34 citations · 99 across the 10 of their papers we have counts for
10 papers
Exploring Transformers for Open-world Instance Segmentation
Jiannan Wu, Yi Jiang, Bin Yan +3
Open-world instance segmentation is a rising task, which aims to segment all objects in the image by learning from a limited number of base-category objects. This task is challengi…
Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking
Mingzhan Yang, Guangxin Han, Bin Yan +4
Multi-Object Tracking (MOT) aims to detect and associate all desired objects across frames. Most methods accomplish the task by explicitly or implicitly leveraging strong cues (i.e…
Universal Instance Perception as Object Discovery and Retrieval
Bin Yan, Yi Jiang, Jiannan Wu +4
All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field…
Towards Grand Unification of Object Tracking
Bin Yan, Yi Jiang, Peize Sun +4
We present a unified method, termed Unicorn, that can simultaneously solve four tracking problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters. Due t…
High-Performance Transformer Tracking
Xin Chen, Bin Yan, Jiawen Zhu +3
Correlation has a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion method that considers the s…
LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search
Bin Yan, Houwen Peng, Kan Wu +3
Object tracking has achieved significant progress over the past few years. However, state-of-the-art trackers become increasingly heavy and expensive, which limits their deployment…