Search Results for author:
Found 31 papers, 10 papers with code
A brief introduction to the Grey Machine Learning
A short discussion on the priority of this new framework to the existing grey models and LSSVM have also been discussed in this paper.
Crossbar-aware neural network pruning
Crossbar architecture based devices have been widely adopted in neural network accelerators by taking advantage of the high efficiency on vector-matrix multiplication (VMM) operations.
Batch Normalization Sampling
In this paper, we propose alleviating this problem through sampling only a small fraction of data for normalization at each iteration.
Fast and Incremental Loop Closure Detection Using Proximity Graphs
Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems.
Multi-resolution Graph Neural Network for Identifying Disease-specific Variations in Brain Connectivity
As there is significant interest in understanding the altered interactions between different brain regions that lead to neuro-disorders, it is important to develop data-driven methods that work with a population of graph data for traditional prediction tasks.
Exploiting Style and Attention in Real-World Super-Resolution
Real-world image super-resolution (SR) is a challenging image translation problem.
Free-Form Image Inpainting via Contrastive Attention Network
It is difficult for encoders to capture such powerful representations under this complex situation.
Almost elementariness and fiberwise amenability for étale groupoids
In this paper, we introduce two new approximation properties for \'etale groupoids, almost elementariness and (ubiquitous) fiberwise amenability, inspired by Matui's and Kerr's notions of almost finiteness.
Operator Algebras Dynamical Systems 22A22 (primary) 46L35, 51F30, 37A55, 37B05 (Secondary)
Unsupervised Contrastive Photo-to-Caricature Translation based on Auto-distortion
Considering the intuitive artifacts in the existing methods, we propose a contrastive style loss for style rendering to enforce the similarity between the style of rendered photo and the caricature, and simultaneously enhance its discrepancy to the photos.
Discovering key topics from short, real-world medical inquiries via natural language processing and unsupervised learning
Millions of unsolicited medical inquiries are received by pharmaceutical companies every year.
Adaptive Deconvolution-based stereo matching Net for Local Stereo Matching
In deep learning-based local stereo matching methods, larger image patches usually bring better stereo matching accuracy.
Separating Boundary Points via Structural Regularization for Very Compact Clusters
VCC takes advantage of distributions of local relationships of samples near the boundary of clusters, so that they can be properly separated and pulled to cluster centers to form compact clusters.
Contrastive Attention Network with Dense Field Estimation for Face Completion
This multi-scale architecture is beneficial for the decoder to utilize discriminative representations learned from encoders into images.
Compressing Models with Few Samples: Mimicking then Replacing
Hence, previous methods optimize the compressed model layer-by-layer and try to make every layer have the same outputs as the corresponding layer in the teacher model, which is cumbersome.
TL-GAN: Improving Traffic Light Recognition via Data Synthesis for Autonomous Driving
Traffic light recognition, as a critical component of the perception module of self-driving vehicles, plays a vital role in the intelligent transportation systems.
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm
In this work, we propose FedSSO, a server-side second-order optimization method for federated learning (FL).
High-dimensional Measurement Error Models for Lipschitz Loss
Our estimator is designed to minimize the $L_1$ norm among all estimators belonging to suitable feasible sets, without requiring any knowledge of the noise distribution.
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of Intelligence
We compare the prediction performance for different intelligence measures based on static FC, dynamic FC, and region level time series acquired from the Adolescent Brain Cognitive Development (ABCD) study involving close to 7000 individuals.
Disjoint Masking with Joint Distillation for Efficient Masked Image Modeling
Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency.
LEO: Generative Latent Image Animator for Human Video Synthesis
Our key idea is to represent motion as a sequence of flow maps in the generation process, which inherently isolate motion from appearance.
SRRM: Semantic Region Relation Model for Indoor Scene Recognition
Despite the remarkable success of convolutional neural networks in various computer vision tasks, recognizing indoor scenes still presents a significant challenge due to their complex composition.
Ranked #1 on
Scene Recognition
on MIT Indoor Scenes
(10-stage average accuracy metric)
Semantic-guided spatial relation and object co-occurrence modeling for indoor scene recognition
Exploring the semantic context in scene images is essential for indoor scene recognition.
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
Specifically, we utilize a multi-scale approach to generate video-related descriptions.
Semantic-embedded Similarity Prototype for Scene Recognition
Due to the high inter-class similarity caused by the complex composition and the co-existing objects across scenes, numerous studies have explored object semantic knowledge within scenes to improve scene recognition.
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
To this end, we propose LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model.
Ranked #4 on
Text-to-Video Generation
on EvalCrafter Text-to-Video (ECTV) Dataset
(using extra training data)
On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks
Federated learning (FL) is a widely employed distributed paradigm for collaboratively training machine learning models from multiple clients without sharing local data.
SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction
The goal is to generate high-quality long videos with smooth and creative transitions between scenes and varying lengths of shot-level videos.
Inter-object Discriminative Graph Modeling for Indoor Scene Recognition
Leveraging object information within scenes to enhance the distinguishability of feature representations has emerged as a key approach in this domain.
Latte: Latent Diffusion Transformer for Video Generation
We propose a novel Latent Diffusion Transformer, namely Latte, for video generation.
NPHardEval4V: A Dynamic Reasoning Benchmark of Multimodal Large Language Models
Understanding the reasoning capabilities of Multimodal Large Language Models (MLLMs) is an important area of research.
Large Language Models in Biomedical and Health Informatics: A Bibliometric Review
Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), enabling new ways to analyze data, treat patients, and conduct research.
