CLIP Enhanced Blockwise Classification

CLIP-enhanced blockwise classification leverages the strong cross-modal capabilities of CLIP (Contrastive Language–Image Pre-training) to improve various image-related tasks, moving beyond its initial zero-shot classification applications. Current research focuses on adapting CLIP for tasks like object detection, semantic segmentation, and even crowd counting, often employing techniques such as region prompting, enhanced blockwise classification frameworks, and integration with other models (e.g., LLMs) to address limitations in handling dense prediction problems or improve accuracy. These advancements demonstrate the versatility of CLIP and its potential to significantly impact fields requiring robust image understanding, including medical image analysis and video understanding.