Search Results for author:
Found 8 papers, 6 papers with code
Towards Category Unification of 3D Single Object Tracking on Point Clouds
Based on this observation, we design a novel point set representation learning network inheriting transformer architecture, termed AdaFormer, which adaptively encodes the dynamically varying shape and size information from cross-category data in a unified manner.
Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining
Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of segmenting novel categories from a distinct domain using only limited exemplars.
Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic Segmentation
Such semantic misalignment circulates in pre-training, leading to inferior zero-shot performance in dense predictions due to insufficient visual concepts captured in textual representations.
BEVTrack: A Simple and Strong Baseline for 3D Single Object Tracking in Bird's-Eye View
The spatial information indicating objects' spatial adjacency across consecutive frames is crucial for effective object tracking.
OSP2B: One-Stage Point-to-Box Network for 3D Siamese Tracking
By integrating the derived classification scores with the center-ness scores, the resulting network can effectively suppress interference proposals and further mitigate task misalignment.
GLT-T++: Global-Local Transformer for 3D Siamese Tracking with Ranking Loss
Incorporating this transformer-based voting scheme into 3D RPN, a novel Siamese method dubbed GLT-T is developed for 3D single object tracking on point clouds.
GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds
Technically, a global-local transformer (GLT) module is employed to integrate object- and patch-aware prior into seed point features to effectively form strong feature representation for geometric positions of the seed points, thus providing more robust and accurate cues for offset learning.
Learning Localization-aware Target Confidence for Siamese Visual Tracking
In this paper, to alleviate this misalignment, we propose a novel tracking paradigm, called SiamLA.
