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Found 41 papers, 16 papers with code
Precoder Design for User-Centric Network Massive MIMO with Matrix Manifold Optimization
In this paper, we investigate the precoder design for user-centric network (UCN) massive multiple-input multiple-output (mMIMO) downlink with matrix manifold optimization.
Image-to-Image Matching via Foundation Models: A New Perspective for Open-Vocabulary Semantic Segmentation
Open-vocabulary semantic segmentation (OVS) aims to segment images of arbitrary categories specified by class labels or captions.
Modeling Unified Semantic Discourse Structure for High-quality Headline Generation
To consolidate the efficacy of S3 graphs, we further devise a hierarchical structure pruning mechanism to dynamically screen the redundant and nonessential nodes within the graph.
Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions
Augmentation is an effective alternative to utilize the small amount of labeled protein data.
Perception of Misalignment States for Sky Survey Telescopes with the Digital Twin and the Deep Neural Networks
To address this, we need a method to obtain misalignment states, aiding in the reconstruction of accurate point spread functions for data processing methods or facilitating adjustments of optical components for improved image quality.
GENOME: GenerativE Neuro-symbOlic visual reasoning by growing and reusing ModulEs
If not, we initialize a new module needed by the task and specify the inputs and outputs of this new module.
Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning
While enabling large language models to implement function calling (known as APIs) can greatly enhance the performance of Large Language Models (LLMs), function calling is still a challenging task due to the complicated relations between different APIs, especially in a context-learning setting without fine-tuning.
SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions
Notably, SafetyBench also incorporates both Chinese and English data, facilitating the evaluation in both languages.
Kernel-Based Tests for Likelihood-Free Hypothesis Testing
Given $n$ observations from two balanced classes, consider the task of labeling an additional $m$ inputs that are known to all belong to \emph{one} of the two classes.
UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding
However, we find visual and textual fine-grained information, e. g., keywords in the sentence and objects in the image, can be fairly informative for semantics understanding.
TEC-Net: Vision Transformer Embrace Convolutional Neural Networks for Medical Image Segmentation
The hybrid architecture of convolution neural networks (CNN) and Transformer has been the most popular method for medical image segmentation.
CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation
The hybrid architecture of convolutional neural networks (CNNs) and Transformer are very popular for medical image segmentation.
SR-OOD: Out-of-Distribution Detection via Sample Repairing
Specifically, our framework consists of two components: a sample repairing module and a detection module.
Generative Adversarial Network
Out-of-Distribution Detection
+1
IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models
Specifically, IdealGPT utilizes an LLM to generate sub-questions, a VLM to provide corresponding sub-answers, and another LLM to reason to achieve the final answer.
Human labeling errors and their impact on ConvNets for satellite image scene classification
However, the distribution of human labeling errors on satellite images and their impact on ConvNets have not been investigated.
Fair-CDA: Continuous and Directional Augmentation for Group Fairness
By adjusting the perturbation strength in the direction of the paths, our proposed augmentation is controllable and auditable.
An Error-Guided Correction Model for Chinese Spelling Error Correction
By borrowing the powerful ability of BERT, we propose a novel zero-shot error detection method to do a preliminary detection, which guides our model to attend more on the probably wrong tokens in encoding and to avoid modifying the correct tokens in generating.
DualRel: Semi-Supervised Mitochondria Segmentation From a Prototype Perspective
Automatic mitochondria segmentation enjoys great popularity with the development of deep learning.
Rethinking the Correlation in Few-Shot Segmentation: A Buoys View
In this work, we rethink how to mitigate the false matches from the perspective of representative reference features (referred to as buoys), and propose a novel adaptive buoys correlation (ABC) network to rectify direct pairwise pixel-level correlation, including a buoys mining module and an adaptive correlation module.
Adaptive Template Transformer for Mitochondria Segmentation in Electron Microscopy Images
Mitochondria, as tiny structures within the cell, are of significant importance to study cell functions for biological and clinical analysis.
Camouflaged Instance Segmentation via Explicit De-Camouflaging
To address these challenges, we propose a novel De-camouflaging Network (DCNet) including a pixel-level camouflage decoupling module and an instance-level camouflage suppression module.
Alignment Before Aggregation: Trajectory Memory Retrieval Network for Video Object Segmentation
In this work, we reconcile the inherent tension of spatial and temporal information to retrieve memory frame information along the object trajectory, and propose a novel and coherent Trajectory Memory Retrieval Network (TMRN) to equip with the trajectory information, including a spatial alignment module and a temporal aggregation module.
Find Someone Who: Visual Commonsense Understanding in Human-Centric Grounding
We present a new commonsense task, Human-centric Commonsense Grounding, that tests the models' ability to ground individuals given the context descriptions about what happened before, and their mental/physical states or intentions.
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained Models
To strengthen the word boundary information, we mix the representations of the internal characters within a word.
Semi-Supervised Crowd Counting from Unlabeled Data
Our method achieved competitive performance in semi-supervised learning approaches on these crowd counting datasets.
PNet -- A Deep Learning Based Photometry and Astrometry Bayesian Framework
Time domain astronomy has emerged as a vibrant research field in recent years, focusing on celestial objects that exhibit variable magnitudes or positions.
Lesion-Aware Transformers for Diabetic Retinopathy Grading
First, to the best of our knowledge, this is the first work to formulate lesion discovery as a weakly supervised lesion localization problem via a transformer decoder.
Blind Diagnosis for Millimeter-wave Large-scale Antenna Systems
Millimeter-wave (mmWave) communication systems rely on large-scale antenna arrays to combat large path-loss at mmWave band.
Diagnosis of Intelligent Reflecting Surface in Millimeter-wave Communication Systems
In the second case where only partial CSI is available, we jointly exploit the sparsity of the millimeter-wave channel and the failure, and adopt compressed sparse and low-rank matrix recovery algorithm to decouple channel and failure.
MetaAugment: Sample-Aware Data Augmentation Policy Learning
Automated data augmentation has shown superior performance in image recognition.
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
To address that, we propose DecAug, a novel decomposed feature representation and semantic augmentation approach for OoD generalization.
EPI-based Oriented Relation Networks for Light Field Depth Estimation
Based on the observation that an oriented line and its neighboring pixels in an EPI share a similar linear structure, we propose an end-to-end fully convolutional network (FCN) to estimate the depth value of the intersection point on the horizontal and vertical EPIs.
Risk Variance Penalization
The key of the out-of-distribution (OOD) generalization is to generalize invariance from training domains to target domains.
Online Learning and Optimization for Revenue Management Problems with Add-on Discounts
We show that our learning algorithm can converge to the optimal algorithm that has access to the true demand functions, and we prove that the convergence rate is tight up to a certain logarithmic term.
Multi-view Point Cloud Registration with Adaptive Convergence Threshold and its Application on 3D Model Retrieval
In this paper, we propose a framework to reconstruct the 3D models by the multi-view point cloud registration algorithm with adaptive convergence threshold, and subsequently apply it to 3D model retrieval.
S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction
Recent studies have used deep residual convolutional neural networks (CNNs) for JPEG compression artifact reduction.
Ranked #4 on
JPEG Artifact Correction
on LIVE1 (Quality 20 Color)
JPEG Artifact Correction
Jpeg Compression Artifact Reduction
Study of sedimentation of non-cohesive particles via CFD-DEM simulations
This paper employs the coupled computational fluid dynamics and discrete element method (CFD-DEM) to investigate the sedimentation process of non-cohesive particles, including the hindered settling stage and the deposition stage.
Fluid Dynamics Computational Physics Geophysics
CFD-DEM Simulations of Current-Induced Dune Formation and Morphological Evolution
In this work, current-induced sediment transport problems in a wide range of regimes are simulated, including 'flat bed in motion', `small dune', `vortex dune' and suspended transport.
Fluid Dynamics
Diffusion-Based Coarse Graining in Hybrid Continuum-Discrete Solvers: Applications in CFD-DEM
Moreover, we demonstrate that the overhead computational costs incurred by the proposed coarse-graining procedure are a small portion of the total costs in typical CFD-DEM simulations as long as the number of particles per cell is reasonably large, although admittedly the computational overhead of the coarse graining often exceeds that of the CFD solver.
Computational Physics Fluid Dynamics
Diffusion-Based Coarse Graining in Hybrid Continuum-Discrete Solvers: Theoretical Formulation and A Priori Tests
The numerical tests demonstrated that the proposed coarse graining procedure based on solving diffusion equations is theoretically sound, easy to implement and parallelize in general CFD solvers, and has improved mesh-convergence characteristics compared with existing coarse graining methods.
Computational Physics
