82 citations · 285 across the 13 of their papers we have counts for
14 papers
Shape-Prior-Based Point Cloud Completion for Single-Stage Fully Sparse 3D Object Detection
Kaizheng Wang, Mingqian Ji, Jian Yang +1
Single-stage fully sparse 3D object detectors rely on point clouds data to detect objects in autonomous driving scenarios. However, the sparsity and incompleteness of point clouds…
Grouped Adaptive Loss Weighting for Person Search
Yanling Tian, Di Chen, Yunan Liu +2
Person search is an integrated task of multiple sub-tasks such as foreground/background classification, bounding box regression and person re-identification. Therefore, person sear…
Bi-level Alignment for Cross-Domain Crowd Counting
Shenjian Gong, Shanshan Zhang, Jian Yang +2
Recently, crowd density estimation has received increasing attention. The main challenge for this task is to achieve high-quality manual annotations on a large amount of training d…
PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection
Gang Li, Xiang Li, Yujie Wang +3
In this paper, we delve into two key techniques in Semi-Supervised Object Detection (SSOD), namely pseudo labeling and consistency training. We observe that these two techniques cu…
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…
Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection
Farzaneh Rezaeianaran, Rakshith Shetty, Rahaf Aljundi +3
In order to robustly deploy object detectors across a wide range of scenarios, they should be adaptable to shifts in the input distribution without the need to constantly annotate…
PoseTrackReID: Dataset Description
Andreas Doering, Di Chen, Shanshan Zhang +2
Current datasets for video-based person re-identification (re-ID) do not include structural knowledge in form of human pose annotations for the persons of interest. Nonetheless, po…
Person Search via A Mask-Guided Two-Stream CNN Model
Di Chen, Shanshan Zhang, Wanli Ouyang +2
In this work, we tackle the problem of person search, which is a challenging task consisted of pedestrian detection and person re-identification~(re-ID). Instead of sharing represe…
Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification
Guodong Ding, Shanshan Zhang, Salman Khan +3
Person re-identification aims to match a person's identity across multiple camera streams. Deep neural networks have been successfully applied to the challenging person re-identifi…
CityPersons: A Diverse Dataset for Pedestrian Detection
Shanshan Zhang, Rodrigo Benenson, Bernt Schiele
Convnets have enabled significant progress in pedestrian detection recently, but there are still open questions regarding suitable architectures and training data. We revisit CNN d…
How Far are We from Solving Pedestrian Detection?
Shanshan Zhang, Rodrigo Benenson, Mohamed Omran +2
Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods and the "perfect single frame detector". We enable our an…
Natural Scene Recognition Based on Superpixels and Deep Boltzmann Machines
Jinfu Yang, Jingyu Gao, Guanghui Wang +1
The Deep Boltzmann Machines (DBM) is a state-of-the-art unsupervised learning model, which has been successfully applied to handwritten digit recognition and, as well as object rec…
Exploring Human Vision Driven Features for Pedestrian Detection
Shanshan Zhang, Christian Bauckhage, Dominik A. Klein +1
Motivated by the center-surround mechanism in the human visual attention system, we propose to use average contrast maps for the challenge of pedestrian detection in street scenes…
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…