Search Results for author:
Found 7 papers, 4 papers with code
Video Task Decathlon: Unifying Image and Video Tasks in Autonomous Driving
VTD is a promising new direction for exploring the unification of perception tasks in autonomous driving.
Composite Learning for Robust and Effective Dense Predictions
In this paper, we find that jointly training a dense prediction (target) task with a self-supervised (auxiliary) task can consistently improve the performance of the target task, while eliminating the need for labeling auxiliary tasks.
Ranked #102 on
Semantic Segmentation
on NYU Depth v2
QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking
In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of object regions on a pair of images for contrastive learning.
Ranked #4 on
Multiple Object Tracking
on BDD100K test
Tracking Every Thing in the Wild
Our experiments show that TETA evaluates trackers more comprehensively, and TETer achieves significant improvements on the challenging large-scale datasets BDD100K and TAO compared to the state-of-the-art.
Ranked #4 on
Multi-Object Tracking
on TAO
Dense Prediction with Attentive Feature Aggregation
Our experiments show consistent and significant improvements on challenging semantic segmentation benchmarks, including Cityscapes, BDD100K, and Mapillary Vistas, at negligible computational and parameter overhead.
Robust Object Detection via Instance-Level Temporal Cycle Confusion
Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications.
Frustratingly Simple Few-Shot Object Detection
Such a simple approach outperforms the meta-learning methods by roughly 2~20 points on current benchmarks and sometimes even doubles the accuracy of the prior methods.
Ranked #17 on
Few-Shot Object Detection
on MS-COCO (30-shot)
