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Found 18 papers, 12 papers with code
Modeling Compositionality with Dependency Graph for Dialogue Generation
Because of the compositionality of natural language, syntactic structure which contains the information about the relationship between words is a key factor for semantic understanding.
Towards Zero-shot Human-Object Interaction Detection via Vision-Language Integration
Human-object interaction (HOI) detection aims to locate human-object pairs and identify their interaction categories in images.
FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model
However, compared with financial institutions, it is not easy for ordinary investors to mine factors and analyze news.
Ranked #1 on
Stock Market Prediction
on Astock
PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features
Three-dimensional point cloud anomaly detection that aims to detect anomaly data points from a training set serves as the foundation for a variety of applications, including industrial inspection and autonomous driving.
SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations
Large language models (LLMs) have been widely applied in various fields due to their excellent capability for memorizing knowledge and chain of thought (CoT).
BianQue: Balancing the Questioning and Suggestion Ability of Health LLMs with Multi-turn Health Conversations Polished by ChatGPT
Large language models (LLMs) have performed well in providing general and extensive health suggestions in single-turn conversations, exemplified by systems such as ChatGPT, ChatGLM, ChatDoctor, DoctorGLM, and etc.
CorrTalk: Correlation Between Hierarchical Speech and Facial Activity Variances for 3D Animation
In this study, we propose a novel framework, CorrTalk, which effectively establishes the temporal correlation between hierarchical speech features and facial activities of different intensities across distinct regions.
LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch
Therefore the pruning strategy can gradually prune the network and automatically determine the appropriate pruning rates for each layer.
Vesper: A Compact and Effective Pretrained Model for Speech Emotion Recognition
Although PTMs shed new light on artificial general intelligence, they are constructed with general tasks in mind, and thus, their efficacy for specific tasks can be further improved.
DWFormer: Dynamic Window transFormer for Speech Emotion Recognition
Self-attention mechanism is applied within windows for capturing temporal important information locally in a fine-grained way.
SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processing
Paralinguistic speech processing is important in addressing many issues, such as sentiment and neurocognitive disorder analyses.
CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI
Finally, we provide baseline systems for these tasks and consider the function of speakers' personalities and emotions on conversation.
Ranked #1 on
Emotion Recognition in Conversation
on CPED
Compact Model Training by Low-Rank Projection with Energy Transfer
In this paper, we devise a new training method, low-rank projection with energy transfer (LRPET), that trains low-rank compressed networks from scratch and achieves competitive performance.
Weight Evolution: Improving Deep Neural Networks Training through Evolving Inferior Weight Values
Comprehensive experiments show that WE outperforms the other reactivation methods and plug-in training methods with typical convolutional neural networks, especially lightweight networks.
LSSED: a large-scale dataset and benchmark for speech emotion recognition
Speech emotion recognition is a vital contributor to the next generation of human-computer interaction (HCI).
Ranked #1 on
Speech Emotion Recognition
on LSSED
Listwise View Ranking for Image Cropping
However, the performance of ranking-based methods is often poor and this is mainly due to two reasons: 1) image cropping is a listwise ranking task rather than pairwise comparison; 2) the rescaling caused by pooling layer and the deformation in view generation damage the performance of composition learning.
BIT: Biologically Inspired Tracker
Visual tracking is challenging due to image variations caused by various factors, such as object deformation, scale change, illumination change and occlusion.
Manifold Regularized Slow Feature Analysis for Dynamic Texture Recognition
However, complex temporal variations require high-level semantic representations to fully achieve temporal slowness, and thus it is impractical to learn a high-level representation from dynamic textures directly by SFA.
