20 citations · 55 across the 12 of their papers we have counts for
12 papers
Naaloss: Rethinking the objective of speech enhancement
Kuan-Hsun Ho, En-Lun Yu, Jeih-weih Hung +1
Reducing noise interference is crucial for automatic speech recognition (ASR) in a real-world scenario. However, most single-channel speech enhancement (SE) generates "processing a…
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning
Liang Zhao, En Yu, Zheng Ge +8
Human-AI interactivity is a critical aspect that reflects the usability of multimodal large language models (MLLMs). However, existing end-to-end MLLMs only allow users to interact…
GroupLane: End-to-End 3D Lane Detection with Channel-wise Grouping
Zhuoling Li, Chunrui Han, Zheng Ge +5
Efficiency is quite important for 3D lane detection due to practical deployment demand. In this work, we propose a simple, fast, and end-to-end detector that still maintains high d…
MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking
En Yu, Tiancai Wang, Zhuoling Li +3
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 unsatisf…
Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation
En Yu, Songtao Liu, Zhuoling Li +4
Although existing multi-object tracking (MOT) algorithms have obtained competitive performance on various benchmarks, almost all of them train and validate models on the same domai…
Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking
Pan Wang, Liangliang Ren, Shengkai Wu +4
The point cloud based 3D single object tracking has drawn increasing attention. Although many breakthroughs have been achieved, we also reveal two severe issues. By extensive analy…
Quality Matters: Embracing Quality Clues for Robust 3D Multi-Object Tracking
Jinrong Yang, En Yu, Zeming Li +2
3D Multi-Object Tracking (MOT) has achieved tremendous achievement thanks to the rapid development of 3D object detection and 2D MOT. Recent advanced works generally employ a serie…
Delving into the Pre-training Paradigm of Monocular 3D Object Detection
Zhuoling Li, Chuanrui Zhang, En Yu +1
The labels of monocular 3D object detection (M3OD) are expensive to obtain. Meanwhile, there usually exists numerous unlabeled data in practical applications, and pre-training is a…
Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking
En Yu, Zhuoling Li, Shoudong Han
Discriminative representation is crucial for the association step in multi-object tracking. Recent work mainly utilizes features in single or neighboring frames for constructing me…
RelationTrack: Relation-aware Multiple Object Tracking with Decoupled Representation
En Yu, Zhuoling Li, Shoudong Han +1
Existing online multiple object tracking (MOT) algorithms often consist of two subtasks, detection and re-identification (ReID). In order to enhance the inference speed and reduce…
MAT: Motion-Aware Multi-Object Tracking
Shoudong Han, Piao Huang, Hongwei Wang +4
Modern multi-object tracking (MOT) systems usually model the trajectories by associating per-frame detections. However, when camera motion, fast motion, and occlusion challenges oc…
Refinements in Motion and Appearance for Online Multi-Object Tracking
Piao Huang, Shoudong Han, Jun Zhao +4
Modern multi-object tracking (MOT) system usually involves separated modules, such as motion model for location and appearance model for data association. However, the compatible p…