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Found 21 papers, 4 papers with code
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection
Furthermore, we design DefenseNet as a learn-able pre-processing to the adversarial cloudy images so as to preserve the performance of the deep learning based remote sensing SOD model, without tuning the already deployed deep SOD model.
Real-Time Super-Resolution for Real-World Images on Mobile Devices
Image Super-Resolution (ISR), which aims at recovering High-Resolution (HR) images from the corresponding Low-Resolution (LR) counterparts.
Pik-Fix: Restoring and Colorizing Old Photos
Our proposed framework consists of three modules: a restoration sub-network that conducts restoration from degradations, a similarity network that performs color histogram matching and color transfer, and a colorization subnet that learns to predict the chroma elements of images conditioned on chromatic reference signals.
ROMNet: Renovate the Old Memories
Renovating the memories in old photos is an intriguing research topic in computer vision fields.
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services.
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
GIA-Net: Global Information Aware Network for Low-light Imaging
In this paper, we propose a global information aware (GIA) module, which is capable of extracting and integrating the global information into the network to improve the performance of low-light imaging.
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition
In this paper, we propose a simple yet effective approach, named Point Adversarial Self Mining (PASM), to improve the recognition accuracy in facial expression recognition.
Omni-supervised Facial Expression Recognition via Distilled Data
However, the performance of the current state-of-the-art facial expression recognition (FER) approaches is directly related to the labeled data for training.
Facial Expression Recognition
Facial Expression Recognition (FER)
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction
In this paper, we propose a Residual Channel Attention-Generative Adversarial Network(RCA-GAN) to solve these problems.
Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild
In this paper, we proposed two strategies to fuse information extracted from different modalities, i. e., audio and visual.
Identity-Free Facial Expression Recognition using conditional Generative Adversarial Network
A novel Identity-Free conditional Generative Adversarial Network (IF-GAN) was proposed for Facial Expression Recognition (FER) to explicitly reduce high inter-subject variations caused by identity-related facial attributes, e. g., age, race, and gender.
Facial Expression Recognition
Facial Expression Recognition (FER)
Probabilistic Attribute Tree in Convolutional Neural Networks for Facial Expression Recognition
In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN) to explicitly deal with the large intra-class variations caused by identity-related attributes, e. g., age, race, and gender.
Facial Expression Recognition
Facial Expression Recognition (FER)
Island Loss for Learning Discriminative Features in Facial Expression Recognition
Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition.
Ranked #3 on
Facial Expression Recognition (FER)
on SFEW
Facial Expression Recognition
Facial Expression Recognition (FER)
Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition
Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial AU recognition, where predefined and fixed convolution filter sizes are employed.
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition
Experimental results on four benchmark AU databases have demonstrated that the IB-CNN yields significant improvement over the traditional CNN and the boosting CNN without incremental learning, as well as outperforming the state-of-the-art CNN-based methods in AU recognition.
Improving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion
Instead of solely improving visual observations, this paper presents a novel audiovisual fusion framework, which makes the best use of visual and acoustic cues in recognizing speech-related facial AUs.
Detecting Small Signs from Large Images
Experimental results on a real-world conditioned traffic sign dataset have demonstrated the effectiveness of the proposed method in terms of detection accuracy and recall, especially for those with small sizes.
Listen to Your Face: Inferring Facial Action Units from Audio Channel
Different from all prior work that utilized visual observations for facial AU recognition, this paper presents a novel approach that recognizes speech-related AUs exclusively from audio signals based on the fact that facial activities are highly correlated with voice during speech.
Facial Expression Recognition via a Boosted Deep Belief Network
A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction.
Facial Expression Recognition
Facial Expression Recognition (FER)
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