Search Results for author:
Found 8 papers, 2 papers with code
Bilateral Memory Consolidation for Continual Learning
Humans are proficient at continuously acquiring and integrating new knowledge.
Pro-tuning: Unified Prompt Tuning for Vision Tasks
To this end, we propose parameter-efficient Prompt tuning (Pro-tuning) to adapt frozen vision models to various downstream vision tasks.
TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers
In addition, TR-MISR adopts an additional learnable embedding vector that fuses these vectors to restore the details to the greatest extent. TR-MISR has successfully applied the transformer to MISR tasks for the first time, notably reducing the difficulty of training the transformer by ignoring the spatial relations of image patches.
Ranked #1 on
Multi-Frame Super-Resolution
on PROBA-V
AME: Attention and Memory Enhancement in Hyper-Parameter Optimization
Training Deep Neural Networks (DNNs) is inherently subject to sensitive hyper-parameters and untimely feedbacks of performance evaluation.
Differentiable Convolution Search for Point Cloud Processing
It can work in a purely data-driven manner and thus is capable of auto-creating a group of suitable convolutions for geometric shape modeling.
Adaptive Remote Sensing Image Attribute Learning for Active Object Detection
In recent years, deep learning methods bring incredible progress to the field of object detection.
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
kNN Hashing With Factorized Neighborhood Representation
Hashing is very effective for many tasks in reducing the processing time and in compressing massive databases.
