361 citations · 1.4k across the 31 of their papers we have counts for
32 papers
Enhancing Your Trained DETRs with Box Refinement
Yiqun Chen, Qiang Chen, Peize Sun +3
We present a conceptually simple, efficient, and general framework for localization problems in DETR-like models. We add plugins to well-trained models instead of inefficiently des…
Semantic-SAM: Segment and Recognize Anything at Any Granularity
Feng Li, Hao Zhang, Peize Sun +6
In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Our model offers two key advanta…
GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest
Shilong Zhang, Peize Sun, Shoufa Chen +6
Visual instruction tuning large language model(LLM) on image-text pairs has achieved general-purpose vision-language abilities. However, the lack of region-text pairs limits their…
Going Denser with Open-Vocabulary Part Segmentation
Peize Sun, Shoufa Chen, Chenchen Zhu +4
Object detection has been expanded from a limited number of categories to open vocabulary. Moving forward, a complete intelligent vision system requires understanding more fine-gra…
WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting
Kan Chen, Runzhou Ge, Hang Qiu +12
Widely adopted motion forecasting datasets substitute the observed sensory inputs with higher-level abstractions such as 3D boxes and polylines. These sparse shapes are inferred th…
ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box
Yifu Zhang, Xinggang Wang, Xiaoqing Ye +6
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects across video frames. Detection boxes serve as the basis of both 2D and 3D MOT. The inevitabl…
Learning Object-Language Alignments for Open-Vocabulary Object Detection
Chuang Lin, Peize Sun, Yi Jiang +5
Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding…
DiffusionDet: Diffusion Model for Object Detection
Shoufa Chen, Peize Sun, Yibing Song +1
We propose DiffusionDet, a new framework that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. During the training stage, object boxes…
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations
Zhaoqi Leng, Guowang Li, Chenxi Liu +5
Data augmentations are important in training high-performance 3D object detectors for point clouds. Despite recent efforts on designing new data augmentations, perhaps surprisingly…
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds
Pei Sun, Mingxing Tan, Weiyue Wang +4
3D object detection in point clouds is a core component for modern robotics and autonomous driving systems. A key challenge in 3D object detection comes from the inherent sparse na…
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…
Language as Queries for Referring Video Object Segmentation
Jiannan Wu, Yi Jiang, Peize Sun +2
Referring video object segmentation (R-VOS) is an emerging cross-modal task that aims to segment the target object referred by a language expression in all video frames. In this wo…
DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion
Peize Sun, Jinkun Cao, Yi Jiang +4
A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization, and following re-identification (re-ID) for object association. This pipeline is pa…
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
Yifu Zhang, Peize Sun, Yi Jiang +6
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods obtain identities by associating detection boxes whose scores are hi…
Objects in Semantic Topology
Shuo Yang, Peize Sun, Yi Jiang +6
A more realistic object detection paradigm, Open-World Object Detection, has arisen increasing research interests in the community recently. A qualified open-world object detector…
Towards High-Quality Temporal Action Detection with Sparse Proposals
Jiannan Wu, Peize Sun, Shoufa Chen +4
Temporal Action Detection (TAD) is an essential and challenging topic in video understanding, aiming to localize the temporal segments containing human action instances and predict…
To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels
Yuning Chai, Pei Sun, Jiquan Ngiam +5
3D object detection is vital for many robotics applications. For tasks where a 2D perspective range image exists, we propose to learn a 3D representation directly from this range i…
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection
Pei Sun, Weiyue Wang, Yuning Chai +5
The detection of 3D objects from LiDAR data is a critical component in most autonomous driving systems. Safe, high speed driving needs larger detection ranges, which are enabled by…
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset
Scott Ettinger, Shuyang Cheng, Benjamin Caine +15
As autonomous driving systems mature, motion forecasting has received increasing attention as a critical requirement for planning. Of particular importance are interactive situatio…
Offboard 3D Object Detection from Point Cloud Sequences
Charles R. Qi, Yin Zhou, Mahyar Najibi +4
While current 3D object recognition research mostly focuses on the real-time, onboard scenario, there are many offboard use cases of perception that are largely under-explored, suc…
Segmenting Transparent Object in the Wild with Transformer
Enze Xie, Wenjia Wang, Wenhai Wang +4
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation data…
TransTrack: Multiple Object Tracking with Transformer
Peize Sun, Jinkun Cao, Yi Jiang +5
In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an at…
What Makes for End-to-End Object Detection?
Peize Sun, Yi Jiang, Enze Xie +4
Object detection has recently achieved a breakthrough for removing the last one non-differentiable component in the pipeline, Non-Maximum Suppression (NMS), and building up an end-…
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
Peize Sun, Rufeng Zhang, Yi Jiang +8
We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as anchor boxes…