The DOI matches its Crossref record; 44 of the 46 references it cites were found in a registry or an index.
Two-Stream 3-D convNet Fusion for Action Recognition in Videos With Arbitrary Size and Length
Abstract
3-D convolutional neural networks (3-D-convNets) have been very recently proposed for action recognition in videos, and promising results are achieved. However, existing 3-D-convNets has two “artificial” requirements that may reduce the quality of video analysis: 1) It requires a fixed-sized (e.g., 112 $\times$ 112) input video; and 2) most of the 3-D-convNets require a fixed-length input (i.e., video shots with fixed number of frames). To tackle these issues, we propose an end-to-end pipeline named Two-stream 3-D-convNet Fusion, which can recognize human actions in videos of arbitrary size and length using multiple features. Specifically, we decompose a video into spatial and temporal shots. By taking a sequence of shots as input, each stream is implemented using a spatial temporal pyramid pooling (STPP) convNet with a long short-term memory (LSTM) or CNN-E model, softmax scores of which are combined by a late fusion. We devise the STPP convNet to extract equal-dimensional descriptions for each variable-size shot, and we adopt the LSTM/CNN-E model to learn a global description for the input video using these time-varying descriptions. With these advantages, our method should improve all 3-D CNN-based video analysis methods. We empirically evaluate our method for action recognition in videos and the experimental results show that our method outperforms the state-of-the-art methods (both 2-D and 3-D based) on three standard benchmark datasets (UCF101, HMDB51 and ACT datasets).
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“It is also worth pointing out that DH [35], another deep-based hashing method in those baselines, yields unsatisfactory performance on all datasets, where three speculations are illustrated for those results as follows.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [35]
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Human Action Recognition From Various Data Modalities: A Review
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“Instead of using 2D CNN, some other works [95], [96], [97], [98] used 3D CNNs as their feature extractors.”
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“…as a new machine learning paradigms that have shown tremendous performance gain compared to conventional machine learning algorithms, especially for applications such as voice recognition [4], image classification [13], video action recognition [32, 33], and spatio-temporal data applications [14].”
BackgroundEnhancing transportation systems via deep learning: A survey
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“But it is beyond a set of frames and the temporal information in video is important for video content analysis [33].”
BackgroundSelf-citationFrom Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning
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Self-citation
Metadata
- Authors
- Wang, X., Gao, L., Wang, P., Sun, X., & Liu, X.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- The University of Adelaide (AU)
- Harbin Institute of Technology (CN)
- Beihang University (CN)
- Venue
- IEEE Transactions on Multimedia
- Published
- 2018-03
- Volume
- 20
- Issue
- 3
- Pages
- 634-644
- ISSN
- 1520-9210 (ISSN-L)
- 1941-0077
- OpenAlex
- W2751445731
- Cited by
- 263
- References
- 46
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 12 · 2023: 24 · 2022: 23 · 2021: 44 · 2020: 47 · 2019: 60 · 2018: 42
- FWCI
- 17.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Multimedia (publishedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Funders
- Fundamental Research Funds for the Central Universities (ZYGX2014J063, ZYGX2014Z007)
- National Natural Science Foundation of China (61502080, 61632007)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- 3D convolutional neural networks, long short-term memory, decision fusion
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