Research on Vision-Language Question Answering Models for Industrial Robots
A hierarchical cross-modal fusion model is proposed for vision-language question answering (VLQA) in industrial robotics, targeting the challenges of semantic ambiguity, complex environmental layouts, and domain-specific terminology common in modern manufacturing. The framework integrates advanced object detection, multi-scale visual encoding, syntactic parsing, and task-aware semantic attention to unite vision and language signals into a joint reasoning space. Region-based deep networks extract visual features, weighted embeddings aggregate, and recurrent neural parsing encodes sentence structures. Through fine-grained semantic alignment driven by adaptive fusion and cross-attention mech...
| Authors | Ping Li, Bartlomiej Brzozka |
|---|---|
| Primary category | cs.CV |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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