1 citations · 3 across the 7 of their papers we have counts for
8 papers
Spatial Reasoning via Modality Switching Between Language and Symbolic Representations
Shreya Rajpal, Tanawan Premsri, Parisa Kordjamshidi
Human reasoning is inherently multimodal: when problems become difficult, we rarely think in words alone. We often externalize our reasoning by sketching diagrams or drawing grids…
SATURN: Symbolic Spatial Reasoning for Multi-Perspective Grounding
Danial Kamali, Tanawan Premsri, Shreya Rajpal +3
Vision-Language Models (VLMs) remain unreliable when spatial reasoning requires composing relations whose meanings depend on frames of reference. Existing tool-augmented spatial re…
Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysis
Sania Sinha, Tanawan Premsri, Danial Kamali +1
Neurosymbolic (NeSy) frameworks combine neural representations and learning with symbolic representations and reasoning. Combining the reasoning capacities, explainability, and int…
FoR-SALE: Frame of Reference-guided Spatial Adjustment in LLM-based Diffusion Editing
Tanawan Premsri, Parisa Kordjamshidi
Current text-to-image generation models, even state-of-the-art models, exhibit a significant performance gap when spatial expressions are described from non-camera perspectives. To…
FoREST: Frame of Reference Evaluation in Spatial Reasoning Tasks
Tanawan Premsri, Parisa Kordjamshidi
Spatial reasoning is a fundamental aspect of human intelligence. One key concept in spatial cognition is the Frame of Reference, which identifies the perspective of spatial express…
Neuro-symbolic Training for Reasoning over Spatial Language
Tanawan Premsri, Parisa Kordjamshidi
Spatial reasoning based on natural language expressions is essential for everyday human tasks. This reasoning ability is also crucial for machines to interact with their environmen…
A Survey on Compositional Learning of AI Models: Theoretical and Experimental Practices
Sania Sinha, Tanawan Premsri, Parisa Kordjamshidi
Compositional learning, mastering the ability to combine basic concepts and construct more intricate ones, is crucial for human cognition, especially in human language comprehensio…
GLUECons: A Generic Benchmark for Learning Under Constraints
Hossein Rajaby Faghihi, Aliakbar Nafar, Chen Zheng +7
Recent research has shown that integrating domain knowledge into deep learning architectures is effective -- it helps reduce the amount of required data, improves the accuracy of t…