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Found 30 papers, 9 papers with code
Tuning-Free Adaptive Style Incorporation for Structure-Consistent Text-Driven Style Transfer
In this work, we propose a novel solution to the text-driven style transfer task, namely, Adaptive Style Incorporation~(ASI), to achieve fine-grained feature-level style incorporation.
Active Generation for Image Classification
Recently, the growing capabilities of deep generative models have underscored their potential in enhancing image classification accuracy.
MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder
Within the domain of medical analysis, extensive research has explored the potential of mutual learning between Masked Autoencoders(MAEs) and multimodal data.
Uncertainty-driven and Adversarial Calibration Learning for Epicardial Adipose Tissue Segmentation
we propose a novel feature latent space multilevel supervision network (SPDNet) with uncertainty-driven and adversarial calibration learning to enhance segmentation for more accurate EAT volume estimation.
MULTI: Multimodal Understanding Leaderboard with Text and Images
Rapid progress in multimodal large language models (MLLMs) highlights the need to introduce challenging yet realistic benchmarks to the academic community, while existing benchmarks primarily focus on understanding simple natural images and short context.
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt
Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible.
Certified Minimax Unlearning with Generalization Rates and Deletion Capacity
In addition, our rates of generalization and deletion capacity match the state-of-the-art rates derived previously for standard statistical learning models.
ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach
However, despite their promising efficiency, almost all existing machine unlearning methods handle unlearning requests independently from inference requests, which unfortunately introduces a new security issue of inference service obsolescence and a privacy vulnerability of undesirable exposure for machine unlearning in MLaaS.
Real3D-AD: A Dataset of Point Cloud Anomaly Detection
High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing.
Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries
However, this task is very challenging due to the small sizes of surgical instrument tips, and significant variance of surgical scenes across different procedures.
EasyNet: An Easy Network for 3D Industrial Anomaly Detection
3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM).
Design of the Reverse Logistics System for Medical Waste Recycling Part II: Route Optimization with Case Study under COVID-19 Pandemic
To tackle the emerging challenges, this study designs a reverse logistics system architecture with three modules, i. e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module.
What makes a good data augmentation for few-shot unsupervised image anomaly detection?
Data augmentation is a promising technique for unsupervised anomaly detection in industrial applications, where the availability of positive samples is often limited due to factors such as commercial competition and sample collection difficulties.
Geometry-based spherical JND modeling for 360$^\circ$ display
Second, we extend our 2D-JND model to SJND by jointly exploiting latitude projection and field of view during 360$^\circ$ display.
Design of the Reverse Logistics System for Medical Waste Recycling Part I: System Architecture and Disposal Site Selection Algorithm
To tackle the challenges, this study proposes a reverse logistics system architecture with three modules, i. e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module.
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
We realize that the lack of a uniform IM benchmark is hindering the development and usage of IAD methods in real-world applications.
Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore
Besides, we provide a novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and can improve the performance of anomaly detection.
Deep Industrial Image Anomaly Detection: A Survey
In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets.
Label Distribution Learning for Generalizable Multi-source Person Re-identification
Besides, for the label distribution of each class, we further revise it to give more and equal attention to the other domains that the class does not belong to, which can effectively reduce the domain gap across different domains and obtain the domain-invariant feature.
Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive Framework
A significance of our work lies in that it shows the potential of unsupervised domain generalization for person ReID and sets a strong baseline for the further research on this topic.
Egocentric Human Trajectory Forecasting with a Wearable Camera and Multi-Modal Fusion
To this end, a novel egocentric human trajectory forecasting dataset was constructed, containing real trajectories of people navigating in crowded spaces wearing a camera, as well as extracted rich contextual data.
AskMe: Joint Individual-level and Community-level Behavior Interaction for Question Recommendation
On the one hand, we model the rich correlations between the users' diverse behaviors (e. g., answer, follow, vote) to obtain the individual-level behavior interaction.
DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction
To address these issues, we propose DeepExpress - a deep-learning based express delivery sequence prediction model, which extends the classic seq2seq framework to learning complex coupling between sequence and features.
TL-SDD: A Transfer Learning-Based Method for Surface Defect Detection with Few Samples
To solve the imbalanced distribution problem, in this paper we propose TL-SDD: a novel Transfer Learning-based method for Surface Defect Detection.
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization
XGBoost is one of the most widely used machine learning models in the industry due to its superior learning accuracy and efficiency.
Combine Convolution with Recurrent Networks for Text Classification
In this paper, we propose a novel method to keep the strengths of the two networks to a great extent.
End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables
We present a convolutional-recurrent neural network architecture with long short-term memory for real-time processing and classification of digital sensor data.
Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks
However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods.
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition
We further show that applying deep residual learning can boost the convergence speed of our novel deep recurret convolutional networks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Composing Music with Grammar Argumented Neural Networks and Note-Level Encoding
Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for artificial intelligence research.
