Supplementing Missing Visions via Dialog for Scene Graph Generations
This addresses a practical challenge in computer vision where visual data is often incomplete, offering a model-agnostic framework for AI systems to interact via dialog.
The paper tackles the problem of scene graph generation with incomplete visual input by supplementing missing information through natural language dialog interactions, achieving promising performance improvements over baselines.
Most current AI systems rely on the premise that the input visual data are sufficient to achieve competitive performance in various computer vision tasks. However, the classic task setup rarely considers the challenging, yet common practical situations where the complete visual data may be inaccessible due to various reasons (e.g., restricted view range and occlusions). To this end, we investigate a computer vision task setting with incomplete visual input data. Specifically, we exploit the Scene Graph Generation (SGG) task with various levels of visual data missingness as input. While insufficient visual input intuitively leads to performance drop, we propose to supplement the missing visions via the natural language dialog interactions to better accomplish the task objective. We design a model-agnostic Supplementary Interactive Dialog (SI-Dial) framework that can be jointly learned with most existing models, endowing the current AI systems with the ability of question-answer interactions in natural language. We demonstrate the feasibility of such a task setting with missing visual input and the effectiveness of our proposed dialog module as the supplementary information source through extensive experiments and analysis, by achieving promising performance improvement over multiple baselines.