Web Image

Web image analysis focuses on understanding and leveraging the vast quantity of images available online, aiming to improve tasks like image classification, generation, and retrieval. Current research emphasizes addressing inherent issues in web image datasets, such as noisy labels, content bias, and label ambiguity, using techniques like generalized KL divergence for improved data cleaning and large language models (LLMs) for caption enhancement. These advancements are crucial for improving the performance of vision-language models and enabling more robust and reliable applications in areas such as image search, content generation, and forensic analysis.