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Found 30 papers, 16 papers with code
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM).
DPoser: Diffusion Model as Robust 3D Human Pose Prior
Our approach demonstrates considerable enhancements over common uniform scheduling used in image domains, boasting improvements of 5. 4%, 17. 2%, and 3. 8% across human mesh recovery, pose completion, and motion denoising, respectively.
PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interaction
To make up for the lack of dynamic HOI scenarios in this area, we introduce the BallPlay dataset that contains eight whole-body basketball skills.
ChatPose: Chatting about 3D Human Pose
Additionally, ChatPose empowers LLMs to apply their extensive world knowledge in reasoning about human poses, leading to two advanced tasks: speculative pose generation and reasoning about pose estimation.
Binarized 3D Whole-body Human Mesh Recovery
In this work, we propose a Binarized Dual Residual Network (BiDRN), a novel quantization method to estimate the 3D human body, face, and hands parameters efficiently.
HumanTOMATO: Text-aligned Whole-body Motion Generation
This work targets a novel text-driven whole-body motion generation task, which takes a given textual description as input and aims at generating high-quality, diverse, and coherent facial expressions, hand gestures, and body motions simultaneously.
Driving behavior-guided battery health monitoring for electric vehicles using machine learning
To address these issues, we proposed a feature-based machine learning pipeline for reliable battery health monitoring, enabled by evaluating the acquisition probability of features under real-world driving conditions.
Improving Machine Learning Robustness via Adversarial Training
Moreover, in the non-IID data case, the natural accuracy drops from 66. 23% to 57. 82%, and the robust accuracy decreases by 25% and 23. 4% in C&W and Projected Gradient Descent (PGD) attacks, compared to the IID data case, respectively.
Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling
Battery health assessment and recuperation play a crucial role in the utilization of second-life Li-ion batteries.
Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset
In this paper, we present Motion-X, a large-scale 3D expressive whole-body motion dataset.
Binarized Spectral Compressive Imaging
Finally, our BiSRNet is derived by using the proposed techniques to binarize the base model.
One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer
It is challenging to perform this task with a single network due to resolution issues, i. e., the face and hands are usually located in extremely small regions.
Ranked #3 on
3D Human Pose Estimation
on UBody
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement
When enhancing low-light images, many deep learning algorithms are based on the Retinex theory.
Ranked #1 on
Low-Light Image Enhancement
on SDSD-outdoor
Low-light Image Deblurring and Enhancement
Low-Light Image Enhancement
+3
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from a compressed measurement.
Ranked #1 on
Spectral Reconstruction
on Real HSI
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
On the other hand, we equip the sequence-to-sequence model with an unsupervised optical flow estimator to maximize its potential.
Ranked #2 on
Video Enhancement
on MFQE v2
PAS: A Position-Aware Similarity Measurement for Sequential Recommendation
The common item-based collaborative filtering framework becomes a typical recommendation method when equipped with a certain item-to-item similarity measurement.
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction
Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image to its hyperspectral image (HSI).
Ranked #1 on
Spectral Reconstruction
on ARAD-1K
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction
Many algorithms have been developed to solve the inverse problem of coded aperture snapshot spectral imaging (CASSI), i. e., recovering the 3D hyperspectral images (HSIs) from a 2D compressive measurement.
Ranked #2 on
Spectral Reconstruction
on Real HSI
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging
On the one hand, the proposed HR spatial-spectral attention module with its efficient feature fusion provides continuous and fine pixel-level features.
Ranked #5 on
Spectral Reconstruction
on Real HSI
Flow-Guided Sparse Transformer for Video Deblurring
Exploiting similar and sharper scene patches in spatio-temporal neighborhoods is critical for video deblurring.
Ranked #1 on
Deblurring
on DVD
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks
Many state-of-the-art ML models have outperformed humans in various tasks such as image classification.
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction
The HSI representations are highly similar and correlated across the spectral dimension.
Ranked #2 on
Spectral Reconstruction
on ARAD-1K
Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis
Advancing lithium-ion batteries (LIBs) in both design and usage is key to promoting electrification in the coming decades to mitigate human-caused climate change.
Mahalanobis distance-based robust approaches against false data injection attacks on dynamic power state estimation
Compared to existing approaches, our proposed approaches have three major differences and significant strengths: (1) they defend against the three FDI attacks on dynamic power state estimation rather than static power state estimation, (2) they give a robust estimator that can accurately extract a subset of attack-free sensors for power state estimation, and (3) they adopt the little-known Mahalanobis distance in the consistency check of power sensor measurements, which is different from the Euclidean distance used in all the existing studies on power state estimation.
Active Learning Under Malicious Mislabeling and Poisoning Attacks
To check the performance of the proposed method under an adversarial setting, i. e., malicious mislabeling and data poisoning attacks, we perform an extensive evaluation on the reduced CIFAR-10 dataset, which contains only two classes: airplane and frog.
Data-efficient Alignment of Multimodal Sequences by Aligning Gradient Updates and Internal Feature Distributions
The task of video and text sequence alignment is a prerequisite step toward joint understanding of movie videos and screenplays.
An Adversarial Attack Defending System for Securing In-Vehicle Networks
In a modern vehicle, there are over seventy Electronics Control Units (ECUs).
A comprehensive review on convolutional neural network in machine fault diagnosis
To fill in this gap, this work attempts to review and summarize the development of the Convolutional Network based Fault Diagnosis (CNFD) approaches comprehensively.
