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Found 10 papers, 4 papers with code
Diffusion Models Trained with Large Data Are Transferable Visual Models
We show that, simply initializing image understanding models using a pre-trained UNet (or transformer) of diffusion models, it is possible to achieve remarkable transferable performance on fundamental vision perception tasks using a moderate amount of target data (even synthetic data only), including monocular depth, surface normal, image segmentation, matting, human pose estimation, among virtually many others.
A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook
Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques.
Vision Language Models in Autonomous Driving and Intelligent Transportation Systems
The applications of Vision-Language Models (VLMs) in the fields of Autonomous Driving (AD) and Intelligent Transportation Systems (ITS) have attracted widespread attention due to their outstanding performance and the ability to leverage Large Language Models (LLMs).
3D Understanding of Deformable Linear Objects: Datasets and Transferability Benchmark
Finally, we analyzed the generalization capabilities of these methods by conducting transferability experiments on the PointWire and PointVessel datasets.
Implementing a new fully stepwise decomposition-based sampling technique for the hybrid water level forecasting model in real-world application
Results of VMD-based hybrid model using FSDB sampling technique show that Nash-Sutcliffe Efficiency (NSE) coefficient is increased by 6. 4%, 28. 8% and 7. 0% in three stations respectively, compared with those obtained from the currently most advanced sampling technique.
Looking and Listening: Audio Guided Text Recognition
Text recognition in the wild is a long-standing problem in computer vision.
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.
Neuro-Causal Factor Analysis
Factor analysis (FA) is a statistical tool for studying how observed variables with some mutual dependences can be expressed as functions of mutually independent unobserved factors, and it is widely applied throughout the psychological, biological, and physical sciences.
ICDAR 2023 Competition on Reading the Seal Title
To promote research in this area, we organized ICDAR 2023 competition on reading the seal title (ReST), which included two tasks: seal title text detection (Task 1) and end-to-end seal title recognition (Task 2).
3D Object Detection with a Self-supervised Lidar Scene Flow Backbone
Our main contribution leverages learned flow and motion representations and combines a self-supervised backbone with a supervised 3D detection head.
