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Found 8 papers, 1 papers with code
Context Autoencoder for Self-Supervised Representation Learning
We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised learning.
Oriented Object Detection with Transformer
Object detection with Transformers (DETR) has achieved a competitive performance over traditional detectors, such as Faster R-CNN.
Probabilistic Ranking-Aware Ensembles for Enhanced Object Detections
This leads to a new problem of confidence discrepancy for the detector ensembles.
The 1st Tiny Object Detection Challenge:Methods and Results
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.
2nd Place Solution in Google AI Open Images Object Detection Track 2019
We present an object detection framework based on PaddlePaddle.
Stock Forecasting using M-Band Wavelet-Based SVR and RNN-LSTMs Models
The task of predicting future stock values has always been one that is heavily desired albeit very difficult.
Object detection and tracking benchmark in industry based on improved correlation filter
Real-time object detection and tracking have shown to be the basis of intelligent production for industrial 4. 0 applications.
Modulated Convolutional Networks
In this paper, we propose new Modulated Convolutional Networks (MCNs) to improve the portability of CNNs via binarized filters.
