984 citations · 1.3k across the 27 of their papers we have counts for
11 papers · 1 filter
Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video Classification
Po-Yao Huang, Ye Yuan, Zhenzhong Lan +2
We report on CMU Informedia Lab's system used in Google's YouTube 8 Million Video Understanding Challenge. In this multi-label video classification task, our pipeline achieved 84.6…
Hidden Two-Stream Convolutional Networks for Action Recognition
Yi Zhu, Zhenzhong Lan, Shawn Newsam +1
Analyzing videos of human actions involves understanding the temporal relationships among video frames. State-of-the-art action recognition approaches rely on traditional optical f…
Guided Optical Flow Learning
Yi Zhu, Zhenzhong Lan, Shawn Newsam +1
We study the unsupervised learning of CNNs for optical flow estimation using proxy ground truth data. Supervised CNNs, due to their immense learning capacity, have shown superior p…
Deep Local Video Feature for Action Recognition
Zhenzhong Lan, Yi Zhu, Alexander G. Hauptmann
We investigate the problem of representing an entire video using CNN features for human action recognition. Currently, limited by GPU memory, we have not been able to feed a whole…
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…
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…
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…
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…
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…
Beyond Gaussian Pyramid: Multi-skip Feature Stacking for Action Recognition
Zhenzhong Lan, Ming Lin, Xuanchong Li +2
Most state-of-the-art action feature extractors involve differential operators, which act as highpass filters and tend to attenuate low frequency action information. This attenuati…
Temporal Extension of Scale Pyramid and Spatial Pyramid Matching for Action Recognition
Zhenzhong Lan, Xuanchong Li, Alexandar G. Hauptmann
Historically, researchers in the field have spent a great deal of effort to create image representations that have scale invariance and retain spatial location information. This pa…