Enhanced Video Captioning through Residual and Bottleneck CNNS with LSTM Integration
Amruta Rajendra Chougule, Shankar D. Chavan · Power System Technology · 2025
This study investigates various methods for video captioning, emphasizing the novel contributions of the proposed model. Existing approaches such as Vid2Seq, Positive-Augmented Contrastive Learning, GL-RG, and TextKG utilize diverse techniques to extract and interpret video features, yielding notable performance on datasets like MSR-VTT and MSVD. The proposed model, however, employs a CNN encoder with a combination of residual and bottleneck blocks to effectively capture temporal and spatial features, paired with an LSTM-based RNN for handling sequential data and long-range dependencies. Evaluated on MSR-VTT, MPII Cooking 2, and M-VAD datasets, the model achieves a peak BLEU score of 51, demonstrating its ability to generate high-quality video descriptions. The model maintains a streamlined yet powerful architecture, marking a significant advancement in video captioning. DOI : https://doi.org/10.52783/pst.1728