Search Results for author:
Found 17 papers, 3 papers with code
PCMamba: Physics-Informed Cross-Modal State Space Model for Dual-Camera Compressive Hyperspectral Imaging
Specifically, we analyze the imaging process of hyperspectral thermal signals to enable the network to disentangle the three key physical properties-temperature, emissivity, and texture.
FRN: Fractal-Based Recursive Spectral Reconstruction Network
Generating hyperspectral images (HSIs) from RGB images through spectral reconstruction can significantly reduce the cost of HSI acquisition.
TeleEval-OS: Performance evaluations of large language models for operations scheduling
The rapid advancement of large language models (LLMs) has significantly propelled progress in artificial intelligence, demonstrating substantial application potential across multiple specialized domains.
Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables
To address these challenges, we propose Pan-LUT, a novel learnable look-up table (LUT) framework for pan-sharpening that strikes a balance between performance and computational efficiency for high-resolution remote sensing images.
Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors
As diffusion models are sensitive to noise, diffusion priors are introduced to achieve high-performance noise suppression.
Neural Modulation Alteration to Positive and Negative Emotions in Depressed Patients: Insights from fMRI Using Positive/Negative Emotion Atlas
This research enhances our understanding of emotion modulation in depression, with implications for diagnosis and treatment evaluation.
AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement
Existing low-light image enhancement (LIE) methods have achieved noteworthy success in solving synthetic distortions, yet they often fall short in practical applications.
Training-Free Large Model Priors for Multiple-in-One Image Restoration
To address this issue, we propose Large Model Driven Image Restoration framework (LMDIR), a novel multiple-in-one image restoration paradigm that leverages the generic priors from large multi-modal language models (MMLMs) and the pretrained diffusion models.
AMSA-UNet: An Asymmetric Multiple Scales U-net Based on Self-attention for Deblurring
In order to overcome the limitations of traditional convolutional methods in capturing the long-range dependencies of information, a self-attention mechanism is introduced into the decoder part of the backbone network, which significantly increases the model's receptive field, enabling it to pay more attention to semantic information of the image, thereby producing more accurate and visually pleasing deblurred images.
DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning
Drug combinations can cause adverse drug-drug interactions(DDIs).
End-to-End 3D Hand Pose Estimation from Stereo Cameras
This work proposes an end-to-end approach to estimate full 3D hand pose from stereo cameras.
YACLC: A Chinese Learner Corpus with Multidimensional Annotation
This resource is of great relevance for second language acquisition research, foreign-language teaching, and automatic grammatical error correction.
Reinforcement Learning Based Adaptive WalkingAssistance Control of a Lower Limb Exoskeleton
Lower limb exoskeleton for people suffering from unilateral weakness or even hemiparesis has attracted considerable interest in recent years.
Boundary Content Graph Neural Network for Temporal Action Proposal Generation
To address this issue, we propose a novel Boundary Content Graph Neural Network (BC-GNN) to model the insightful relations between the boundary and action content of temporal proposals by the graph neural networks.
Ranked #26 on
Temporal Action Localization
on ActivityNet-1.3
3D Hand Shape and Pose Estimation from a Single RGB Image
This work addresses a novel and challenging problem of estimating the full 3D hand shape and pose from a single RGB image.
A Multimodal Motion-Captured Corpus of Matched and Mismatched Extravert-Introvert Conversational Pairs
This paper presents a new corpus, the Personality Dyads Corpus, consisting of multimodal data for three conversations between three personality-matched, two-person dyads (a total of 9 separate dialogues).
