Search Results for author:
Found 18 papers, 9 papers with code
PSTR: End-to-End One-Step Person Search With Transformers
We propose a novel one-step transformer-based person search framework, PSTR, that jointly performs person detection and re-identification (re-id) in a single architecture.
Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer
When using the ResNet50 backbone, our MS-STS achieves a mask AP of 50. 1 %, outperforming the best reported results in literature by 2. 7 % and by 4. 8 % at higher overlap threshold of AP_75, while being comparable in model size and speed on Youtube-VIS 2019 val.
ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection
The key in our ESGN is an efficient geometry-aware feature generation (EGFG) module.
Shape Prior Non-Uniform Sampling Guided Real-time Stereo 3D Object Detection
Compared with the baseline RTS3D, our proposed method has 2. 57% improvement on AP3d almost without extra network parameters.
Track to Detect and Segment: An Online Multi-Object Tracker
Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking.
Ranked #1 on
Online Multi-Object Tracking
on MOT16
Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection
Object detectors usually achieve promising results with the supervision of complete instance annotations.
TJU-DHD: A Diverse High-Resolution Dataset for Object Detection
In addition, a new diverse pedestrian dataset is further built.
From Handcrafted to Deep Features for Pedestrian Detection: A Survey
In addition to single-spectral pedestrian detection, we also review multi-spectral pedestrian detection, which provides more robust features for illumination variance.
SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation
In terms of real-time capabilities, SipMask outperforms YOLACT with an absolute gain of 3. 0% (mask AP) under similar settings, while operating at comparable speed on a Titan Xp.
Ranked #5 on
Real-time Instance Segmentation
on MSCOCO
D2Det: Towards High Quality Object Detection and Instance Segmentation
For precise localization, we introduce a dense local regression that predicts multiple dense box offsets for an object proposal.
Ranked #36 on
Instance Segmentation
on COCO test-dev
NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection
With this observation, we propose a new Neighbor Erasing and Transferring (NET) mechanism to reconfigure the pyramid features and explore scale-aware features.
Hierarchical Shot Detector
To further solve the second problem, a hierarchical shot detector (HSD) is proposed, which stacks two ROC modules and one feature enhanced module.
Ranked #3 on
Object Detection
on PASCAL VOC 2007
Triply Supervised Decoder Networks for Joint Detection and Segmentation
Experimental results on the VOC2007 and VOC2012 datasets demonstrate that the proposed TripleNet is able to improve both the detection and segmentation accuracies without adding extra computational costs.
Ranked #26 on
Semantic Segmentation
on PASCAL VOC 2012 test
Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection
In this paper, we propose a multi-branch and high-level semantic network by gradually splitting a base network into multiple different branches.
Learning Multilayer Channel Features for Pedestrian Detection
For example, CNN classifies these proposals by the full-connected layer features while proposal scores and the features in the inner-layers of CNN are ignored.
Ranked #23 on
Pedestrian Detection
on Caltech
Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry
Finally, we propose to combine both non-neighboring and neighboring features for pedestrian detection.
Ranked #26 on
Pedestrian Detection
on Caltech
Learning Sampling Distributions for Efficient Object Detection
Multistage particle windows (MPW), proposed by Gualdi et al., is an algorithm of fast and accurate object detection.
Cascade Learning by Optimally Partitioning
iCascade searches the optimal number ri of weak classifiers of each stage i by directly minimizing the computation cost of the cascade.
