This paper presents a unified grammatical framework capable of reconstructing a variety of scene types (e.g., urban, campus, country etc.) from a single input image. The key idea of our approach is to study a novel commonsense reasoning framework that mainly exploits two types of prior knowledge: (i) prior distributions over a single dimension of objects, e.g., that the length of a sedan is about 4.5 meters; (ii) pair-wise relationships between the dimensions of scene entities, e.g., that the length of a sedan is shorter than a bus. These unary or relative geometric knowledge, once extracted, are fairly stable across different types of natural scenes, and are informative for enhancing the understanding of various scenes in both 2D images and 3D world. Methodologically, we propose to construct a hierarchical graph representation as a unified representation of the input image and related geometric knowledge. We formulate these objectives with a unified probabilistic formula and develop a data-driven Monte Carlo method to infer the optimal solution with both bottom-to-up and top-down computations. Results with comparisons on public datasets showed that our method clearly outperforms the alternative methods.
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Joint Commonsense and Relation Reasoning for Image and Video Captioning
Exploiting relationships between objects for image and video
captioning has received increasing attention. Most existing
methods depend heavily on pre-trained detectors of objects
and their relationships, and thus may not work well when
facing detection challenges such as heavy occlusion, tiny-size
objects, and long-tail classes. In this paper, we propose a joint
commonsense and relation reasoning method that exploits
prior knowledge for image and video captioning without relying on any detectors. The prior knowledge provides semantic
correlations and constraints between objects, serving as guidance to build semantic graphs that summarize object relationships, some of which cannot be directly perceived from images or videos. Particularly, our method is implemented by an
iterative learning algorithm that alternates between 1) commonsense reasoning for embedding visual regions into the
semantic space to build a semantic graph and 2) relation reasoning for encoding semantic graphs to generate sentences.
Experiments on several benchmark datasets validate the effectiveness of our prior knowledge-based approach.
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- NSF-PAR ID:
- 10169172
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- ISSN:
- 2159-5399
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
