55 citations · 103 across the 11 of their papers we have counts for
13 papers
The 7th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +17
The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems…
Self-supervised Character-to-Character Distillation for Text Recognition
Tongkun Guan, Wei Shen, Xue Yang +3
When handling complicated text images (e.g., irregular structures, low resolution, heavy occlusion, and uneven illumination), existing supervised text recognition methods are data-…
The 6th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +14
The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and art…
Self-supervised Implicit Glyph Attention for Text Recognition
Tongkun Guan, Chaochen Gu, Jingzheng Tu +5
The attention mechanism has become the \emph{de facto} module in scene text recognition (STR) methods, due to its capability of extracting character-level representations. These me…
Abnormal Occupancy Grid Map Recognition using Attention Network
Fuqin Deng, Hua Feng, Mingjian Liang +6
The occupancy grid map is a critical component of autonomous positioning and navigation in the mobile robotic system, as many other systems' performance depends heavily on it. To g…
Industrial Scene Text Detection with Refined Feature-attentive Network
Tongkun Guan, Chaochen Gu, Changsheng Lu +4
Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded character structures, and cluttered backgro…
The 5th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +11
The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2)…
Decoupled Spatial Temporal Graphs for Generic Visual Grounding
Qianyu Feng, Yunchao Wei, Mingming Cheng +1
Visual grounding is a long-lasting problem in vision-language understanding due to its diversity and complexity. Current practices concentrate mostly on performing visual grounding…
Look, Cast and Mold: Learning 3D Shape Manifold from Single-view Synthetic Data
Qianyu Feng, Yawei Luo, Keyang Luo +1
Inferring the stereo structure of objects in the real world is a challenging yet practical task. To equip deep models with this ability usually requires abundant 3D supervision whi…
CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions
Qi Feng, Vitaly Ablavsky, Stan Sclaroff
Natural Language (NL) descriptions can be one of the most convenient or the only way to interact with systems built to understand and detect city scale traffic patterns and vehicle…
Siamese Natural Language Tracker: Tracking by Natural Language Descriptions with Siamese Trackers
Qi Feng, Vitaly Ablavsky, Qinxun Bai +1
We propose a novel Siamese Natural Language Tracker (SNLT), which brings the advancements in visual tracking to the tracking by natural language (NL) descriptions task. The propose…
Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes
Chenhongyi Yang, Vitaly Ablavsky, Kaihong Wang +2
While visual object detection with deep learning has received much attention in the past decade, cases when heavy intra-class occlusions occur have not been studied thoroughly. In…
Real-time Visual Object Tracking with Natural Language Description
Qi Feng, Vitaly Ablavsky, Qinxun Bai +2
In recent years, deep-learning-based visual object trackers have been studied thoroughly, but handling occlusions and/or rapid motion of the target remains challenging. In this wor…