Search Results for author:
Found 37 papers, 17 papers with code
D$^3$: Scaling Up Deepfake Detection by Learning from Discrepancy
The boom of Generative AI brings opportunities entangled with risks and concerns.
Anomaly Detection Based on Isolation Mechanisms: A Survey
Anomaly detection is a longstanding and active research area that has many applications in domains such as finance, security, and manufacturing.
DETER: Detecting Edited Regions for Deterring Generative Manipulations
While face swapping and attribute editing are performed on similar face regions such as eyes and nose, the inpainting operation can be performed on random image regions, removing the spurious correlations of previous datasets.
Unseen Image Synthesis with Diffusion Models
While the current trend in the generative field is scaling up towards larger models and more training data for generalized domain representations, we go the opposite direction in this work by synthesizing unseen domain images without additional training.
Distribution-Based Trajectory Clustering
Independent of the distance measure employed, existing clustering algorithms have another challenge: either effectiveness issues or high time complexity.
Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation
In this work, we investigate the diffusion (physics) in diffusion (machine learning) properties and propose our Cyclic One-Way Diffusion (COW) method to control the direction of diffusion phenomenon given a pre-trained frozen diffusion model for versatile customization application scenarios, where the low-level pixel information from the conditioning needs to be preserved.
Ripple Knowledge Graph Convolutional Networks For Recommendation Systems
Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy.
Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds
We introduce the state-of-the-art deep learning Denoising Diffusion Probabilistic Model (DDPM) as a method to infer the volume or number density of giant molecular clouds (GMCs) from projected mass surface density maps.
Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation
Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations.
Detecting Change Intervals with Isolation Distributional Kernel
Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis.
Domain generalization Person Re-identification on Attention-aware multi-operation strategery
Domain generalization person re-identification (DG Re-ID) aims to directly deploy a model trained on the source domain to the unseen target domain with good generalization, which is a challenging problem and has practical value in a real-world deployment.
Robust Human Matting via Semantic Guidance
Unlike previous works, our framework is data efficient, which requires a small amount of matting ground-truth to learn to estimate high quality object mattes.
Vision+X: A Survey on Multimodal Learning in the Light of Data
We are perceiving and communicating with the world in a multisensory manner, where different information sources are sophisticatedly processed and interpreted by separate parts of the human brain to constitute a complex, yet harmonious and unified sensing system.
Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-modal Representation Consistency
By aligning the class tokens and spatial attention maps of paired NBI and WL images at different levels, the Transformer achieves the ability to keep both global and local representation consistency for the above two modalities.
Toward Unpaired Multi-modal Medical Image Segmentation via Learning Structured Semantic Consistency
Integrating multi-modal data to promote medical image analysis has recently gained great attention.
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods.
Discrete Contrastive Diffusion for Cross-Modal Music and Image Generation
Diffusion probabilistic models (DPMs) have become a popular approach to conditional generation, due to their promising results and support for cross-modal synthesis.
Supplementing Missing Visions via Dialog for Scene Graph Generations
Most current AI systems rely on the premise that the input visual data are sufficient to achieve competitive performance in various computer vision tasks.
Quantized GAN for Complex Music Generation from Dance Videos
We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal framework that generates complex musical samples conditioned on dance videos.
Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion Network for Action Recognition
To solve the problem, we propose a multi-modality feature fusion network to combine the modalities of the skeleton sequence and RGB frame instead of the RGB video, as the key information contained by the combination of skeleton sequence and RGB frame is close to that of the skeleton sequence and RGB video.
Breaking the curse of dimensionality with Isolation Kernel
The curse of dimensionality has been studied in different aspects.
Saying the Unseen: Video Descriptions via Dialog Agents
Current vision and language tasks usually take complete visual data (e. g., raw images or videos) as input, however, practical scenarios may often consist the situations where part of the visual information becomes inaccessible due to various reasons e. g., restricted view with fixed camera or intentional vision block for security concerns.
Real Time Video based Heart and Respiration Rate Monitoring
The proposed method is based on measuring fluctuations in the Hue, and can therefore extract both HR and RR from the video of a user's face.
Learning Audio-Visual Correlations from Variational Cross-Modal Generation
People can easily imagine the potential sound while seeing an event.
The Impact of Isolation Kernel on Agglomerative Hierarchical Clustering Algorithms
In this paper, we identify the root cause of this issue and show that the use of a data-dependent kernel (instead of distance or existing kernel) provides an effective means to address it.
Describing Unseen Videos via Multi-Modal Cooperative Dialog Agents
With the arising concerns for the AI systems provided with direct access to abundant sensitive information, researchers seek to develop more reliable AI with implicit information sources.
Hierarchical HMM for Eye Movement Classification
In this work, we tackle the problem of ternary eye movement classification, which aims to separate fixations, saccades and smooth pursuits from the raw eye positional data.
Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering
Based on the ELBOs, we propose a VAE-based Bayesian MF framework.
Point-Set Kernel Clustering
This paper introduces a new similarity measure called point-set kernel which computes the similarity between an object and a set of objects.
Nearest-Neighbour-Induced Isolation Similarity and its Impact on Density-Based Clustering
A new type of clusters called mass-connected clusters is formally defined.
Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel
This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel.
Strain engineering of epitaxial oxide heterostructures beyond substrate limitations
The limitation of commercially available single-crystal substrates and the lack of continuous strain tunability preclude the ability to take full advantage of strain engineering for further exploring novel properties and exhaustively studying fundamental physics in complex oxides.
Materials Science
A Technical Survey on Statistical Modelling and Design Methods for Crowdsourcing Quality Control
Our survey is the first to bridge the two branches by providing technical details on how they work together under frameworks that systematically unify crowdsourcing aspects modelled by both of them to determine the response quality.
Multiview Based 3D Scene Understanding On Partial Point Sets
Deep learning within the context of point clouds has gained much research interest in recent years mostly due to the promising results that have been achieved on a number of challenging benchmarks, such as 3D shape recognition and scene semantic segmentation.
Hierarchical clustering that takes advantage of both density-peak and density-connectivity
This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods to yield a density-based hierarchical clustering algorithm.
CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities
To match the implicit assumption, we propose to transform a given dataset such that the transformed clusters have approximately the same density while all regions of locally low density become globally low density -- homogenising cluster density while preserving the cluster structure of the dataset.
Distinguishing Question Subjectivity from Difficulty for Improved Crowdsourcing
Experiments show that our model(1) improves the performance of both quality control for crowd-sourced answers and next answer prediction for crowd-workers, and (2) can potentially provide coherent rankings of questions in terms of their difficulty and subjectivity, so that task providers can refine their designs of the crowdsourcing tasks, e. g. by removing highly subjective questions or inappropriately difficult questions.
