27 citations · 37 across the 2 of their papers we have counts for
7 papers
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)…
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…
DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognition
Nuno C. Garcia, Sarah Adel Bargal, Vitaly Ablavsky +3
In this work, we address the problem of learning an ensemble of specialist networks using multimodal data, while considering the realistic and challenging scenario of possible miss…
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…