activity
20192025
most citedRTMDet: An Empirical Study of Designing Real-Time Object Detectors

316 citations · 686 across the 21 of their papers we have counts for

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24 papers · 1 filter

cs.CV2025

Harmonizing Visual Representations for Unified Multimodal Understanding and Generation

Size Wu, Wenwei Zhang, Lumin Xu +6

Unifying visual understanding and generation within a single multimodal framework remains a significant challenge, as the two inherently heterogeneous tasks require representations…

cs.CV2024★ 1 cited

OMG-Seg: Is One Model Good Enough For All Segmentation?

Xiangtai Li, Haobo Yuan, Wei Li +6

In this work, we address various segmentation tasks, each traditionally tackled by distinct or partially unified models. We propose OMG-Seg, One Model that is Good enough to effici…

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…