Collaborative positional attention for image to English question answering.

Area of Science:

• Artificial Intelligence
• Computer Vision
• Natural Language Processing

Background:

• Traditional multi-head attention in Visual Question Answering (VQA) faces limitations in inter-attention head communication and positional information encoding.
• These limitations hinder effective modeling of both intra-modal and cross-modal relationships crucial for VQA tasks.

Purpose of the Study:

• To propose a novel position-aware collaborative attention framework to overcome the limitations of traditional attention mechanisms in VQA.
• To enhance the interaction between attention heads and improve the capture of positional information for better multi-modal understanding.

Main Methods:

• Introduced an Inter-Head Communication Matrix (IHCM) within multi-head attention to facilitate information sharing across heads.
• Developed Intra-modal Self-Attention with Collaboration (IMSAC) for refining single-modality features and Cross-modal Guided Attention with Collaboration (CMGAC) for text-guided image attention.
• Incorporated absolute positional encoding into the self-attention mechanism to improve semantic understanding of textual features.

Main Results:

• The proposed collaborative attention framework consistently improved accuracy across various question categories on the TDIUC, VQA-CP v2, and GQA datasets.
• The combination of IMSAC and CMGAC achieved the best performance, demonstrating the effectiveness of collaborative attention and positional encoding.
• Ablation studies confirmed the significant contributions of inter-head collaboration and positional encoding in bridging the semantic gap and enhancing cross-modal reasoning.

Conclusions:

• The novel position-aware collaborative attention framework offers a robust solution for Visual Question Answering, outperforming existing attention-based methods.
• The framework exhibits superior resilience to language bias and enhanced compositional reasoning capabilities, addressing key challenges in multi-modal learning.