Search Results for author:
Found 8 papers, 6 papers with code
UniMD: Towards Unifying Moment Retrieval and Temporal Action Detection
Temporal Action Detection (TAD) focuses on detecting pre-defined actions, while Moment Retrieval (MR) aims to identify the events described by open-ended natural language within untrimmed videos.
Ranked #2 on
Natural Language Moment Retrieval
on ActivityNet Captions
(R@5,IoU=0.5 metric)
InstaGen: Enhancing Object Detection by Training on Synthetic Dataset
The grounding head is trained to align the text embedding of category names with the regional visual feature of the diffusion model, using supervision from an off-the-shelf object detector, and a novel self-training scheme on (novel) categories not covered by the detector.
FastPillars: A Deployment-friendly Pillar-based 3D Detector
The deployment of 3D detectors strikes one of the major challenges in real-world self-driving scenarios.
AeDet: Azimuth-invariant Multi-view 3D Object Detection
However, the typical convolution ignores the radial symmetry of the BEV features and increases the difficulty of the detector optimization.
PromptDet: Towards Open-vocabulary Detection using Uncurated Images
The goal of this work is to establish a scalable pipeline for expanding an object detector towards novel/unseen categories, using zero manual annotations.
Exploring Classification Equilibrium in Long-Tailed Object Detection
Specifically, EBL increases the intensity of the adjustment of the decision boundary for the weak classes by a designed score-guided loss margin between any two classes.
Ranked #10 on
Object Detection
on LVIS v1.0 val
TOOD: Task-aligned One-stage Object Detection
One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of spatial misalignment in predictions between the two tasks.
Ranked #3 on
2D Object Detection
on CeyMo
Domain Confusion with Self Ensembling for Unsupervised Adaptation
Data collection and annotation are time-consuming in machine learning, expecially for large scale problem.
