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Found 13 papers, 5 papers with code
InstructERC: Reforming Emotion Recognition in Conversation with a Retrieval Multi-task LLMs Framework
The field of emotion recognition of conversation (ERC) has been focusing on separating sentence feature encoding and context modeling, lacking exploration in generative paradigms based on unified designs.
Ranked #2 on
Emotion Recognition in Conversation
on MELD
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.
ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy
We utilize TE and SE to combine the strengths of previous methods in a simplistic manner to efficiently capture temporal and spatial contextual information in the conversation.
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 data distributions between modalities; ACME is utilized to perform multimodal interaction centered on textual modality; LESM is used to model emotion shift and capture emotion-shift information, thereby guiding the learning of the main task.
Ranked #8 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.
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.
EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation
Emotion Recognition in Conversation (ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies.
Ranked #5 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 #19 on
Emotion Recognition in Conversation
on IEMOCAP
Emotion Classification
Emotion Recognition in Conversation
+1
Tree-based Search Graph for Approximate Nearest Neighbor Search
Research by Fu et al. shows that the algorithms based on Monotonic Search Network (MSNET), such as NSG and NSSG, have achieved the state-of-the-art search performance in efficiency.
Multimodal Representations Learning Based on Mutual Information Maximization and Minimization and Identity Embedding for Multimodal Sentiment Analysis
Multimodal sentiment analysis (MSA) is a fundamental complex research problem due to the heterogeneity gap between different modalities and the ambiguity of human emotional expression.
Canted antiferromagnetic order and spin dynamics in the honeycomb-lattice Tb2Ir3Ga9
Despite the $J_{{\rm eff}}=1/2$ moments, the spin Hamiltonian is denominated by a large in-plane anisotropy $K_z \sim -1$ meV.
Strongly Correlated Electrons Materials Science
STA: Adversarial Attacks on Siamese Trackers
On this basis, we formulate the adversarial generation problem and propose an end-to-end pipeline to generate a perturbed texture map for the 3D object that causes the trackers to fail.
