Search Results for author:
Found 10 papers, 4 papers with code
Stacking Networks Dynamically for Image Restoration Based on the Plug-and-Play Framework
Recently, stacked networks show powerful performance in Image Restoration, such as challenging motion deblurring problems.
BENO: Boundary-embedded Neural Operators for Elliptic PDEs
Here we introduce Boundary-Embedded Neural Operators (BENO), a novel neural operator architecture that embeds the complex geometries and inhomogeneous boundary values into the solving of elliptic PDEs.
When Parameter-efficient Tuning Meets General-purpose Vision-language Models
Instruction tuning has shown promising potential for developing general-purpose AI capabilities by using large-scale pre-trained models and boosts growing research to integrate multimodal information for creative applications.
PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction
In this paper, we investigate the challenge of spatio-temporal video prediction, which involves generating future videos based on historical data streams.
Visual Tuning
With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer.
LION: Implicit Vision Prompt Tuning
Despite recent competitive performance across a range of vision tasks, vision Transformers still have an issue of heavy computational costs.
Prototypical Mixing and Retrieval-Based Refinement for Label Noise-Resistant Image Retrieval
This paper investigates a realistic but understudied problem of image retrieval under label noise, which could lead to severe overfitting or memorization of noisy samples during optimization.
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
Obj2Seq is able to flexibly determine input categories to satisfy customized requirements, and be easily extended to different visual tasks.
A Survey on Deep Hashing Methods
Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining.
Deep Dynamic Boosted Forest
Specically, we propose to measure the quality of each leaf node of every decision tree in the random forest to determine hard examples.
