Search Results for author:
Found 10 papers, 6 papers with code
SmartCooper: Vehicular Collaborative Perception with Adaptive Fusion and Judger Mechanism
Moreover, in the context of collaborative perception, it is important to recognize that not all CAVs contribute valuable data, and some CAV data even have detrimental effects on collaborative perception.
VLM-Eval: A General Evaluation on Video Large Language Models
Despite the rapid development of video Large Language Models (LLMs), a comprehensive evaluation is still absent.
OnlineRefer: A Simple Online Baseline for Referring Video Object Segmentation
Referring video object segmentation (RVOS) aims at segmenting an object in a video following human instruction.
Referring Expression Segmentation
Referring Video Object Segmentation
+2
Unleashing the Potential of Unsupervised Deep Outlier Detection through Automated Training Stopping
To our knowledge, our approach is the first to enable reliable identification of the optimal training iteration during training without requiring any labels.
MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking
Although end-to-end multi-object trackers like MOTR enjoy the merits of simplicity, they suffer from the conflict between detection and association seriously, resulting in unsatisfactory convergence dynamics.
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors
In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector.
Ranked #2 on
Multi-Object Tracking
on DanceTrack
(using extra training data)
Multi-Object Tracking
Multiple Object Tracking with Transformer
+2
The 1st-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge
We present our 1st place solution to the Group Dance Multiple People Tracking Challenge.
Multi-Object Tracking
Multiple Object Tracking with Transformer
+1
Progressive End-to-End Object Detection in Crowded Scenes
Experiments show that our method can significantly boost the performance of query-based detectors in crowded scenes.
Ranked #1 on
Object Detection
on CrowdHuman
MOTR: End-to-End Multiple-Object Tracking with Transformer
Temporal modeling of objects is a key challenge in multiple object tracking (MOT).
Ranked #1 on
Multi-Object Tracking
on MOT17
(e2e-MOT metric)
Multi-Object Tracking
Multiple Object Tracking with Transformer
+1
Variational Pedestrian Detection
Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the current IoU-based ground truth assignment procedure in classical object detection methods.
