Search Results for author:
Found 10 papers, 1 papers with code
GridFace: Face Rectification via Learning Local Homography Transformations
In this paper, we propose a method, called GridFace, to reduce facial geometric variations and improve the recognition performance.
What Can Help Pedestrian Detection?
Aggregating extra features has been considered as an effective approach to boost traditional pedestrian detection methods.
Ranked #15 on
Pedestrian Detection
on Caltech
FastMask: Segment Multi-scale Object Candidates in One Shot
Objects appear to scale differently in natural images.
UnitBox: An Advanced Object Detection Network
In present object detection systems, the deep convolutional neural networks (CNNs) are utilized to predict bounding boxes of object candidates, and have gained performance advantages over the traditional region proposal methods.
Scene Text Detection via Holistic, Multi-Channel Prediction
Recently, scene text detection has become an active research topic in computer vision and document analysis, because of its great importance and significant challenge.
Ranked #6 on
Scene Text Detection
on COCO-Text
Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4
Different from focused texts present in natural images, which are captured with user's intention and intervention, incidental texts usually exhibit much more diversity, variability and complexity, thus posing significant difficulties and challenges for scene text detection and recognition algorithms.
Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression
Facial landmark localization plays an important role in face recognition and analysis applications.
ICDAR 2015 Text Reading in the Wild Competition
Recently, text detection and recognition in natural scenes are becoming increasing popular in the computer vision community as well as the document analysis community.
Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?
In this paper, we report our observations on how big data impacts the recognition performance.
Learning Deep Face Representation
Our basic network is capable of achieving high recognition accuracy ($85. 8\%$ on LFW benchmark) with only 8 dimension representation.
