Search Results for author:
Found 26 papers, 5 papers with code
RepFace: Refining Closed-Set Noise with Progressive Label Correction for Face Recognition
However, face recognition performance is heavily affected by the label noise, especially closed-set noise.
CLIP-based Camera-Agnostic Feature Learning for Intra-camera Person Re-Identification
To address this, we propose a novel framework called CLIP-based Camera-Agnostic Feature Learning (CCAFL) for ICS ReID.
LCE: A Framework for Explainability of DNNs for Ultrasound Image Based on Concept Discovery
Explaining the decisions of Deep Neural Networks (DNNs) for medical images has become increasingly important.
HcNet: Image Modeling with Heat Conduction Equation
This finding inspired us to model images by the heat conduction equation, where the essential idea is to conceptualize image features as temperatures and model their information interaction as the diffusion of thermal energy.
Tri-VQA: Triangular Reasoning Medical Visual Question Answering for Multi-Attribute Analysis
However, existing Med-VQA methods based on joint embedding fail to explain whether their provided results are based on correct reasoning or coincidental answers, which undermines the credibility of VQA answers.
MDA: An Interpretable and Scalable Multi-Modal Fusion under Missing Modalities and Intrinsic Noise Conditions
MDA constructs linear relationships between modalities through continuous attention, due to its ability to adaptively allocate dynamic attention to different modalities, MDA can reduce attention to low-correlation data, missing modalities, or modalities with inherent noise, thereby maintaining SOTA performance across various tasks on multiple public datasets.
Generating Multi-Center Classifier via Conditional Gaussian Distribution
Specifically, we create a conditional Gaussian distribution for each class and then sample multiple sub-centers from that distribution to extend the linear classifier.
Prospective Role of Foundation Models in Advancing Autonomous Vehicles
With the development of artificial intelligence and breakthroughs in deep learning, large-scale Foundation Models (FMs), such as GPT, Sora, etc., have achieved remarkable results in many fields including natural language processing and computer vision.
Vision Big Bird: Random Sparsification for Full Attention
Inspired by one of the most successful transformers-based models for NLP: Big Bird, we propose a novel sparse attention mechanism for Vision Transformers (ViT).
Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation
Autonomous vehicle (AV) evaluation has been the subject of increased interest in recent years both in industry and in academia.
On Data-Driven Modeling and Control in Modern Power Grids Stability: Survey and Perspective
Modern power grids are fast evolving with the increasing volatile renewable generation, distributed energy resources (DERs) and time-varying operating conditions.
DQ-Whisper: Joint Distillation and Quantization for Efficient Multilingual Speech Recognition
Then, a quantization-aware distillation framework is introduced to integrate quantization with distillation.
RSIR Transformer: Hierarchical Vision Transformer using Random Sampling Windows and Important Region Windows
Recently, Transformers have shown promising performance in various vision tasks.
LongFNT: Long-form Speech Recognition with Factorized Neural Transducer
This motivates us to leverage the factorized neural transducer structure, containing a real language model, the vocabulary predictor.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
BoundaryFace: A mining framework with noise label self-correction for Face Recognition
Specifically, a closed-set noise label self-correction module is put forward, making this framework work well on datasets containing a lot of label noise.
SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data
In this paper, we propose a cross-modal Speech and Language Model (SpeechLM) to explicitly align speech and text pre-training with a pre-defined unified discrete representation.
Axially Expanded Windows for Local-Global Interaction in Vision Transformers
Recently, Transformers have shown promising performance in various vision tasks.
An Online Data-Driven Method for Microgrid Secondary Voltage and Frequency Control with Ensemble Koopman Modeling
Low inertia, nonlinearity and a high level of uncertainty (varying topologies and operating conditions) pose challenges to microgrid (MG) systemwide operation.
Self-Supervised Implicit Attention: Guided Attention by The Model Itself
To verify the effectiveness of SSIA, we performed a particular implementation (called an SSIA block) in convolutional neural network models and validated it on several image classification datasets.
Positional Label for Self-Supervised Vision Transformer
Positional encoding is important for vision transformer (ViT) to capture the spatial structure of the input image.
ReplaceBlock: An improved regularization method based on background information
In this way, ReplaceBlock can effectively simulate the feature map of the occluded image.
The Fixed Sub-Center: A Better Way to Capture Data Complexity
The F-SC specifically, first samples a class center Ui for each class from a uniform distribution, and then generates a normal distribution for each class, where the mean is equal to Ui.
A Two-Stage Data-Free Adversarial Patch Generation Framework
The proxy dataset selection stage calculates the proposed average patch saliency (APS) of each available dataset to select a high-APS proxy dataset that can guarantee patches' fooling abilities.
Achievable Rates of Opportunistic Cognitive Radio Systems Using Reconfigurable Antennas with Imperfect Sensing and Channel Estimation
We establish a lower bound on the achievable rates of SUtx-SUrx link, in the presence of spectrum sensing and channel estimation errors, and errors due to incorrect detection of the beam corresponding to PU's location and incorrect selection of the strongest beam for data transmission.
A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning
Traffic flow forecasting has been regarded as a key problem of intelligent transport systems.
Three-Stream Convolutional Networks for Video-based Person Re-Identification
This paper aims to develop a new architecture that can make full use of the feature maps of convolutional networks.
