Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment

Conceptos Básicos

The author proposes CLFA, a novel approach for multi-modal feature alignment using CLIP as a teacher model. This method significantly improves performance on multi-modal sarcasm detection and sentiment analysis tasks.

Resumen

The content discusses the importance of multi-modal semantic understanding and introduces a novel approach called CLFA for cross-modal feature alignment. The paper highlights the challenges in existing methods and presents experimental results showing the effectiveness of CLFA in improving performance on various tasks.

The paper emphasizes the need for integrating information from different modalities to extract users' real intentions behind words. It introduces a novel architecture, CLIP-guided contrastive-learning-based feature alignment, to address the limitations of previous methods.

By leveraging BERT and ViT encoders guided by CLIP, the proposed method aligns features from different modalities into a unified space. This approach significantly outperforms baseline models on multi-modal sarcasm detection and sentiment analysis tasks.

Experimental results demonstrate that CLFA enhances cross-modal interaction during feature fusion, leading to improved performance on challenging tasks like sarcasm detection and sentiment analysis. The method is simple to implement without external knowledge, making it versatile for other multi-modal tasks.

Estadísticas

Our proposed model achieves an F1 score of 83.91 on MMSD.
The model significantly outperforms several baselines on MMSA tasks.
The improvement with our alignment strategy is 4.11 points over BERT+ViT.
External knowledge-enhanced models achieve competitive results but can be further improved with CLFA.
Applying different aggregating methods shows improvements with CLFA across all settings.

Citas

"Our proposed model significantly outperforms several baselines."
"Our model is simple to implement without using task-specific external knowledge."
"CLFA gains large improvement on MMSA and MMSD tasks."

Ideas clave extraídas de

Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment

by Ming Zhang,K... a las arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06355.pdf

Consultas más profundas

How does the use of external knowledge impact the performance compared to feature alignment

The use of external knowledge in multi-modal semantic understanding tasks can have a significant impact on performance compared to feature alignment alone. External knowledge, such as sentiment analysis databases or object recognition results, provides additional context and information that can enhance the model's understanding of the input data. By incorporating external knowledge into the model, it gains access to domain-specific information that may not be present in the training data. This can lead to improved accuracy, precision, recall, and F1 scores by providing more relevant features for classification.
In comparison, feature alignment focuses on aligning representations from different modalities to improve cross-modal interaction and fusion. While feature alignment is crucial for capturing relationships between modalities and enhancing semantic understanding, it may not provide specific domain knowledge that could further boost performance. External knowledge acts as a supplement to feature alignment by enriching the input data with additional insights and nuances that are beneficial for tasks like sarcasm detection and sentiment analysis.

What are the potential applications of CLFA beyond sarcasm detection and sentiment analysis

The concept of CLFA (CLIP-guided Contrastive-Learning-based Feature Alignment) has potential applications beyond sarcasm detection and sentiment analysis in various domains where multi-modal semantic understanding is essential. Some potential applications include:

Visual Question Answering (VQA): CLFA could be applied to VQA tasks where models need to understand both image content and textual questions to provide accurate answers.

Medical Imaging Analysis: In medical imaging analysis, combining visual images with patient reports or medical records could benefit from CLFA for better integration of text-image features.

Social Media Content Analysis: Analyzing social media posts containing text and images requires an understanding of both modalities simultaneously; CLFA can help align these features effectively.

E-commerce Product Recommendation: Multi-modal product recommendation systems could leverage CLFA to align product descriptions with images for more personalized recommendations.

By applying CLFA in these diverse domains, researchers can enhance multi-modal interactions across different types of data sources leading to improved performance in various complex tasks requiring deep semantic understanding.

How can the concept of cross-modal feature alignment be applied in other domains outside of semantic understanding

Cross-modal feature alignment concepts introduced through models like CLFA have broad applicability outside traditional semantic understanding domains:

Autonomous Vehicles: In autonomous driving systems where sensor inputs include visual data (images) along with textual commands or environmental descriptions, cross-modal alignment techniques can help integrate this information effectively for decision-making processes.

Smart Assistants: Cross-modality plays a key role in smart assistants like Siri or Alexa which process voice commands (audio modality) along with contextual information displayed visually on screens; applying feature alignment methods here would improve user interactions.

Education Technology: Educational platforms utilizing multimedia content such as videos paired with textual explanations could benefit from cross-modal alignments ensuring consistency between audio-visual elements aiding comprehension.

By implementing cross-modal feature alignment principles beyond traditional NLP tasks into these areas mentioned above among others will enable enhanced processing capabilities across multiple modes of data representation facilitating more robust AI systems overall.