The goal of video captioning is to generate captions for a video by understanding visual and temporal cues. A general video captioning model consists of an Encoder-Decoder framework where Encoder generally captures the visual and temporal information while the decoder generates captions. Recent works have incorporated object-level information into the Encoder by a pretrained off-the-shelf object detector, significantly
improving performance.

However, using an object detector comes with the following downsides: 1) object detectors may not exhaustively capture all the object categories. 2) In a realistic setting, the performance may be influenced by the domain gap between the object-detector and the visual-captioning dataset. To remedy this, we argue that using an external object detector could be eliminated if the model is equipped with the capability of automatically finding salient regions. To achieve this, we propose a novel architecture that learns to attend to salient regions such as objects, persons automatically using a co-segmentation inspired attention module. Then, we utilize a novel salient region interaction module to promote information propagation between salient regions of adjacent frames. Further, we incorporate this salient region-level information into the model using knowledge distillation. We evaluate our model on two benchmark datasets MSR-VTT and MSVD, and show that our model achieves competitive performance without using any object detector.