The recent developments in the research area highlight a significant shift towards leveraging large language models (LLMs) and vision-language models (VLMs) for enhancing various computer vision tasks, including quality assessment, visual question answering, and classification. A common theme across these advancements is the innovative use of multimodal approaches to bridge the gap between visual data and textual descriptions, aiming for more accurate and human-aligned assessments and interpretations.

In the realm of quality assessment, there's a notable move towards simulating human subjective evaluation processes more closely. This involves adopting strategies that go beyond direct mapping functions to quality scores, instead focusing on discrete quality descriptions and opinion score distributions. Such approaches aim to capture the nuanced and often subjective nature of human perception, leading to more accurate and reliable quality assessments.

Visual question answering (VQA) research is also evolving, with a focus on zero-shot learning scenarios that do not require training samples. The integration of knowledge graphs with LLMs is emerging as a powerful strategy to enhance the model's ability to interpret and answer visual questions accurately. This combination leverages the rich entity relationships in knowledge graphs and the deep understanding capabilities of LLMs, offering a more comprehensive approach to VQA.

Patent figure classification is another area witnessing innovative applications of VLMs, particularly in zero-shot and few-shot learning contexts. The introduction of new datasets and classification strategies, such as tournament-style classification, demonstrates the potential of VLMs to handle complex classification tasks efficiently.

Lastly, the concept of multi-aspect knowledge distillation using MLLMs is gaining traction. This approach aims to enrich the model's understanding by incorporating various aspects of knowledge, beyond just class labels, into the learning process. This not only enhances the model's performance on specific tasks like image classification but also opens up possibilities for extending these benefits to other computer vision tasks.

Noteworthy Papers:

• CLIP-PCQA: Introduces a novel language-driven method for point cloud quality assessment, simulating subjective evaluation through discrete quality descriptions and opinion score distributions.
• DeQA-Score: Proposes a distribution-based approach for image quality assessment, leveraging MLLMs to regress accurate quality scores by preserving score distribution characteristics.
• Combining Knowledge Graph and LLMs for Enhanced Zero-shot Visual Question Answering: Demonstrates the effectiveness of integrating knowledge graphs with LLMs for improving zero-shot visual question answering accuracy.
• Patent Figure Classification using Large Vision-language Models: Explores the use of LVLMs for patent figure classification, introducing new datasets and a tournament-style classification strategy.
• Multi-aspect Knowledge Distillation with Large Language Model: Presents a method for distilling multi-aspect knowledge from MLLMs into models, enhancing their understanding and performance across various computer vision tasks.