Grounded Objects and Interactions for Video Captioning
This addresses the problem of fine-grained video understanding for applications like automated video description, though it appears incremental by focusing on object interactions.
The paper tackles video captioning by grounding language generation on object interactions, achieving state-of-the-art results on the ActivityNet Captions dataset.
We address the problem of video captioning by grounding language generation on object interactions in the video. Existing work mostly focuses on overall scene understanding with often limited or no emphasis on object interactions to address the problem of video understanding. In this paper, we propose SINet-Caption that learns to generate captions grounded over higher-order interactions between arbitrary groups of objects for fine-grained video understanding. We discuss the challenges and benefits of such an approach. We further demonstrate state-of-the-art results on the ActivityNet Captions dataset using our model, SINet-Caption based on this approach.