6 papers
NAIPv2: Debiased Pairwise Learning for Efficient Paper Quality Estimation
Penghai Zhao, Jinyu Tian, Qinghua Xing +5
The ability to estimate the quality of scientific papers is central to how both humans and AI systems will advance scientific knowledge in the future. However, existing LLM-based e…
Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Training Framework
Wenhao Tang, Heng Fang, Ge Wu +2
Computational pathology (CPath) digitizes pathology slides into whole slide images (WSIs), enabling analysis for critical healthcare tasks such as cancer diagnosis and prognosis. H…
DenoDet V2: Phase-Amplitude Cross Denoising for SAR Object Detection
Kang Ni, Minrui Zou, Yuxuan Li +4
One of the primary challenges in Synthetic Aperture Radar (SAR) object detection lies in the pervasive influence of coherent noise. As a common practice, most existing methods, whe…
Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology
Wenhao Tang, Rong Qin, Heng Fang +4
Pre-trained encoders for offline feature extraction followed by multiple instance learning (MIL) aggregators have become the dominant paradigm in computational pathology (CPath), b…
A Simple Detector with Frame Dynamics is a Strong Tracker
Chenxu Peng, Chenxu Wang, Minrui Zou +5
Infrared object tracking plays a crucial role in Anti-Unmanned Aerial Vehicle (Anti-UAV) applications. Existing trackers often depend on cropped template regions and have limited m…
A Vision for Auto Research with LLM Agents
Chengwei Liu, Chong Wang, Jiayue Cao +16
This paper introduces Agent-Based Auto Research, a structured multi-agent framework designed to automate, coordinate, and optimize the full lifecycle of scientific research. Levera…