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Found 10 papers, 4 papers with code
Learning with Noisy Low-Cost MOS for Image Quality Assessment via Dual-Bias Calibration
Learning based image quality assessment (IQA) models have obtained impressive performance with the help of reliable subjective quality labels, where mean opinion score (MOS) is the most popular choice.
Cross-modal Cognitive Consensus guided Audio-Visual Segmentation
Audio-Visual Segmentation (AVS) aims to extract the sounding object from a video frame, which is represented by a pixel-wise segmentation mask.
Distributed Extended Object Tracking Using Coupled Velocity Model from WLS Perspective
Under the consensus scheme, a distributed WLS filter over a realistic network with ``naive'' node is developed by proper weighting of the priors and measurements.
Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning
However, the deficiency of related dataset hinders the development of multi-modal deep learning for egocentric activity recognition.
Forgetting to Remember: A Scalable Incremental Learning Framework for Cross-Task Blind Image Quality Assessment
More specifically, we develop a dynamic parameter isolation strategy to sequentially update the task-specific parameter subsets, which are non-overlapped with each other.
BDG-Net: Boundary Distribution Guided Network for Accurate Polyp Segmentation
However, due to the different sizes of polyps and the unclear boundary between polyps and their surrounding mucosa, it is challenging to segment polyps accurately.
Ranked #20 on
Medical Image Segmentation
on Kvasir-SEG
Distributed Extended Object Tracking Information Filter Over Sensor Networks
This work aims to design a distributed extended object tracking (EOT) system over a realistic network, where both the extent and kinematics are required to retain consensus within the entire network.
Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning
In this paper, we propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning.
Linking Attention-Based Multiscale CNN With Dynamical GCN for Driving Fatigue Detection
Finally, the critical brain regions and connections for driving fatigue detection were investigated through the dynamically learned adjacency matrix. Index Terms— Attention-based multiscale convolutional neural network (CNN), driving fatigue, dynamical graph convolution network (GCN), electroencephalography (EEG), spatiotemporalstructure.
Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image
Then, a subjective study is conducted on our DQA database, which collects the subject-rated scores of all de-rained images.
