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Found 16 papers, 4 papers with code
Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement
Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work.
Simple Model Also Works: A Novel Emotion Recognition Network in Textual Conversation Based on Curriculum Learning Strategy
To simulate the way humans learn curriculum from easy to hard, we apply the idea of CL to the ERC task to progressively optimize the network parameters of ERNetCL.
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition
RUME is applied to extract conversation-level contextual emotional cues while pulling together the data distributions between modalities; ACME is utilized to perform multimodal interaction centered on textual modality; LESM is used to model emotion shift and capture related information, thereby guide the learning of the main task.
Ranked #3 on
Emotion Recognition in Conversation
on IEMOCAP
Emotion Recognition in Conversation
Multimodal Emotion Recognition
A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog
How to model the context in a conversation is a central aspect and a major challenge of ERC tasks.
TransERR: Translation-based Knowledge Graph Completion via Efficient Relation Rotation
This paper presents translation-based knowledge graph completion method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph completion models.
Ranked #14 on
Link Property Prediction
on ogbl-wikikg2
Towards Complex Real-World Safety Factory Inspection: A High-Quality Dataset for Safety Clothing and Helmet Detection
However, limited availability of high-quality datasets has hindered the development of deep learning methods for safety clothing and helmet detection.
SimHaze: game engine simulated data for real-world dehazing
Using a modern game engine, our approach renders crisp clean images and their precise depth maps, based on which high-quality hazy images can be synthesized for training dehazing models.
The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image Prior
Existing methods mostly handcraft or search for the architecture from a large design space, due to the lack of understanding on how the architectural choice corresponds to the image.
EmotionIC: Emotional Inertia and Contagion-Driven Dependency Modeling for Emotion Recognition in Conversation
The ablation studies confirm that our modules can effectively model emotional inertia and contagion.
Ranked #4 on
Emotion Recognition in Conversation
on EmoryNLP
InferEM: Inferring the Speaker's Intention for Empathetic Dialogue Generation
We separately encode the last utterance and fuse it with the entire dialogue through the multi-head attention based intention fusion module to capture the speaker's intention.
GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition
In multimodal ERC, GNNs are capable of extracting both long-distance contextual information and inter-modal interactive information.
Ranked #11 on
Emotion Recognition in Conversation
on IEMOCAP
Emotion Classification
Emotion Recognition in Conversation
+1
SciEv: Finding Scientific Evidence Papers for Scientific News
To our best knowledge, this is the first dataset of this kind.
JIZHI: A Fast and Cost-Effective Model-As-A-Service System for Web-Scale Online Inference at Baidu
In this work, we present JIZHI - a Model-as-a-Service system - that per second handles hundreds of millions of online inference requests to huge deep models with more than trillions of sparse parameters, for over twenty real-time recommendation services at Baidu, Inc.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
AUSN: Approximately Uniform Quantization by Adaptively Superimposing Non-uniform Distribution for Deep Neural Networks
Theoretical analysis~(see Appendix A) and accuracy evaluation on various DNN models of different tasks show the effectiveness and generalization of AUSN.
Deep Models for Engagement Assessment With Scarce Label Information
We compared performances of the new data representations with the original EEG features for engagement assessment.
