16 citations · 32 across the 3 of their papers we have counts for
8 papers
Unsupervised Learning of Video Representations via Dense Trajectory Clustering
Pavel Tokmakov, Martial Hebert, Cordelia Schmid
This paper addresses the task of unsupervised learning of representations for action recognition in videos. Previous works proposed to utilize future prediction, or other domain-sp…
TAO: A Large-Scale Benchmark for Tracking Any Object
Achal Dave, Tarasha Khurana, Pavel Tokmakov +2
For many years, multi-object tracking benchmarks have focused on a handful of categories. Motivated primarily by surveillance and self-driving applications, these datasets provide…
Learning to Track Any Object
Achal Dave, Pavel Tokmakov, Cordelia Schmid +1
Object tracking can be formulated as "finding the right object in a video". We observe that recent approaches for class-agnostic tracking tend to focus on the "finding" part, but l…
A Study on Action Detection in the Wild
Yubo Zhang, Pavel Tokmakov, Martial Hebert +1
The recent introduction of the AVA dataset for action detection has caused a renewed interest to this problem. Several approaches have been recently proposed that improved the perf…
Towards Segmenting Anything That Moves
Achal Dave, Pavel Tokmakov, Deva Ramanan
Detecting and segmenting individual objects, regardless of their category, is crucial for many applications such as action detection or robotic interaction. While this problem has…
Learning Compositional Representations for Few-Shot Recognition
Pavel Tokmakov, Yu-Xiong Wang, Martial Hebert
One of the key limitations of modern deep learning approaches lies in the amount of data required to train them. Humans, by contrast, can learn to recognize novel categories from j…