activity
20152022
most citedFiltered Channel Features for Pedestrian Detection

55 citations · 86 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV202213 cited

DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection

Gang Li, Xiang Li, Yujie Wang +3

The Mean-Teacher (MT) scheme is widely adopted in semi-supervised object detection (SSOD). In MT, the sparse pseudo labels, offered by the final predictions of the teacher (e.g., a…

cs.CV20214 cited

Keypoint Message Passing for Video-based Person Re-Identification

Di Chen, Andreas Doering, Shanshan Zhang +3

Video-based person re-identification (re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras. Ex…

cs.CV2021

Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation

Gang Li, Xiang Li, Yujie Wang +3

Knowledge Distillation (KD) is a widely-used technology to inherit information from cumbersome teacher models to compact student models, consequently realizing model compression an…

cs.NE20212 cited

Adaptive Group Collaborative Artificial Bee Colony Algorithm

Haiquan Wang, Hans-DietrichHaasis, Panpan Du +5

As an effective algorithm for solving complex optimization problems, artificial bee colony (ABC) algorithm has shown to be competitive, but the same as other population-based algor…

cs.CL201612 cited

Semi-supervised Discovery of Informative Tweets During the Emerging Disasters

Shanshan Zhang, Slobodan Vucetic

The first objective towards the effective use of microblogging services such as Twitter for situational awareness during the emerging disasters is discovery of the disaster-related…

cs.CV201555 cited

Filtered Channel Features for Pedestrian Detection

Shanshan Zhang, Rodrigo Benenson, Bernt Schiele

This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination…