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Found 42 papers, 21 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.
PsyDT: Using LLMs to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling
Experimental results indicate that our proposed PsyDT framework can synthesize multi-turn dialogues that closely resemble real-world counseling cases and demonstrate better performance compared to other baselines, thereby show that our framework can effectively construct the digital twin of psychological counselor with a specific counseling style.
Shared Attention-based Autoencoder with Hierarchical Fusion-based Graph Convolution Network for sEEG SOZ Identification
sATAE is trained by sEEG data across all patients, with attention blocks introduced to enhance the representation of interdependencies between feature elements.
SleepCoT: A Lightweight Personalized Sleep Health Model via Chain-of-Thought Distillation
We present a novel approach to personalized sleep health management using few-shot Chain-of-Thought (CoT) distillation, enabling small-scale language models (> 2B parameters) to rival the performance of large language models (LLMs) in specialized health domains.
Multi-Scale Temporal Transformer For Speech Emotion Recognition
Speech emotion recognition plays a crucial role in human-machine interaction systems.
Online Multi-level Contrastive Representation Distillation for Cross-Subject fNIRS Emotion Recognition
Utilizing functional near-infrared spectroscopy (fNIRS) signals for emotion recognition is a significant advancement in understanding human emotions.
AS-Speech: Adaptive Style For Speech Synthesis
In recent years, there has been significant progress in Text-to-Speech (TTS) synthesis technology, enabling the high-quality synthesis of voices in common scenarios.
Timeliness-Fidelity Tradeoff in 3D Scene Representations
We test our framework and the proposed approach with different well-known 3D scene representation methods.
VideoCoT: A Video Chain-of-Thought Dataset with Active Annotation Tool
Therefore, we try to explore the collection of CoT datasets in videos to lead to video OpenQA and improve the reasoning ability of MLLMs.
Disentangled Pre-training for Human-Object Interaction Detection
Therefore, we propose an efficient disentangled pre-training method for HOI detection (DP-HOI) to address this problem.
Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On
Second, we propose a novel diffusion-based method that predicts a precise inpainting mask based on the person and reference garment images, further enhancing the reliability of the try-on results.
Exploring 3D Human Pose Estimation and Forecasting from the Robot's Perspective: The HARPER Dataset
The scenario underlying HARPER includes 15 actions, of which 10 involve physical contact between the robot and users.
ViTGaze: Gaze Following with Interaction Features in Vision Transformers
Gaze following aims to interpret human-scene interactions by predicting the person's focal point of gaze.
Ranked #1 on
Gaze Target Estimation
on VideoAttentionTarget
(using extra training data)
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.
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.
Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction
By adding linear image domain error analysis, the noise is reduced after under-sampled and DFT processing, and the anti-interference ability of the algorithm is enhanced.
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.
Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model
Finally, the experimental results on real-world fetal brain MRI stacks demonstrate the state-of-the-art performance of our method.
Dynamic Shuffle: An Efficient Channel Mixture Method
To reduce the data-dependent redundancy, we devise a dynamic shuffle module to generate data-dependent permutation matrices for shuffling.
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.
Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressor
Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features.
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.
Superpoint Transformer for 3D Scene Instance Segmentation
The key step in this framework is a novel query decoder with transformers that can capture the instance information through the superpoint cross-attention mechanism and generate the superpoint masks of the instances.
Ranked #6 on
3D Instance Segmentation
on ScanNet(v2)
Context Sensing Attention Network for Video-based Person Re-identification
In this work, we propose a novel Context Sensing Attention Network (CSA-Net), which improves both the frame feature extraction and temporal aggregation steps.
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
We theoretically show that energy transfer eases the trend of gradient vanishing caused by projection.
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.
To Deconvolve, or Not to Deconvolve: Inferences of Neuronal Activities using Calcium Imaging Data
With the increasing popularity of calcium imaging data in neuroscience research, methods for analyzing calcium trace data are critical to address various questions.
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.
Geometry Processing of Conventionally Produced Mouse Brain Slice Images
In this paper we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space.
Deep Sampling Networks
With the powerful down-sampling process, the co-training DSN set a new state-of-the-art performance for image super-resolution.
FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks
Rectified linear unit (ReLU) is a widely used activation function for deep convolutional neural networks.
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.
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
However, the previous methods mainly restore images from one single area in the low resolution (LR) input, which limits the flexibility of models to infer various scales of details for high resolution (HR) output.
Multi-scale Convolutional Neural Networks for Crowd Counting
Crowd counting on static images is a challenging problem due to scale variations.
DehazeNet: An End-to-End System for Single Image Haze Removal
The key to achieve haze removal is to estimate a medium transmission map for an input hazy image.
Ranked #7 on
Image Dehazing
on RS-Haze
