Enhanced Hybrid Framework and Comparative Analysis of Deep Learning Architectures for Video Captioning

With the rapid development of multimedia content on digital platforms, there is more and more need for intelligent systems to understand and describe videos in natural language. Automatic video captioning is a task that seeks to produce natural language descriptions of the spatial and temporal content in visual sequences. This paper studies the recent deep learning-based video captioning methods, and presents a new hybrid architecture based on EfficientNet for spatial feature extraction and Long Short-Term Memory (LSTM) network for temporal modeling. The model also includes analytic validation mechanisms that check for semantic coherence, temporal order and linguistic fluency. An extensive analysis of the state of the art from CNN-RNN hybrids to Transformer-based models and GAN-based models is presented with comparison results on standard benchmarks (MSVD, MSR-VTT, ActivityNet Captions). We investigate empirical results from previous work to demonstrate current capabilities and limitations in spatial-temporal learning, contextual reasoning, and caption fluency. Experimental results of existing hybrid model yields superior BLEU-4, CIDEr and METEOR scores to a state-of-the-art video captioning method, which verifies its effectiveness for context-aware and semantically rich video captioning.