Search Results for author:
Found 8 papers, 6 papers with code
Taming Pre-trained LLMs for Generalised Time Series Forecasting via Cross-modal Knowledge Distillation
Recently, with the surge of the Large Language Models (LLMs), several works have attempted to introduce LLMs into time series forecasting.
Knowledge Distillation
Multivariate Time Series Forecasting
+2
MambaIR: A Simple Baseline for Image Restoration with State-Space Model
In this way, our MambaIR takes advantage of the local pixel similarity and reduces the channel redundancy.
AdaptIR: Parameter Efficient Multi-task Adaptation for Pre-trained Image Restoration Models
Recently, Parameter Efficient Transfer Learning (PETL) offers an efficient alternative solution to full fine-tuning, yet still faces great challenges for pre-trained image restoration models, due to the diversity of different degradations.
One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer
Current solutions for low-resolution text recognition (LTR) typically rely on a two-stage pipeline that involves super-resolution as the first stage followed by the second-stage recognition.
Towards Robust Scene Text Image Super-resolution via Explicit Location Enhancement
Scene text image super-resolution (STISR), aiming to improve image quality while boosting downstream scene text recognition accuracy, has recently achieved great success.
MGTR: End-to-End Mutual Gaze Detection with Transformer
People's looking at each other or mutual gaze is ubiquitous in our daily interactions, and detecting mutual gaze is of great significance for understanding human social scenes.
Dissecting Local Properties of Adversarial Examples
Adversarial examples have attracted significant attention over the years, yet a sufficient understanding is in lack, especially when analyzing their performances in combination with adversarial training.
Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud
We show that our models detect nearly all malicious flows for 2 of the 4 cloud IPs under attack (at least 99. 99%) and detect most malicious flows (94. 75% and 91. 37%) for the remaining 2 IPs.
