Ming‐Ming Cheng

RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks

2022 · arXiv (Cornell University)

Temporal/spatial receptive fields of models play an important role in sequential/spatial tasks. Large receptive fields facilitate long-term relations, while small receptive fields help to capture the local details. Existing methods construct models with hand-designed receptive …

Advancing Textual Prompt Learning with Anchored Attributes

2024 · arXiv (Cornell University)

Textual-based prompt learning methods primarily employ multiple learnable soft prompts and hard class tokens in a cascading manner as text inputs, aiming to align image and text (category) spaces for downstream tasks. However, current training …

A Vision for Auto Research with LLM Agents

2025 · arXiv (Cornell University)

This paper introduces Agent-Based Auto Research, a structured multi-agent framework designed to automate, coordinate, and optimize the full lifecycle of scientific research. Leveraging the capabilities of large language models (LLMs) and modular agent collaboration, the …