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We report a first effort to model the solution of meaningful four-term visual analogies, by combining a machine-vision model (ResNet50-A) that can classify pixel-level images into object categories, with a cognitive model (BART) that takes semantic representations of words as input and identifies semantic relations instantiated by a word pair. Each model achieves above-chance performance in selecting the best analogical option from a set of four. However, combining the visual and the semantic models increases analogical performance above the level achieved by either model alone. The contribution of vision to reasoning thus may extend beyond simply generating verbal representations from images. These findings provide a proof of concept that a comprehensive model can solve semantically-rich analogies from pixel-level inputs.
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Open Vocabulary Semantic Scene Sketch Understanding
We study the underexplored but fundamental vision problem of machine understanding of abstract freehand scene sketches We introduce a sketch encoder that results in semantically- aware feature space, which we evaluate by testing its performance on a semantic sketch seg- mentation task. To train our model we rely only on the availability of bitmap sketches with their brief captions and do not require any pixel-level annotations. To obtain generalization to a large set of sketches and categories, we build on a vision transformer encoder pretrained with the CLIP model. We freeze the text encoder and perform visual-prompt tuning of the visual encoder branch while introducing a set of critical modifications. Firstly, we augment the classical key-query (k-q) self-attention blocks with value-value (v-v) self-attention blocks. Central to our model is a two-level hierarchical network design that enables efficient semantic disentanglement: The first level ensures holistic scene sketch encoding, and the second level focuses on individual categories. We, then, in the second level of the hierarchy, introduce a cross-attention between textual and visual branches. Our method outperforms zero-shot CLIP pixel accuracy of segmentation results by 37 points, reaching an accuracy of 85.5% on the FS-COCO sketch dataset. Finally, we conduct a user study that allows us to identify further improvements needed over our method to reconcile machine and human understanding of scene sketches.
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- Award ID(s):
- 2436199
- PAR ID:
- 10593242
- Publisher / Repository:
- Computer Vision Foundation
- Date Published:
- Format(s):
- Medium: X
- Location:
- Seattle, WA, USA
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
