activity
20122022
most citedPerson Re-identification: Past, Present and Future

1k citations · 1.8k across the 49 of their papers we have counts for

collaborators
Showing 2015Show all

6 papers · 1 filter

cs.CV2015★ 5 cited

Improving Human Activity Recognition Through Ranking and Re-ranking

Zhenzhong Lan, Shoou-I Yu, Alexander G. Hauptmann

We propose two well-motivated ranking-based methods to enhance the performance of current state-of-the-art human activity recognition systems. First, as an improvement over the cla…

cs.CV2015★ 9 cited

Handcrafted Local Features are Convolutional Neural Networks

Zhenzhong Lan, Shoou-I Yu, Ming Lin +2

Image and video classification research has made great progress through the development of handcrafted local features and learning based features. These two architectures were prop…

cs.CV2015★ 42 cited

Uncovering Temporal Context for Video Question and Answering

Linchao Zhu, Zhongwen Xu, Yi Yang +1

In this work, we introduce Video Question Answering in temporal domain to infer the past, describe the present and predict the future. We present an encoder-decoder approach using…

cs.CV2015★ 5 cited

Beyond Spatial Pyramid Matching: Space-time Extended Descriptor for Action Recognition

Zhenzhong Lan, Alexander G. Hauptmann

We address the problem of generating video features for action recognition. The spatial pyramid and its variants have been very popular feature models due to their success in balan…

cs.CV2015★ 6 cited

The Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action Recognition

Zhenzhong Lan, Dezhong Yao, Ming Lin +2

Motivated by the success of data-driven convolutional neural networks (CNNs) in object recognition on static images, researchers are working hard towards developing CNN equivalents…

cs.CV2015★ 6 cited

Long-short Term Motion Feature for Action Classification and Retrieval

Zhenzhong Lan, Xuanchong Li, Ming Lin +1

We propose a method for representing motion information for video classification and retrieval. We improve upon local descriptor based methods that have been among the most popular…