Cross-View and Multi-Step Interaction for Change Captioning

Change captioning is a task that describes changes in image pairs using natural language. This task is more complex than single-image captioning as it requires a comprehensive understanding of each image and the ability to recognize and describe the semantic changes in image pairs. The key challenge lies in making the network generate an accurate and stable change representation under the interference of viewpoint shift. In this paper, we propose a cross-view and multi-step interaction network to generate robust change representation to resist pseudo-change. Specifically, in the intra-image representation learning stage, a cross-view interaction encoder is designed to enhance internal relationships by cross-referencing in image pairs. In the change feature learning stage, a multi-step change perceptron is employed to capture the change semantics from coarse to fine progressively. Then, a fusion module dynamically combines them as a fine-grained change representation. Besides, we propose a backward representation reconstruction module that facilitates the capture of semantic changes, thus improving the quality of captions in a self-supervised manner. Extensive experiments have shown that the method effectively captures real semantic changes under the interference of viewpoint shift and achieves state-of-the-art performance on five public datasets. The code is available at https://github.com/TTXiann/CVMSI