Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Zhuowen TuSuggested from co-authorship
- Longin Jan LateckiSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works400 from public data
- DOTA: A Large-Scale Dataset for Object Detection in Aerial Images3,264
A large-scale Dataset for Object deTection in Aerial images (DOTA) is introduced and state-of-the-art object detection algorithms on DOTA are evaluated, demonstrating that DOTA well represents real Earth Vision applications and are quite challenging.
- An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition3,166
A novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, is proposed, which generates an effective yet much smaller model, which is more practical for real-world application scenarios.
- Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection1,012
An obliquity factor based on area ratio between the object and its horizontal bounding box, guiding the selection of horizontal or oriented detection for each object is introduced, and five extra target variables are added to the regression head of faster R-CNN, which requires ignorable extra computation time.
- TextBoxes: A Fast Text Detector with a Single Deep Neural Network955
An end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum suppression.
- 905
- TextBoxes++: A Single-Shot Oriented Scene Text Detector897
An end- to-end trainable fast scene text detector, named TextBoxes++, which detects arbitrary-oriented scene text with both high accuracy and efficiency in a single network forward pass, and significantly outperforms the state-of-the-art approaches for word spotting and end-to-end text recognition tasks on popular benchmarks.
- Real-Time Scene Text Detection with Differentiable Binarization891
This paper proposes a module named Differentiable Binarization (DB), which can perform the binarization process in a segmentation network, and validate the performance improvements of DB on five benchmark datasets, which consistently achieves state-of-the-art results, in terms of both detection accuracy and speed.
- Detecting Oriented Text in Natural Images by Linking Segments775
SegLink, an oriented text detection method to decompose text into two locally detectable elements, namely segments and links, achieves an f-measure of 75.0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin.
- Asymmetric Non-Local Neural Networks for Semantic Segmentation720
Asymmetric Pyramid Non- local Block (APNB) and Asymmetric Fusion Non-local Block (AFNB) are presented, which has two prominent components: APNB leverages a pyramid sampling module into the non-local block to largely reduce the computation and memory consumption without sacrificing the performance.
- Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges714
The expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images.
- End-to-End Semi-Supervised Object Detection with Soft Teacher698
This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods, and proposes two simple yet effective techniques within this framework: a soft teacher mechanism where the classification loss of each unlabeled bounding box is weighed by the classification score produced by the teacher network.
- Robust Scene Text Recognition with Automatic Rectification693
RARE (Robust text recognizer with Automatic REctification), a recognition model that is robust to irregular text, which is end-to-end trainable, requiring only images and associated text labels, making it convenient to train and deploy the model in practical systems.
- Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes650
The recognition module of the Mask TextSpotter method is investigated separately, which significantly outperforms state-of-the-art methods on both regular and irregular text datasets for scene text recognition.
- Multi-oriented Text Detection with Fully Convolutional Networks596
A novel approach for text detection in natural images that consistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, I CDAR2015 and ICDAR2013.
- OCRBench: on the hidden mystery of OCR in large multimodal models574
A comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks, revealing both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression recognition.
- Rotation-Sensitive Regression for Oriented Scene Text Detection561
The proposed method named Rotation-sensitive Regression Detector (RRD) achieves state-of-the-art performance on several oriented scene text benchmark datasets, including ICDAR 2015, MSRA-TD500, RCTW-17, and COCO-Text, and achieves a significant improvement on a ship collection dataset, demonstrating its generality on oriented object detection.
- Revisiting multiple instance neural networks533
This article revisit Multiple Instance Neural Networks (MINNs) that the neural networks aim at solving the MIL problems and proposes a new type of MINN to learn bag representations, which is different from the existing MINNs that focus on estimating instance label.
- EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection514
A novel fusion module is proposed to enhance the point features with semantic image features in a point-wise manner without any image annotations to address two critical issues in the 3D detection task, including the exploitation of multiple sensors~ and the inconsistency between the localization and classification confidence.
- 496
- YOLOP: You Only Look Once for Panoptic Driving Perception495
To the best knowledge, this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS), and maintain excellent accuracy.
- Side Adapter Network for Open-Vocabulary Semantic Segmentation487
This paper presents a new framework for open-vocabulary semantic segmentation with the pre-trained vision-language model, named Side Adapter Network (SAN), which significantly outperforms other counterparts, with up to 18 times fewer trainable parameters and 19 times faster inference speed.
- ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction452
The ICDAR 2019 Challenge on "Scanned receipts OCR and key information extraction" (SROIE) covers important aspects related to the automated analysis of scanned receipts, and is considered to evolve into a useful resource for the community, drawing further attention and promoting research and development efforts in this field.
- Real-Time Scene Text Detection With Differentiable Binarization and Adaptive Scale Fusion431
A Differentiable Binarization (DB) module that integrates the binarization process, one of the most important steps in the post-processing procedure, into a segmentation network is proposed and an efficient Adaptive Scale Fusion (ASF) module is proposed to improve the scale robustness by fusing features of different scales adaptively.
- TANet: Robust 3D Object Detection from Point Clouds with Triple Attention428
A novel TANet is introduced in this paper, which mainly contains a Triple Attention (TA) module, and a Coarse-to-Fine Regression (CFR) module that boosts the accuracy of localization without excessive computation cost.
- 425
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.