|
I am a researcher at Media Lab of The City College, City University of New York, where I work in the intersection of Deep Learning, Computer Vision, and Image Processing. I am finishing a Ph.D. advised by Prof. Yingli Tian. Currently my research interests mainly focus on inference and learning for scene text detection and recognition in the wild, and visual-linguistic understanding on scene text Images. My thesis proposal is entitled "Deep Features for Context-aware Text Extraction" and the slides are available as linked. I interned at Siemens Corporate Research and worked on the visual representation learning for novel view synthesis. I obtained the B.E. degree from Nanjing University of Aeronautics and Astronautics at 2013. Email / Resume / Google Scholar / LinkedIn |
|
|
News:
|
|
Extended our previous CVPR paper as an End-to-End pipeline from scene text detection and retrieval to recognition.
|
|
To incrementally generates complete and consistent 2D or 3D scenes with learned scene priors, while real observations of an actual scene can be incorporated, and unobserved parts of the scene can be hallucinated. Applications include autonomous agent exploration and few-shot learning. |
|
Combining the power of both instance-level scene text segmentation and visual phrase grounding.
|
|
To utilize text instances for understanding natural scenes, we have proposed a framework that combines image-based text localization with language-based context description for text instances. Specifically, we explore the task of unambiguous text localization and retrieval, to accurately localize a specific targeted text instance in a cluttered image given a natural language description that refers to it. |
|
We evaluate several of the most commonly used low-level features for real-world surveillance event detection tasks.
|
|
Our system allows blind users to explore multi-floor environment with a wearable Tango device.
|
|
An assistive text spotting based navigation system is proposed, based on stroke-specific features and subsequent text tracking process.
|
|
A framework is proposed to deconvolve blind image with patch-wise prior and adaptive shrinkage cascades.
|
|
A top-down framework is introduced for automatic localization and recognition of text-based traffic guide panels captured by car-mounted cameras from natural scene images.
|
|
We introduce an algorithm based on region trajectories to establish the connections between object localization in individual frames and video sequences. |
|
We presented a novel mobile wearable context-aware indoor maps and navigation system with obstacle avoidance for the blind.
|
|
A wearable Obstacle Stereo Feedback (OSF) System for the Blind people based on 3D space obstacle detection is presented to assist the navigation.
|
|
We present a novel video-based object localization system, which is developed for the Semantic Localization task of TRECVID 2015. We won the 1st place on this track. Extended to an ICMR 2016 paper. |
|
We present two video-based event detection systems for the Surveillance Event Detection (SED) task of TRECVID 2014. We won the 3rd place on this track. Extended to a TCSVT paper. |
|
We proposed a multi-frame based scene text recognition method by tracking text regions in a video captured by a moving camera. |