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Found 8 papers, 2 papers with code
An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition
Micro-expression recognition (MER) in low-resolution (LR) scenarios presents an important and complex challenge, particularly for practical applications such as group MER in crowded environments.
Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning
Group-level emotion recognition (GER) is an inseparable part of human behavior analysis, aiming to recognize an overall emotion in a multi-person scene.
Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels
However, these CL methods fail to be directly adapted to multi-label image classification due to the difficulty in defining the positive and negative instances to contrast a given anchor image in multi-label scenario, let the label missing one alone, implying that borrowing a commonly-used way from contrastive multi-class learning to define them will incur a lot of false negative instances unfavorable for learning.
Region attention and graph embedding network for occlusion objective class-based micro-expression recognition
More specifically, the backbone network aims at extracting feature representations from different facial regions, RI module computing an adaptive weight from the region itself based on attention mechanism with respect to the unobstructedness and importance for suppressing the influence of occlusion, and RR module exploiting the progressive interactions among these regions by performing graph convolutions.
Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition
It aims to obtain salient and discriminative features for specific expressions and also predict expression by fusing the expression-specific features.
Micro Expression Recognition
Micro-Expression Recognition
+1
Objective Class-based Micro-Expression Recognition through Simultaneous Action Unit Detection and Feature Aggregation
Specifically, we propose two new strategies in our AU detection module for more effective AU feature learning: the attention mechanism and the balanced detection loss function.
Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation
By introduces a improved transformer, elements in speech sequences can interact directly, which enables DPTNet can model for the speech sequences with direct context-awareness.
Ranked #15 on
Speech Separation
on WSJ0-2mix
Speech Separation
Audio and Speech Processing
Sound
Joint Pose and Expression Modeling for Facial Expression Recognition
First, the encoder-decoder structure of the generator can learn a generative and discriminative identity representation for face images.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
