Search Results for author:
Found 9 papers, 3 papers with code
Scene Text Recognition from Two-Dimensional Perspective
Inspired by speech recognition, recent state-of-the-art algorithms mostly consider scene text recognition as a sequence prediction problem.
Ranked #30 on
Scene Text Recognition
on SVT
2D-CTC for Scene Text Recognition
Scene text recognition has been an important, active research topic in computer vision for years.
Real-time Scene Text Detection with Differentiable Binarization
Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text.
Ranked #6 on
Scene Text Detection
on MSRA-TD500
TextScanner: Reading Characters in Order for Robust Scene Text Recognition
Driven by deep learning and the large volume of data, scene text recognition has evolved rapidly in recent years.
Ranked #18 on
Scene Text Recognition
on ICDAR2015
On Vocabulary Reliance in Scene Text Recognition
This remedy alleviates the problem of vocabulary reliance and improves the overall scene text recognition performance.
Slender Object Detection: Diagnoses and Improvements
In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely \textbf{slender objects}.
Ranked #1 on
Object Detection
on COCO+
Facial Attribute Transformers for Precise and Robust Makeup Transfer
In this paper, we address the problem of makeup transfer, which aims at transplanting the makeup from the reference face to the source face while preserving the identity of the source.
Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion
By incorporating the proposed DB and ASF with the segmentation network, our proposed scene text detector consistently achieves state-of-the-art results, in terms of both detection accuracy and speed, on five standard benchmarks.
Ranked #3 on
Scene Text Detection
on MSRA-TD500
SparseDet: Towards End-to-End 3D Object Detection
We believe this end-to-end paradigm of SparseDet will inspire new thinking on the sparsity of 3D object detection.
