Rob Barton

Composed Image Retrieval (CIR) targets the retrieval of images conditioned on a reference image and a textual modification, but constructing labeled triplets (reference image, textual modification, target image) is inherently challenging. Existing Zero-Shot CIR (ZS-CIR) approaches often rely on well-aligned vision-language models (VLMs) to combine visual and textual inputs, or use large language models (LLMs) for richer modification understanding.

Unifying image clustering across different clustering scenarios remains challenging due to fundamental gaps among tasks. We introduce a Guideline-Driven Image Clustering Agent, the first universal framework that bridges these gaps through textual guidelines. To incorporate complex guidelines without task-specific training, we propose Generative Concept Proxy Modeling, which generates guideline-aware embeddings via concept proxy extraction.

The Model Context Protocol (MCP) and Agent-to-Agent (A2A) have gained a significant industry attention over the past year. MCP first grabbed the world’s attention in dramatic fashion when it was published by Anthropic in November 2024, garnering tens of thousands of stars on GitHub within the first month. Organizations quickly saw the value of MCP as a way to abstract APIs into natural language, allowing LLMs to easily interpret and use them as tools.