Lili Zhao

Wuhan University

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Jianwen Chen

Tianshui Normal University

Wenyi Wang

University of Electronic Science and Technology of China

Mingjing Ai

Beihang University

Qian Yin

Peking University

Xuhu Lin

University of Electronic Science and Technology of China

Lishu Zhang

Forschungszentrum Jülich

Dake Wang

Shanghai Jiao Tong University

Qiang Yang

Hong Kong University of Science and Technology

Zhuangzhi Yan

Shanghai University

Ming Xie

Guangzhou University

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Chinese Academy of Sciences

Tsinghua University

University of Electronic Science and Technology of China

University of Chinese Academy of Sciences

Zhejiang University

Shanghai University

University of Science and Technology of China

University of Michigan–Ann Arbor

Peking University

Zhejiang University of Technology

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COIN: A Large-Scale Dataset for Comprehensive Instructional Video Analysis

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

Yansong Tang Dajun Ding Yongming Rao Yu Zheng Danyang Zhang

There are substantial instruction videos on the Internet, which enables us to acquire knowledge for completing various tasks. However, most existing datasets for instruction video analysis have the limitations in diversity and scale, which makes them far from many real-world applications where more diverse activities occur. Moreover, it still remains a great challenge to organize and harness such data. To address these problems, we introduce a large-scale dataset called "COIN" for COmprehensive INstruction video analysis. Organized with a hierarchical structure, the COIN dataset contains 11,827 videos of 180 tasks in 12 domains (e.g., vehicles, gadgets, etc.) related to our daily life. With a new developed toolbox, all the videos are annotated effectively with a series of step descriptions and the corresponding temporal boundaries. Furthermore, we propose a simple yet effective method to capture the dependencies among different steps, which can be easily plugged into conventional proposal-based action detection methods for localizing important steps in instruction videos. In order to provide a benchmark for instruction video analysis, we evaluate plenty of approaches on the COIN dataset under different evaluation criteria. We expect the introduction of the COIN dataset will promote the future in-depth research on instruction video analysis for the community.

Toolbox

Benchmark (surveying)

Internet video

10.1109/cvpr.2019.00130

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Citations (200)

MVFI-Net: Motion-Aware Video Frame Interpolation Network

Lecture notes in computer science (2023)

Xuhu Lin Lili Zhao Xi Liu Jianwen Chen

Image warping

Kernel (algebra)

Motion interpolation

Interpolation

Optical Flow

Inter frame

Convolution (computer science)

Scope (computer science)

10.1007/978-3-031-26313-2_21

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Citations (2)

Single Image Dehazing Using Frequency Attention

Lecture notes in computer science (2023)

Bin Hu Zhuangzhuang Yue Yuehua Li Lili Zhao Shi Cheng

Convolution (computer science)

Benchmark (surveying)

10.1007/978-3-031-30111-7_22

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Citations (0)

Region based inter-color intra prediction for RGB signals

Mingjing Ai Lili Zhao Jingke Wang

High-fidelity video coding tends to adopt the RGB coding, which directly reduces RGB inter-color redundancy without external color space conversion. This paper presents a region based inter-color prediction scheme for RGB signals, which treats G component as primary component and predicts B/R component from G component with different translation offsets based on different texture regions. Experimental results show that compared with H.264/AVC intra coding algorithm, the proposed algorithm can save an average of about 13.64% bits with limited PSNR loss for most high resolution video sequences and with much better PSNR gain for certain testing material. Meanwhile, the complexity of the proposed algorithm is much less than H.264/AVC intra prediction technique in independent mode.

RGB color model

RGB color space

Component (thermodynamics)

10.1109/iccsit.2010.5563534

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Citations (0)

PIV Measurements of the Near-Wake Flow of an Airfoil Above a Free Surface

Journal of Hydrodynamics (2007)

Daichin Kang Wen Lili Zhao

Free surface

10.1016/s1001-6058(07)60143-7

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Citations (19)

Aggregation-based spectrum allocation algorithm in cognitive radio networks

Yun Li Lili Zhao Chonggang Wang Amir Daneshmand Qing Hu

In cognitive radio networks, the idle spectrum bands that cognitive users sensed are usually discontinuous. Only one idle spectrum band may not be able to fulfill cognitive users' bandwidth requirements. In order to let cognitive users access allocated spectrum bands successfully and further improve the efficiency of spectrum utilization, we propose spectrum aggregation-based graph coloring algorithm (SAGCA), a spectrum allocation algorithm in cognitive radio networks. SAGCA considers bandwidth requirements of cognitive users and limitation of spectrum range that equipment can utilize due to hardware constraint. Numerical results show that the proposed algorithm can achieve greater performance in total bandwidth and percentage of cognitive users that networks can support compared to the original algorithm.

Bandwidth allocation

Idle

Frequency allocation

Graph Coloring

10.1109/noms.2012.6211942

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Citations (16)

A unified framework of active transfer learning for cross-system recommendation

Artificial Intelligence (2016)

Lili Zhao Sinno Jialin Pan Qiang Yang

Transfer of learning

10.1016/j.artint.2016.12.004

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Citations (76)

Spatial Bit Allocation with SAQD-Domain Source Models for H.264/SVC

Lecture notes in computer science (2010)

Mingjing Ai Lili Zhao

Rate–distortion theory

Rate distortion

Distortion (music)

10.1007/978-3-642-15696-0_18

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Citations (0)

Deep Hashing with Category Mask for Fast Video Retrieval

arXiv (Cornell University) (2017)

Xu Liu Lili Zhao Dajun Ding Yajiao Dong

This paper proposes an end-to-end deep hashing framework with category mask for fast video retrieval. We train our network in a supervised way by fully exploiting inter-class diversity and intra-class identity. Classification loss is optimized to maximize inter-class diversity, while intra-pair is introduced to learn representative intra-class identity. We investigate the binary bits distribution related to categories and find out that the effectiveness of binary bits is highly correlated with data categories, and some bits may degrade classification performance of some categories. We then design hash code generation scheme with category mask to filter out bits with negative contribution. Experimental results demonstrate the proposed method outperforms several state-of-the-arts under various evaluation metrics on public datasets.

Binary code

Code (set theory)

10.48550/arxiv.1712.08315

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Citations (10)

Cross-Layer Design for Energy Efficiency of TCP Traffic in Cognitive Radio Networks

Gengyu Li Zheng Hu Guoyi Zhang Lili Zhao Wenpeng Li

In cognitive radio (CR) networks, cross-layer design is an important issue since the behavior of one protocol could affect the performance of others. However, for the energy-constraint CR networks, the previous works mostly focus on maximizing the throughput in physical or transport layer, rather than the energy efficiency of the end-to-end transmission control protocol (TCP). In this paper, we propose a novel cross-layer scheme which takes the lower layers' parameters into consideration, e.g., signal-to-noise ratio (SNR), modulation and frame size, to improve the energy efficiency of TCP. Specifically, we use a finite state Markov channel (FSMC) model to characterize the fading channel, and solve the optimization problem by a restless bandit approach. Simulation results show that the physical and data link layer parameters affect the energy efficiency of the TCP traffic significantly and the performance can be improved by the adjustment of lower layers' parameters compared with the existing method.

Data link layer

Transport layer

10.1109/vetecf.2011.6092890

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Citations (10)