To create rich visualizations, data analysts often need to iterate back and forth among data processing and chart specification to achieve their goals.
These requisites, which we refer to as the Agent Communication Trilemma, are hard to achieve in large networks of agents.
Multi-agent systems, where multiple agents (generative AI models + tools) collaborate, are emerging as an effective pattern for solving long-running, complex tasks in numerous domains.
Benefiting from its heuristic search design, SAM2Long is robust toward occlusions and object reappearances, and can effectively segment and track objects for complex long-term videos.
When pretrained on Objects365, D-FINE-L / X attains 57. 1% / 59. 3% AP, surpassing all existing real-time detectors.
Ranked #1 on Real-Time Object Detection on MS COCO (using extra training data)
In this paper, we identify the key issue as the redundant content in videos.
Specifically, WebRL incorporates 1) a self-evolving curriculum that generates new tasks from unsuccessful attempts, 2) a robust outcome-supervised reward model (ORM), and 3) adaptive reinforcement learning strategies to ensure consistent improvements.
Recent advancements in Large Language Models (LLMs) have expanded their capabilities to multimodal contexts, including comprehensive video understanding.
In this paper, we propose LLM2CLIP, a novel approach that embraces the power of LLMs to unlock CLIP's potential.
Geometry is a ubiquitous tool in computer graphics, design, and engineering.