Pruning Long-term Recurrent Convolutional Networks for Video Classification and captioning

Gogineni, Saikiran; Suryanarayana, Gowri; Soujanya, K.

doi:10.1109/icosec49089.2020.9215414

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Continuing the exploration of innovative architectures, the Long-term Recurrent Convolutional Network (LRCN) has emerged as a prominent example in the fusion of CNNs and LSTMs, as demonstrated in the work by several studies. (4,5,6,7) LRCN follows a sequential approach, initially utilizing CNNs to capture frame-level characteristics encoding spatial information. These features are then fed into an LSTM network to capture temporal dependencies between frames.…”

Section: Related Workmentioning

confidence: 99%

Investigating the Influence of Convolutional Operations on LSTM Networks in Video Classification

Benzyane,

Azrour,

Zeroual

et al. 2023

Data and Metadata

View full text Add to dashboard Cite

Video classification holds a foundational position in the realm of computer vision, involving the categorization and labeling of videos based on their content. Its significance resonates across various applications, including video surveil-lance, content recommendation, action recognition, video indexing, and more. The primary objective of video classification is to automatically analyze and comprehend the visual information embedded in videos, facilitating the efficient organization, retrieval, and interpretation of extensive video collections. The integration of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks has brought about a revolution in video classification. This fusion effectively captures both spatial and temporal dependencies within video sequences, leveraging the strengths of CNNs in extracting spatial features and LSTMs in modeling sequential and temporal information. ConvLSTM and LRCN (Long-term Recurrent Convolutional Networks) are two widely embraced architectures that embody this fusion. This paper seeks to investigate the impact of convolutions on LSTM networks in the context of video classification, aiming to compare the performance of ConvLSTM and LRCN.

show abstract

Section: Related Workmentioning

confidence: 99%

Investigating the Influence of Convolutional Operations on LSTM Networks in Video Classification

Benzyane,

Azrour,

Zeroual

et al. 2023

Data and Metadata

View full text Add to dashboard Cite

show abstract

Effective Video Event Detection Using Optimized Bidirectional Long Short-Term Memory Network

Alamuru

Jain

2023

Int. J. Info. Tech. Dec. Mak.

View full text Add to dashboard Cite

In recent times, video event detection gained high attention in the researcher’s community, because of its widespread applications. In this paper, a new model is proposed for detecting different human actions in the video sequences. First, the videos are acquired from the University of Central Florida (UCF) 101, Human Motion Database (HMDB) 51 and Columbia Consumer Video (CCV) datasets. In addition, the DenseNet201 model is implemented for extracting deep feature values from the acquired datasets. Further, the Improved Gray Wolf Optimization (IGWO) algorithm is developed for selecting active/relevant feature values that effectively improve the computational time and system complexity. In the IGWO, leader enhancement and competitive strategies are employed to improve the convergence rate and to prevent the algorithm from falling into the local optima. Finally, the Bi-directional Long Short Term Memory (BiLSTM) network is used for event classification (101 action types in UCF101, 51 action types in HMDB51, and 20 action types in CCV). In the resulting phase, the IGWO-based BiLSTM network achieved 94.73%, 96.53%, and 93.91% accuracy on the UCF101, HMDB51, and CCV datasets.

show abstract

Pruning Long-term Recurrent Convolutional Networks for Video Classification and captioning

Cited by 2 publications

References 12 publications

Investigating the Influence of Convolutional Operations on LSTM Networks in Video Classification

Investigating the Influence of Convolutional Operations on LSTM Networks in Video Classification

Effective Video Event Detection Using Optimized Bidirectional Long Short-Term Memory Network

Contact Info

Product

Resources

About