Search Results for author:
Found 10 papers, 9 papers with code
GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular Video
3D human pose lifting is one of the promising research directions toward the task where both estimated pose and ground truth pose data are used for training.
Ranked #1 on
3D Human Pose Estimation
on HumanEva-I
A Better Choice: Entire-space Datasets for Aspect Sentiment Triplet Extraction
Aspect sentiment triplet extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences.
Modeling User Repeat Consumption Behavior for Online Novel Recommendation
In this paper, we concentrate on recommending online novels to new users of an online novel reading platform, whose first visits to the platform occurred in the last seven days.
MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D Videos
Upon aggregating the results of multiple modalities, our method is found to outperform state-of-the-art approaches on six evaluation protocols of the five datasets; thus, the proposed MMNet can effectively capture mutually complementary features in different RGB-D video modalities and provide more discriminative features for HAR.
Ranked #1 on
Action Recognition In Videos
on PKU-MMD
(using extra training data)
Training Entire-Space Models for Target-oriented Opinion Words Extraction
Moreover, the performance of these models on the first type of instance cannot reflect their performance on entire space.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Aspect-Sentiment-Multiple-Opinion Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term (aspect), sentiment and opinion term (opinion) triplets from sentences and can tell a complete story, i. e., the discussed aspect, the sentiment toward the aspect, and the cause of the sentiment.
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition
Data augmentation has recently achieved considerable performance improvement for deep learning models: increased accuracy, stability, and reduced over-fitting.
A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task
Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences and tries to provide a complete solution for aspect-based sentiment analysis (ABSA).
Ranked #1 on
Aspect-Sentiment-Opinion Triplet Extraction
on Res14
Aspect-Based Sentiment Analysis
Aspect-Sentiment-Opinion Triplet Extraction
+2
Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis
Given a sentence and the aspect categories mentioned in the sentence, AC-MIMLLN first predicts the sentiments of the instances, then finds the key instances for the aspect categories, finally obtains the sentiments of the sentence toward the aspect categories by aggregating the key instance sentiments.
Sentence Constituent-Aware Aspect-Category Sentiment Analysis with Graph Attention Networks
Aspect category sentiment analysis (ACSA) aims to predict the sentiment polarities of the aspect categories discussed in sentences.
Aspect Category Detection
Aspect Category Sentiment Analysis
+3
