most citedCityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions

27 citations · 30 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV20211 cited

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…

cs.CV2021

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)…

cs.CV20212 cited

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…

cs.CV202127 cited

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…

cs.CV2019

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…

cs.CV2019

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…