Search Results for author:
Found 5 papers, 3 papers with code
Prompt-Driven Dynamic Object-Centric Learning for Single Domain Generalization
In this paper, we propose a dynamic object-centric perception network based on prompt learning, aiming to adapt to the variations in image complexity.
Prototype-guided Cross-task Knowledge Distillation for Large-scale Models
Considering the complexity and variability of real scene tasks, we propose a Prototype-guided Cross-task Knowledge Distillation (ProC-KD) approach to transfer the intrinsic local-level object knowledge of a large-scale teacher network to various task scenarios.
Peng Cheng Object Detection Benchmark for Smart City
Considering the diversity and complexity of scenes in intelligent city governance, we build a large-scale object detection benchmark for the smart city.
LineCounter: Learning Handwritten Text Line Segmentation by Counting
In this paper, we propose a novel Line Counting formulation for HTLS -- that involves counting the number of text lines from the top at every pixel location.
SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization
The AST module further consolidates the outputs from MWS and PWA and predicts the final adaptive threshold for each pixel location.
