arXiv:2605.29610v1 Announce Type: cross Abstract: In scene graph generation, a central challenge is modeling polysemous predicates whose meanings shift across contexts. Prior approaches address this issue by decomposing predicates into multiple static prototypes or retrieving semantically similar exemplars. However, these strategies keep predicate representations static and cannot reorganize semantics to reflect image-specific evidence, leading to systematic confusions in ambiguous contexts. We propose AlignG, which learns context-conditioned predicate semantics via prototype feedback. AlignG
The proliferation of complex visual data and the need for more nuanced AI understanding in dynamic environments makes improving scene graph generation a timely challenge.
This research addresses a fundamental limitation in AI's ability to interpret ambiguous visual contexts, which is critical for advances in areas like autonomous systems and sophisticated AI agents.
AI systems can now infer predicate meanings from context, leading to more accurate and robust scene understanding compared to reliance on static, pre-defined semantic representations.
- · AI researchers
- · Robotics developers
- · Generative AI model creators
- · Computer vision companies
- · AI systems reliant on static predicate definitions
- · Companies with less sophisticated scene understanding technologies
Improved performance in complex visual tasks for AI.
Reduced errors in autonomous system perception and decision-making.
Acceleration of multi-modal AI and more human-like contextual reasoning in AI agents.
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Read at arXiv cs.LG