A two-stage model trained on VLM-generated, critic-filtered captions produces frame-level captions that track action progression and outperforms existing VLMs on the new FrameCapEval benchmark.
A Review of Deep Learning for Video Captioning
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abstract
Video captioning (VC) is a fast-moving, cross-disciplinary area of research that bridges work in the fields of computer vision, natural language processing (NLP), linguistics, and human-computer interaction. In essence, VC involves understanding a video and describing it with language. Captioning is used in a host of applications from creating more accessible interfaces (e.g., low-vision navigation) to video question answering (V-QA), video retrieval and content generation. This survey covers deep learning-based VC, including but, not limited to, attention-based architectures, graph networks, reinforcement learning, adversarial networks, dense video captioning (DVC), and more. We discuss the datasets and evaluation metrics used in the field, and limitations, applications, challenges, and future directions for VC.
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Progress-Aware Video Frame Captioning
A two-stage model trained on VLM-generated, critic-filtered captions produces frame-level captions that track action progression and outperforms existing VLMs on the new FrameCapEval benchmark.