To overcome the problem, we propose a prompt retrieval framework to automate the selection of in-context examples.
To further exploit the potential of the transformer, we propose a novel flexible window training strategy.
Our Fast-BEV consists of five parts, We novelly propose (1) a lightweight deployment-friendly view transformation which fast transfers 2D image feature to 3D voxel space, (2) an multi-scale image encoder which leverages multi-scale information for better performance, (3) an efficient BEV encoder which is particularly designed to speed up on-vehicle inference.
With such multi-dimension and multi-scale factorization, our MorphMLP block can achieve a great accuracy-computation balance.
Ranked #18 on Action Recognition on Something-Something V2 (using extra training data)
We study the design decisions of publicly available instruction tuning methods, and break down the development of Flan 2022 (Chung et al., 2022).
By evaluating on a number of benchmark test sets, we find that ChatGPT performs competitively with commercial translation products (e. g., Google Translate) on high-resource European languages but lags behind significantly on low-resource or distant languages.
The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test.
Large language models (LLMs) such as GPT-3 and ChatGPT have recently demonstrated impressive results across a wide range of tasks.
This paper deals with the problem of audio source separation.