Deep Semantics for Explainable Visuospatial Intelligence : Perspectives on Integrating Commonsense Spatial Abstractions and Low-Level Neural Features
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Summary
This paper addresses the challenge of achieving high-level semantic interpretation and explainability in visuospatial intelligence by integrating commonsense spatial abstractions with low-level neural features. The authors argue that while deep learning has advanced visual computing, it lacks the declarative reasoning capabilities necessary for tasks such as semantic question-answering, relational concept learning, and non-monotonic abduction. To bridge this gap, the paper proposes a neurosymbolic framework termed "deep semantics," which combines robust low-level visual detection with formal knowledge representation methods to enable domain-independent, explainable reasoning about space, time, and motion. The methodology relies on a modular integration of two primary components: declarative programming paradigms for commonsense reasoning and state-of-the-art deep learning for visual perception. The symbolic layer utilizes Constraint Logic Programming (CLP), Inductive Logic Programming (ILP), and Answer Set Programming (ASP) to model qualitative spatial relations (e.g., mereotopology, orientation, distance) and temporal dynamics. The perceptual layer employs deep learning models, including Faster R-CNN, YOLOv3, TinyFaces, and OpenPose, for object detection, tracking, and pose estimation. This hybrid architecture allows the system to process multimodal data, including video, audio, and eye-tracking inputs, to generate structured, relational representations of scenes. The paper demonstrates the efficacy of this approach through three specific application cases. First, in autonomous driving, the system addresses real-time visuospatial abduction, such as anticipating the reappearance of occluded cyclists by maintaining identity and computing counterfactuals, thereby supporting ethical and regulatory requirements for explainable AI. Second, the authors present a model for semantically guided neural learning, where high-level spatial descriptions (e.g., symmetry or specific relational configurations like "man on elephant") guide the optimization of neural network loss functions. This allows the system to calculate divergence between predicted scene structures and declarative spatial constraints, enhancing interpretability. Third, the framework is applied to cognitive vision research in psychology, specifically analyzing visuo-auditory perception in film. By integrating eye-tracking data with computational narrative structures, the system learns behavioral models of human attention, such as gaze transitions driven by on-screen character movements. The significance of this work lies in its contribution to the field of cognitive vision by providing a systematic method for integrating artificial intelligence, logic, and spatial cognition. By grounding neural features in declarative, commonsense models, the approach enables explainable visual interpretation and relational learning that are currently lacking in purely data-driven systems. The authors conclude that this neurosymbolic integration is essential for developing human-centered AI solutions that meet legal, ethical, and industrial standards, particularly in safety-critical domains like autonomous driving and in understanding complex human behaviors in behavioral research.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-29.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-29 |
| archive | success | canonical_url | — | — | 6 | 2026-06-09 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-29 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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