Search Results for author:
Found 8 papers, 7 papers with code
UniDM: A Unified Framework for Data Manipulation with Large Language Models
In this paper, inspired by the cross-task generality of LLMs on NLP tasks, we pave the first step to design an automatic and general solution to tackle with data manipulation tasks.
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
Our explorations highlight open-source LLMs' potential in Text-to-SQL, as well as the advantages and disadvantages of the supervised fine-tuning.
Ranked #2 on
Text-To-SQL
on spider
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition
In this work, we propose to automatically design efficient 3D CNN architectures via a novel training-free neural architecture search approach tailored for 3D CNNs considering the model complexity.
Ranked #85 on
Action Recognition
on Something-Something V2
Entroformer: A Transformer-based Entropy Model for Learned Image Compression
One critical component in lossy deep image compression is the entropy model, which predicts the probability distribution of the quantized latent representation in the encoding and decoding modules.
Interpolation variable rate image compression
Compression standards have been used to reduce the cost of image storage and transmission for decades.
Spatiotemporal Entropy Model is All You Need for Learned Video Compression
The framework of dominant learned video compression methods is usually composed of motion prediction modules as well as motion vector and residual image compression modules, suffering from its complex structure and error propagation problem.
Learning Accurate Entropy Model with Global Reference for Image Compression
In this work, we propose a novel Global Reference Model for image compression to effectively leverage both the local and the global context information, leading to an enhanced compression rate.
Unsupervised Face Normalization With Extreme Pose and Expression in the Wild
Face normalization provides an effective and cheap way to distil face identity and dispel face variances for recognition.
