arXiv:2408.00305v1 Announce Type: new
Abstract: Cross-modal coherence modeling is essential for intelligent systems to help them organize and structure information, thereby understanding and creating content of the physical world coherently like human-beings. Previous work on cross-modal coherence modeling attempted to leverage the order information from another modality to assist the coherence recovering of the target modality. Despite of the effectiveness, labeled associated coherency information is not always available and might be costly to acquire, making the cross-modal guidance hard to leverage. To tackle this challenge, this paper explores a new way to take advantage of cross-modal guidance without gold labels on coherency, and proposes the Weak Cross-Modal Guided Ordering (WeGO) model. More specifically, it leverages high-confidence predicted pairwise order in one modality as reference information to guide the coherence modeling in another. An iterative learning paradigm is further designed to jointly optimize the coherence modeling in two modalities with selected guidance from each other. The iterative cross-modal boosting also functions in inference to further enhance coherence prediction in each modality. Experimental results on two public datasets have demonstrated that the proposed method outperforms existing methods for cross-modal coherence modeling tasks. Major technical modules have been evaluated effective through ablation studies. Codes are available at: url{https://github.com/scvready123/IterWeGO}.
Cross-modal Coherence Modeling: Unlocking the Potential of Intelligent Systems
Intelligent systems have made tremendous progress in understanding and organizing information from the physical world. However, they still lag behind humans in terms of coherence and context understanding. One key challenge is modeling cross-modal coherence, which involves leveraging information from multiple modalities to create a coherent understanding of the world.
The article introduces the Weak Cross-Modal Guided Ordering (WeGO) model as a novel approach to cross-modal coherence modeling. Unlike previous methods that rely on labeled associated coherency information, WeGO leverages high-confidence predicted pairwise order in one modality as reference information to guide the coherence modeling in another modality. This allows the system to take advantage of cross-modal guidance without the need for expensive or unavailable gold labels on coherency.
Unlocking the Potential of Cross-modal Guidance
This new approach has significant implications for the field of multimedia information systems and related technologies such as animations, artificial reality, augmented reality, and virtual realities. Cross-modal coherence modeling is a multi-disciplinary concept that spans various domains, and WeGO opens up new possibilities for achieving coherence and context understanding in intelligent systems.
One of the key advantages of WeGO is its iterative learning paradigm, which optimizes coherence modeling in two modalities by incorporating selected guidance from each other. This iterative cross-modal boosting not only enhances coherence prediction during model training, but also improves inference performance by further enhancing coherence prediction in each modality. This iterative approach allows the system to continuously refine its understanding and coherence modeling abilities.
Practical Implications and Future Directions
The experimental results on two public datasets showcased the effectiveness of the WeGO model in comparison to existing methods. The major technical modules of WeGO were evaluated through ablation studies, further demonstrating their effectiveness in cross-modal coherence modeling tasks.
As we move forward, the WeGO model holds the potential to enhance various applications and systems that rely on cross-modal coherence, including intelligent assistants, content recommendation systems, and virtual reality experiences. Additionally, this research opens up new avenues for exploring the role of cross-modal guidance in the wider field of multimedia information systems.
In conclusion, the WeGO model represents a significant advancement in the field of cross-modal coherence modeling. By leveraging cross-modal guidance without the need for labeled coherency information, it unlocks greater potential for intelligent systems to understand and create content coherently, ultimately bridging the gap between human-like understanding and machine intelligence.