Search Results for author:
Found 33 papers, 10 papers with code
SIDME: Self-supervised Image Demoiréing via Masked Encoder-Decoder Reconstruction
Moir\'e patterns, resulting from aliasing between object light signals and camera sampling frequencies, often degrade image quality during capture.
Pseudo-Autoregressive Neural Codec Language Models for Efficient Zero-Shot Text-to-Speech Synthesis
Building on PAR, we propose PALLE, a two-stage TTS system that leverages PAR for initial generation followed by NAR refinement.
CoGen: 3D Consistent Video Generation via Adaptive Conditioning for Autonomous Driving
Recent progress in driving video generation has shown significant potential for enhancing self-driving systems by providing scalable and controllable training data.
BAT: Learning Event-based Optical Flow with Bidirectional Adaptive Temporal Correlation
Event cameras deliver visual information characterized by a high dynamic range and high temporal resolution, offering significant advantages in estimating optical flow for complex lighting conditions and fast-moving objects.
Improved VMD Based Remote Heartbeat Estimation Utilizing 60GHz mmWave Radar
This study introduces an improved VMD based signal decomposition methodology for non-contact heartbeat estimation using millimeterwave (mmWave) radar.
FELLE: Autoregressive Speech Synthesis with Token-Wise Coarse-to-Fine Flow Matching
To advance continuous-valued token modeling and temporal-coherence enforcement, we propose FELLE, an autoregressive model that integrates language modeling with token-wise flow matching.
Brain-Inspired Exploration of Functional Networks and Key Neurons in Large Language Models
We use methods similar to those in the field of functional neuroimaging analysis to locate and identify functional networks in LLM.
CMamba: Learned Image Compression with State Space Models
In this paper, we propose a hybrid Convolution and State Space Models (SSMs) based image compression framework, termed \textit{CMamba}, to achieve superior rate-distortion performance with low computational complexity.
Interleaved Speech-Text Language Models are Simple Streaming Text to Speech Synthesizers
This paper introduces Interleaved Speech-Text Language Model (IST-LM) for streaming zero-shot Text-to-Speech (TTS).
Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research
Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity.
LinBridge: A Learnable Framework for Interpreting Nonlinear Neural Encoding Models
LinBridge posits that the nonlinear mapping between ANN representations and neural responses can be factorized into a linear inherent component that approximates the complex nonlinear relationship, and a mapping bias that captures sample-selective nonlinearity.
Brain-like Functional Organization within Large Language Models
In this study, we bridge this gap by directly coupling sub-groups of artificial neurons with functional brain networks (FBNs), the foundational organizational structure of the human brain.
Diverse Sign Language Translation
In this work, we introduce a Diverse Sign Language Translation (DivSLT) task, aiming to generate diverse yet accurate translations for sign language videos.
DiVE: DiT-based Video Generation with Enhanced Control
Generating high-fidelity, temporally consistent videos in autonomous driving scenarios faces a significant challenge, e. g. problematic maneuvers in corner cases.
DreamCar: Leveraging Car-specific Prior for in-the-wild 3D Car Reconstruction
With this dataset, we make the generative model more robust to cars.
3DRealCar: An In-the-wild RGB-D Car Dataset with 360-degree Views
(1) \textbf{High-Volume}: 2, 500 cars are meticulously scanned by 3D scanners, obtaining car images and point clouds with real-world dimensions; (2) \textbf{High-Quality}: Each car is captured in an average of 200 dense, high-resolution 360-degree RGB-D views, enabling high-fidelity 3D reconstruction; (3) \textbf{High-Diversity}: The dataset contains various cars from over 100 brands, collected under three distinct lighting conditions, including reflective, standard, and dark.
UA-Track: Uncertainty-Aware End-to-End 3D Multi-Object Tracking
Secondly, we propose an Uncertainty-guided Query Denoising strategy to further enhance the training process.
Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generation
Instead of randomly generating new data, we further design a sampling policy to let Delphi generate new data that are similar to those failure cases to improve the sample efficiency.
MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
However, this process may lead to inaccurate annotations, such as ignoring non-majority or non-candidate labels.
TOD3Cap: Towards 3D Dense Captioning in Outdoor Scenes
However, the exploration of 3D dense captioning in outdoor scenes is hindered by two major challenges: 1) the domain gap between indoor and outdoor scenes, such as dynamics and sparse visual inputs, makes it difficult to directly adapt existing indoor methods; 2) the lack of data with comprehensive box-caption pair annotations specifically tailored for outdoor scenes.
Multimodal Fusion with Pre-Trained Model Features in Affective Behaviour Analysis In-the-wild
Multimodal fusion is a significant method for most multimodal tasks.
Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark for Deception Reasoning
Deception detection has attracted increasing attention due to its importance in real-world scenarios.
BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving
Furthermore, there lack of well-formed retrieval datasets for effective evaluation.
Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting
Recent methods extend NeRF by incorporating tracked vehicle poses to animate vehicles, enabling photo-realistic view synthesis of dynamic urban street scenes.
SVFAP: Self-supervised Video Facial Affect Perceiver
Video-based facial affect analysis has recently attracted increasing attention owing to its critical role in human-computer interaction.
Ranked #6 on
Dynamic Facial Expression Recognition
on FERV39k
Dynamic Facial Expression Recognition
Emotion Recognition
+2
OpenSight: A Simple Open-Vocabulary Framework for LiDAR-Based Object Detection
OpenSight utilizes 2D-3D geometric priors for the initial discernment and localization of generic objects, followed by a more specific semantic interpretation of the detected objects.
GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition
To bridge this gap, we present the quantitative evaluation results of GPT-4V on 21 benchmark datasets covering 6 tasks: visual sentiment analysis, tweet sentiment analysis, micro-expression recognition, facial emotion recognition, dynamic facial emotion recognition, and multimodal emotion recognition.
MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition
Considering comprehensiveness, we partition speech knowledge into Textual-related Emotional Content (TEC) and Speech-related Emotional Content (SEC), capturing cues from both semantic and acoustic perspectives, and we design a new architecture search space to fully leverage them.
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia.
Fully Automated End-to-End Fake Audio Detection
The existing fake audio detection systems often rely on expert experience to design the acoustic features or manually design the hyperparameters of the network structure.
Two-Aspect Information Fusion Model For ABAW4 Multi-task Challenge
In this paper, we propose the solution to the Multi-Task Learning (MTL) Challenge of the 4th Affective Behavior Analysis in-the-wild (ABAW) competition.
Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection
There are two critical sensors for 3D perception in autonomous driving, the camera and the LiDAR.
EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition
Speech emotion recognition (SER) is an important research topic in human-computer interaction.
