7 papers
Visual Instruction Pretraining for Domain-Specific Foundation Models
Yuxuan Li, Yicheng Zhang, Wenhao Tang +4
Modern computer vision is converging on a closed loop in which perception, reasoning and generation mutually reinforce each other. However, this loop remains incomplete: the top-do…
AnchorOPT: Towards Optimizing Dynamic Anchors for Adaptive Prompt Learning
Zheng Li, Yibing Song, Xin Zhang +3
Existing prompt learning methods, which are built upon CLIP models, leverage textual tokens as anchors to guide the learnable soft tokens. This guidance improves CLIP generalizatio…
A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence
Penghai Zhao, Xin Zhang, Jiayue Cao +3
The rapid growth of research in Pattern Analysis and Machine Intelligence (PAMI) has rendered literature reviews essential for consolidating and interpreting knowledge across its m…
LSKNet: A Foundation Lightweight Backbone for Remote Sensing
Yuxuan Li, Xiang Li, Yimian Dai +5
Remote sensing images pose distinct challenges for downstream tasks due to their inherent complexity. While a considerable amount of research has been dedicated to remote sensing c…
SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection
Yuxuan Li, Xiang Li, Weijie Li +4
Synthetic Aperture Radar (SAR) object detection has gained significant attention recently due to its irreplaceable all-weather imaging capabilities. However, this research field su…
Advancing Textual Prompt Learning with Anchored Attributes
Zheng Li, Yibing Song, Ming-Ming Cheng +2
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 (c…