towards Reliable Stereo from Cues
Qing Su, Shihao Ji
Current stereo matching techniques are challenged by restricted searching space, occluded regions and sheer size. While single image depth estimation is spared from these challenge…
RoadMap: A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving
Tong Qin, Yuxin Zheng, Tongqing Chen +2
Accurate localization is of crucial importance for autonomous driving tasks. Nowadays, we have seen a lot of sensor-rich vehicles (e.g. Robo-taxi) driving on the street autonomousl…
AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot
Tong Qin, Tongqing Chen, Yilun Chen +1
Autonomous valet parking is a specific application for autonomous vehicles. In this task, vehicles need to navigate in narrow, crowded and GPS-denied parking lots. Accurate localiz…