Search Results for author:
Found 107 papers, 37 papers with code
Instance-hiding Schemes for Private Distributed Learning
The new ideas in the current paper are: (a) new variants of mixup with negative as well as positive coefficients, and extend the sample-wise mixup to be pixel-wise.
Subnetwork-to-go: Elastic Neural Network with Dynamic Training and Customizable Inference
Deploying neural networks to different devices or platforms is in general challenging, especially when the model size is large or model complexity is high.
Detection and Mitigation of Position Spoofing Attacks on Cooperative UAV Swarm Formations
Detecting spoofing attacks on the positions of unmanned aerial vehicles (UAVs) within a swarm is challenging.
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach
The attacker then adversarially regenerates the graph structural correlations while maximizing the FL training loss, and subsequently generates malicious local models using the adversarial graph structure and the training data features of the benign ones.
PanBench: Towards High-Resolution and High-Performance Pansharpening
Pansharpening, a pivotal task in remote sensing, involves integrating low-resolution multispectral images with high-resolution panchromatic images to synthesize an image that is both high-resolution and retains multispectral information.
Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model
These priors are subsequently utilized by RGformer to guide the decomposition of image features into their respective reflectance and illumination components.
Rebuild City Buildings from Off-Nadir Aerial Images with Offset-Building Model (OBM)
Accurate measurement of the offset from roof-to-footprint in very-high-resolution remote sensing imagery is crucial for urban information extraction tasks.
Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation
Finally, we propose an effective alignment method that explores diverse generation strategies, which can reasonably reduce the misalignment rate under our attack.
RTFS-Net: Recurrent time-frequency modelling for efficient audio-visual speech separation
We model and capture the time and frequency dimensions of the audio independently using a multi-layered RNN along each dimension.
Gastro-Intestinal Tract Segmentation Using an Explainable 3D Unet
This paper proposes a deep learning pipeline that incorporates XAI to address the challenges of organ segmentation.
High-Fidelity Lake Extraction via Two-Stage Prompt Enhancement: Establishing a Novel Baseline and Benchmark
The extraction of lakes from remote sensing images is a complex challenge due to the varied lake shapes and data noise.
SCANet: A Self- and Cross-Attention Network for Audio-Visual Speech Separation
The integration of different modalities, such as audio and visual information, plays a crucial role in human perception of the surrounding environment.
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Cinematic Demixing Track
This paper summarizes the cinematic demixing (CDX) track of the Sound Demixing Challenge 2023 (SDX'23).
LEFormer: A Hybrid CNN-Transformer Architecture for Accurate Lake Extraction from Remote Sensing Imagery
LEFormer contains four main modules: CNN encoder, Transformer encoder, cross-encoder fusion, and lightweight decoder.
DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images
Optical satellite images are a critical data source; however, cloud cover often compromises their quality, hindering image applications and analysis.
Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects
On the prey side, we propose an adversarial training framework, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect.
Consistency Regularization for Generalizable Source-free Domain Adaptation
In this paper, we propose a consistency regularization framework to develop a more generalizable SFDA method, which simultaneously boosts model performance on both target training and testing datasets.
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
How do software citation formats evolve over time? A longitudinal analysis of R programming language packages
However, software is hardly consistently cited: one software entity can be cited as different objects, and the citations can change over time.
HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance
Specifically, we extract features from an HQ image and explicitly insert the features, which are expected to encode HQ cues, into the enhancement network to guide the LQ enhancement with the variational normalization module.
Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean Teacher
This paper aims to address these two issues by proposing the Class-Balanced Mean Teacher (CBMT) model.
Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities
In recent years, ubiquitous semantic Metaverse has been studied to revolutionize immersive cyber-virtual experiences for augmented reality (AR) and virtual reality (VR) users, which leverages advanced semantic understanding and representation to enable seamless, context-aware interactions within mixed-reality environments.
PCG-based Static Underground Garage Scenario Generation
The key to crossing these levels lies in training the autonomous driving model.
Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model
We propose Audio-Visual Lightweight ITerative model (AVLIT), an effective and lightweight neural network that uses Progressive Learning (PL) to perform audio-visual speech separation in noisy environments.
A Neural State-Space Model Approach to Efficient Speech Separation
In this work, we introduce S4M, a new efficient speech separation framework based on neural state-space models (SSM).
PruMUX: Augmenting Data Multiplexing with Model Compression
As language models increase in size by the day, methods for efficient inference are critical to leveraging their capabilities for various applications.
Privacy Implications of Retrieval-Based Language Models
Crucially, we find that $k$NN-LMs are more susceptible to leaking private information from their private datastore than parametric models.
Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping
It remains a challenging task since (1) it is hard to distinguish concealed objects from the background due to the intrinsic similarity and (2) the sparsely-annotated training data only provide weak supervision for model learning.
Exploring Compositional Visual Generation with Latent Classifier Guidance
We show that this paradigm based on latent classifier guidance is agnostic to pre-trained generative models, and present competitive results for both image generation and sequential manipulation of real and synthetic images.
Towards Realizing the Value of Labeled Target Samples: a Two-Stage Approach for Semi-Supervised Domain Adaptation
We further propose a consistency learning based mean teacher model to effectively adapt the learned UDA model using labeled and unlabeled target samples.
Semi-supervised Domain Adaptation
Unsupervised Domain Adaptation
PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-performance Cloud Removal from Multi-temporal Satellite Imagery
Satellite imagery analysis plays a pivotal role in remote sensing; however, information loss due to cloud cover significantly impedes its application.
Conditional Image-to-Video Generation with Latent Flow Diffusion Models
In this paper, we propose an approach for cI2V using novel latent flow diffusion models (LFDM) that synthesize an optical flow sequence in the latent space based on the given condition to warp the given image.
Learning Trustworthy Model from Noisy Labels based on Rough Set for Surface Defect Detection
In the surface defect detection, there are some suspicious regions that cannot be uniquely classified as abnormal or normal.
Adversarial Alignment for Source Free Object Detection
Source-free object detection (SFOD) aims to transfer a detector pre-trained on a label-rich source domain to an unlabeled target domain without seeing source data.
Camouflaged Object Detection With Feature Decomposition and Edge Reconstruction
COD is a challenging task due to the intrinsic similarity of camouflaged objects with the background, as well as their ambiguous boundaries.
Degradation-Resistant Unfolding Network for Heterogeneous Image Fusion
Heterogeneous image fusion (HIF) techniques aim to enhance image quality by merging complementary information from images captured by different sensors.
Personalized Semantics Excitation for Federated Image Classification
Federated learning casts a light on the collaboration of distributed local clients with privacy protected to attain a more generic global model.
Few-Shot Video Classification via Representation Fusion and Promotion Learning
This operation maximizes the contribution of discriminative frames to further capture the similarity of support and query samples from the same category.
Source-Free Video Domain Adaptation With Spatial-Temporal-Historical Consistency Learning
Source-free domain adaptation (SFDA) is an emerging research topic that studies how to adapt a pretrained source model using unlabeled target data.
Source-Free Domain Adaptation
Unsupervised Domain Adaptation
An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits
Then, inspired by the large number of connections between cortical regions and the thalamus, the model fuses the auditory and visual information in a thalamic subnetwork through top-down connections.
Ranked #1 on
Speech Separation
on VoxCeleb2
SSDNeRF: Semantic Soft Decomposition of Neural Radiance Fields
Neural Radiance Fields (NeRFs) encode the radiance in a scene parameterized by the scene's plenoptic function.
Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.
Deep Spectro-temporal Artifacts for Detecting Synthesized Speech
The Audio Deep Synthesis Detection (ADD) Challenge has been held to detect generated human-like speech.
An efficient encoder-decoder architecture with top-down attention for speech separation
In addition, a large-size version of TDANet obtained SOTA results on three datasets, with MACs still only 10\% of Sepformer and the CPU inference time only 24\% of Sepformer.
Ranked #2 on
Speech Separation
on WHAM!
A Review on Method Entities in the Academic Literature: Extraction, Evaluation, and Application
With the advancement of sciences, many scientific methods are being proposed, modified, and used in academic literature.
A Multi-scale Video Denoising Algorithm for Raw Image
Video denoising for raw image has always been the difficulty of camera image processing.
Designs, Motion Mechanism, Motion Coordination, and Communication of Bionic Robot Fishes: A Survey
Various methods are needed to narrow the gap in swimming performance between robot fishes and fish.
Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications
The design of MAFENN framework and algorithm are dedicated to enhance the learning capability of the feedfoward DL networks or their variations with the simple data feedback.
Recovering Private Text in Federated Learning of Language Models
For the first time, we show the feasibility of recovering text from large batch sizes of up to 128 sentences.
Fast Few-shot Debugging for NLU Test Suites
We study few-shot debugging of transformer based natural language understanding models, using recently popularized test suites to not just diagnose but correct a problem.
Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems with Limited Feedback
The downlink channel covariance matrix (CCM) acquisition is the key step for the practical performance of massive multiple-input and multiple-output (MIMO) systems, including beamforming, channel tracking, and user scheduling.
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis
Based on the identified latent directions of attributes, we propose Compositional Attribute Adjustment to adjust the latent code, resulting in better compositionality of image synthesis.
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT
Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT).
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
Gradient inversion attack (or input recovery from gradient) is an emerging threat to the security and privacy preservation of Federated learning, whereby malicious eavesdroppers or participants in the protocol can recover (partially) the clients' private data.
DRL-Clusters: Buffer Management with Clustering based Deep Reinforcement Learning
Buffer cache has been widely implemented in database systems to reduce disk I/Os.
Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game
The deep policy gradient method has demonstrated promising results in many large-scale games, where the agent learns purely from its own experience.
Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detection
For the second issue, we reconsider how to improve detection efficiency with excellent performance, and then propose our lightweight encoder-decoder architecture termed CarNet.
EMA: Auditing Data Removal from Trained Models
In this paper, we propose a new method called Ensembled Membership Auditing (EMA) for auditing data removal to overcome these limitations.
Plot2Spectra: an Automatic Spectra Extraction Tool
In scientific literature, XANES/Raman data are usually plotted in line graphs which is a visually appropriate way to represent the information when the end-user is a human reader.
Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function
Numerous detection problems in computer vision, including road crack detection, suffer from exceedingly foreground-background imbalance.
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network
They also fail to sense the entire space of the input, which is critical for high-quality MR image SR. To address those problems, we propose squeeze and excitation reasoning attention networks (SERAN) for accurate MR image SR. We propose to squeeze attention from global spatial information of the input and obtain global descriptors.
Ranked #2 on
Image Super-Resolution
on IXI
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
In addition, we use a novel agent network named Population Invariant agent with Transformer (PIT) to realize the coordination transfer in more varieties of scenarios.
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit Assignment
In this paper, we propose a cooperative MARL method with sequential credit assignment (SeCA) that deduces each agent's contribution to the team's success one by one to learn better cooperation.
ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation
This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled samples and a few labeled samples in the target domain, with the help of labeled samples from a source domain.
RPCL: A Framework for Improving Cross-Domain Detection with Auxiliary Tasks
Contrasted with prior work, this paper provides a complementary solution to align domains by learning the same auxiliary tasks in both domains simultaneously.
L2E: Learning to Exploit Your Opponent
In this work, we propose a novel Learning to Exploit (L2E) framework for implicit opponent modeling.
VINS: Visual Search for Mobile User Interface Design
By utilizing this dataset, we propose an object-detection based image retrieval framework that models the UI context and hierarchical structure.
Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details
With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography.
Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References
This paper proposes a neural network for multi-level low-light image enhancement, which is user-friendly to meet various requirements by selecting different images as brightness reference.
Temperature Regret Matching for Imperfect-Information Games
Regret matching (RM) plays a crucial role in CFR and its variants to approach Nash equilibrium.
Video Matting via Consistency-Regularized Graph Neural Networks
In this paper, we propose to enhance the temporal coherence by Consistency-Regularized Graph Neural Networks (CRGNN) with the aid of a synthesized video matting dataset.
Neighbor Class Consistency on Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data in a target domain with labeled data from source domain available.
OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research
Owning to the unremitting efforts by a few institutes, significant progress has recently been made in designing superhuman AIs in No-limit Texas Hold'em (NLTH), the primary testbed for large-scale imperfect-information game research.
DSAM: A Distance Shrinking with Angular Marginalizing Loss for High Performance Vehicle Re-identificatio
To obtain a high-performance vehicle ReID model, we present a novel Distance Shrinking with Angular Marginalizing (DSAM) loss function to perform hybrid learning in both the Original Feature Space (OFS) and the Feature Angular Space (FAS) using the local verification and the global identification information.
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
Beyond the Deep Metric Learning: Enhance the Cross-Modal Matching with Adversarial Discriminative Domain Regularization
In this paper, we address this limitation with an efficient learning objective that considers the discriminative feature distributions between the visual objects and sentence words.
MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery
To address the issue that deep neural networks (DNNs) are vulnerable to model inversion attacks, we design an objective function, which adjusts the separability of the hidden data representations, as a way to control the trade-off between data utility and vulnerability to inversion attacks.
Pushing The Limit of Type I Codebook For FDD Massive MIMO Beamforming: A Channel Covariance Reconstruction Approach
There is a fundamental trade-off between the channel representation resolution of codebooks and the overheads of feedback communications in the fifth generation new radio (5G NR) frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems.
TextHide: Tackling Data Privacy in Language Understanding Tasks
In addition, TextHide fits well with the popular framework of fine-tuning pre-trained language models (e. g., BERT) for any sentence or sentence-pair task.
InstaHide: Instance-hiding Schemes for Private Distributed Learning
This paper introduces InstaHide, a simple encryption of training images, which can be plugged into existing distributed deep learning pipelines.
Light Field View Synthesis via Aperture Disparity and Warping Confidence Map
This paper presents a learning-based approach to synthesize the view from an arbitrary camera position given a sparse set of images.
Adversarial Feature Hallucination Networks for Few-Shot Learning
The recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data.
Cross-Domain Document Object Detection: Benchmark Suite and Method
We establish a benchmark suite consisting of different types of PDF document datasets that can be utilized for cross-domain DOD model training and evaluation.
Privacy-preserving Learning via Deep Net Pruning
This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility.
Learning Relaxed Belady for Content Distribution Network Caching
This paper presents a new approach for caching in CDNs that uses machine learning to approximate the Belady MIN algorithm.
Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective
We instead reformulate ZSL as a conditioned visual classification problem, i. e., classifying visual features based on the classifiers learned from the semantic descriptions.
Visual Semantic Reasoning for Image-Text Matching
It outperforms the current best method by 6. 8% relatively for image retrieval and 4. 8% relatively for caption retrieval on MS-COCO (Recall@1 using 1K test set).
Ranked #8 on
Image Retrieval
on Flickr30K 1K test
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection
A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e. g., patrolling velocity) for preventing battery drainage and data queue overflow of the sensing devices, while up-to-date knowledge on battery level and data queue of the devices is not available at the UAV.
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes
However, capturing the short-term effects of drugs and therapeutic interventions on patient physiological state remains challenging.
Authenticated Key-Value Stores with Hardware Enclaves
To circumvent the limited enclave memory (128 MB with the latest Intel CPUs), we propose to place the memory buffer of the eLSM store outside the enclave and protect the buffer using a new authenticated data structure by digesting individual LSM-tree levels.
Cryptography and Security Databases Distributed, Parallel, and Cluster Computing Data Structures and Algorithms
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
Face Anti-spoofing gains increased attentions recently in both academic and industrial fields.
Residual Non-local Attention Networks for Image Restoration
To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts.
PZnet: Efficient 3D ConvNet Inference on Manycore CPUs
Convolutional nets have been shown to achieve state-of-the-art accuracy in many biomedical image analysis tasks.
Fast 3D Line Segment Detection From Unorganized Point Cloud
This paper presents a very simple but efficient algorithm for 3D line segment detection from large scale unorganized point cloud.
Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream.
Support Neighbor Loss for Person Re-Identification
To ensure scalability and separability, a softmax-like function is formulated to push apart the positive and negative support sets.
Payoff Control in the Iterated Prisoner's Dilemma
Repeated game has long been the touchstone model for agents' long-run relationships.
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
To solve these problems, we propose the very deep residual channel attention networks (RCAN).
Ranked #14 on
Image Super-Resolution
on BSD100 - 4x upscaling
Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation
First, a novel cost-sensitive multi-task loss function is designed to learn transferable aging features by training on the source population.
Deep Ordinal Hashing with Spatial Attention
However, most existing deep hashing methods directly learn the hash functions by encoding the global semantic information, while ignoring the local spatial information of images.
Large Linear Multi-output Gaussian Process Learning
Gaussian processes (GPs), or distributions over arbitrary functions in a continuous domain, can be generalized to the multi-output case: a linear model of coregionalization (LMC) is one approach.
Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction
In the scenario of real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab tests is essential to enable successful medical interventions and improve patient outcomes.
Quantum cluster approach to the topological invariants in correlated Chern insulators
We detect the topological properties of Chern insulators with strong Coulomb interactions by use of cluster perturbation theory and variational cluster approach.
Strongly Correlated Electrons
From rules to runs: A dynamic epistemic take on imperfect information games
We argue that the problem lies in the mix-up of two interpretations of the extensive form game structures: game rules or game runs which do not always coincide.
First-Take-All: Temporal Order-Preserving Hashing for 3D Action Videos
With the prevalence of the commodity depth cameras, the new paradigm of user interfaces based on 3D motion capturing and recognition have dramatically changed the way of interactions between human and computers.
Line-Based Multi-Label Energy Optimization for Fisheye Image Rectification and Calibration
Fisheye image rectification and estimation of intrinsic parameters for real scenes have been addressed in the literature by using line information on the distorted images.
Rank Subspace Learning for Compact Hash Codes
In this work, we propose a novel hash learning framework that encodes feature's rank orders instead of numeric values in a number of optimal low-dimensional ranking subspaces.
