activity
20192025
most citedMMRotate: A Rotated Object Detection Benchmark using PyTorch

397 citations · 1.1k across the 28 of their papers we have counts for

collaborators
Showing 2023Show all

13 papers · 1 filter

cs.CV2023

CLIM: Contrastive Language-Image Mosaic for Region Representation

Size Wu, Wenwei Zhang, Lumin Xu +3

Detecting objects accurately from a large or open vocabulary necessitates the vision-language alignment on region representations. However, learning such a region-text alignment by…

cs.CV2023★ 12 cited

CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction

Size Wu, Wenwei Zhang, Lumin Xu +4

Open-vocabulary dense prediction tasks including object detection and image segmentation have been advanced by the success of Contrastive Language-Image Pre-training (CLIP). CLIP m…

cs.CV2023★ 6 cited

DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object Detection

Shilin Xu, Xiangtai Li, Size Wu +3

Open-vocabulary object detection (OVOD) aims to detect the objects beyond the set of classes observed during training. This work introduces a straightforward and efficient strategy…

cs.CV2023★ 3 cited

Object2Scene: Putting Objects in Context for Open-Vocabulary 3D Detection

Chenming Zhu, Wenwei Zhang, Tai Wang +2

Point cloud-based open-vocabulary 3D object detection aims to detect 3D categories that do not have ground-truth annotations in the training set. It is extremely challenging becaus…

cs.CV2023★ 33 cited

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…

cs.CV2023★ 29 cited

Segment Any Point Cloud Sequences by Distilling Vision Foundation Models

Youquan Liu, Lingdong Kong, Jun Cen +5

Recent advancements in vision foundation models (VFMs) have opened up new possibilities for versatile and efficient visual perception. In this work, we introduce Seal, a novel fram…

cs.CV2023★ 4 cited

RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions

Shaoyuan Xie, Lingdong Kong, Wenwei Zhang +4

The recent advances in camera-based bird's eye view (BEV) representation exhibit great potential for in-vehicle 3D perception. Despite the substantial progress achieved on standard…

cs.CV2023★ 11 cited

Transformer-Based Visual Segmentation: A Survey

Xiangtai Li, Henghui Ding, Haobo Yuan +6

Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. This technique has numerous real-world applications, such as autonomo…

cs.CV2023★ 1 cited

MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training

Runsen Xu, Tai Wang, Wenwei Zhang +4

This paper introduces the Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training and a carefully designed data-efficient 3D object dete…

cs.CV2023

Position-Guided Point Cloud Panoptic Segmentation Transformer

Zeqi Xiao, Wenwei Zhang, Tai Wang +3

DEtection TRansformer (DETR) started a trend that uses a group of learnable queries for unified visual perception. This work begins by applying this appealing paradigm to LiDAR-bas…

cs.CV2023★ 3 cited

Robo3D: Towards Robust and Reliable 3D Perception against Corruptions

Lingdong Kong, Youquan Liu, Xin Li +6

The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications. Existing large-scale 3D perception data…

cs.CV2023★ 3 cited

Dense Distinct Query for End-to-End Object Detection

Shilong Zhang, Xinjiang Wang, Jiaqi Wang +5

One-to-one label assignment in object detection has successfully obviated the need for non-maximum suppression (NMS) as postprocessing and makes the pipeline end-to-end. However, i…

cs.CV2023★ 3 cited

Aligning Bag of Regions for Open-Vocabulary Object Detection

Size Wu, Wenwei Zhang, Sheng Jin +2

Pre-trained vision-language models (VLMs) learn to align vision and language representations on large-scale datasets, where each image-text pair usually contains a bag of semantic…