Abductive Reasoning, has long been considered to be at the core ability of humans, which enables us to infer the most plausible explanation of incomplete known phenomena in daily life. However, such critical reasoning capability is rarely investigated for contemporary AI systems under such limited observations. To facilitate this research community, this paper sheds new light on Abductive Reasoning by studying a new vision-language task, Multi-modal Action chain abductive Reasoning (MAR), together with a large-scale Abductive Reasoning dataset: Given an incomplete set of language described events, MAR aims to imagine the most plausible event by spatio-temporal grounding in past video and then infer the hypothesis of subsequent action chain that can best explain the language premise. To solve this task, we propose a strong baseline model that realizes MAR from two perspectives: (i) we first introduce the transformer, which learns to encode the observation to imagine the plausible event with explicitly interpretable event grounding in the video based on the commonsense knowledge recognition ability. (ii) To complete the assumption of a follow-up action chain, we design a novel symbolic module that can complete strict derivation of the progressive action chain layer by layer. We conducted extensive experiments on the proposed dataset, and the experimental study shows that the proposed model significantly outperforms existing video-language models in terms of effectiveness on our newly created MAR dataset.

Natural language spatial video grounding aims to detect the relevant objects in video frames with descriptive sentences as the query. In spite of the great advances, most existing methods rely on dense video frame annotations, which require a tremendous amount of human effort. To achieve effective grounding under a limited annotation budget, we investigate one-shot video grounding and learn to ground natural language in all video frames with solely one frame labeled, in an end-to-end manner. One major challenge of end-to-end one-shot video grounding is the existence of videos frames that are either irrelevant to the language query or the labeled frame. Another challenge relates to the limited supervision, which might result in ineffective representation learning. To address these challenges, we designed an end-to-end model via Information Tree for One-Shot video grounding (IT-OS). Its key module, the information tree, can eliminate the interference of irrelevant frames based on branch search and branch cropping techniques. In addition, several self-supervised tasks are proposed based on the information tree to improve the representation learning under insufficient labeling. Experiments on the benchmark dataset demonstrate the effectiveness of our model.