Online Pseudo-Label Unified Object Detection for Multiple Datasets Training

The Unified Object Detection (UOD) task aims to achieve object detection of all merged categories through training on multiple datasets, and is of great significance in comprehensive object detection scenarios. In this paper, we conduct a thorough analysis of the cross datasets missing annotations issue, and propose an Online Pseudo-Label Unified Object Detection scheme. Our method uses a periodically updated teacher model to generate pseudo-labels for the unlabelled objects in each sub-dataset. This periodical update strategy could better ensure that the accuracy of the teacher model reaches the local maxima and maximized the quality of pseudo-labels. In addition, we survey the influence of overlapped region proposals on the accuracy of box regression. We propose a category specific box regression and a pseudo-label RPN head to improve the recall rate of the Region Proposal Network (PRN). Our experimental results on common used benchmarks (\eg COCO, Object365 and OpenImages) indicates that our online pseudo-label UOD method achieves higher accuracy than existing SOTA methods.

D2SL: Decouple Defogging and Semantic Learning for Foggy Domain-Adaptive Segmentation

We investigated domain adaptive semantic segmentation in foggy weather scenarios, which aims to enhance the utilization of unlabeled foggy data and improve the model's adaptability to foggy conditions. Current methods rely on clear images as references, jointly learning defogging and segmentation for foggy images. Despite making some progress, there are still two main drawbacks: (1) the coupling of segmentation and defogging feature representations, resulting in a decrease in semantic representation capability, and (2) the failure to leverage real fog priors in unlabeled foggy data, leading to insufficient model generalization ability. To address these issues, we propose a novel training framework, Decouple Defogging and Semantic learning, called D2SL, aiming to alleviate the adverse impact of defogging tasks on the final segmentation task. In this framework, we introduce a domain-consistent transfer strategy to establish a connection between defogging and segmentation tasks. Furthermore, we design a real fog transfer strategy to improve defogging effects by fully leveraging the fog priors from real foggy images. Our approach enhances the semantic representations required for segmentation during the defogging learning process and maximizes the representation capability of fog invariance by effectively utilizing real fog data. Comprehensive experiments validate the effectiveness of the proposed method.

Residual Recurrent CRNN for End-to-End Optical Music Recognition on Monophonic Scores

Optical Music Recognition is a field that attempts to extract digital information from images of either the printed music scores or the handwritten music scores. One of the challenges of the Optical Music Recognition task is to transcript the symbols of the camera-captured images into digital music notations. Previous end-to-end model, based on deep learning, was developed as a Convolutional Recurrent Neural Network. However, it does not explore sufficient contextual information from full scales and there is still a large room for improvement. In this paper, we propose an innovative end-to-end framework that combines a block of Residual Recurrent Convolutional Neural Network with a recurrent Encoder-Decoder network to map a sequence of monophonic music symbols corresponding to the notations present in the image. The Residual Recurrent Convolutional block can improve the ability of the model to enrich the context information while the number of parameter will not be increasing. The experiment results were benchmarked against a publicly available dataset called CAMERA-PRIMUS. We evaluate the performances of our model on both the images with ideal conditions and that with non-ideal conditions. The experiments show that our approach surpass the state-of-the-art end-to-end method using Convolutional Recurrent Neural Network.