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Found 13 papers, 5 papers with code
Diffusion-Augmented Depth Prediction with Sparse Annotations
We leverage the structural characteristics of diffusion model to enforce depth structures of depth models in a plug-and-play manner.
Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells
Learning-based multi-view stereo (MVS) methods deal with predicting accurate depth maps to achieve an accurate and complete 3D representation.
Neural Video Depth Stabilizer
Video depth estimation aims to infer temporally consistent depth.
Ranked #16 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
Application of attention-based Siamese composite neural network in medical image recognition
Aiming at the problem of few-shot samples, a Siamese neural network suitable for classification model is proposed.
Matching Is Not Enough: A Two-Stage Framework for Category-Agnostic Pose Estimation
To calibrate the inaccurate matching results, we introduce a two-stage framework, where matched keypoints from the first stage are viewed as similarity-aware position proposals.
Ranked #3 on
2D Pose Estimation
on MP-100
Adaptive De-noising of Photoacoustic Signal and Image based on Modified Kalman Filter
As a burgeoning medical imaging method based on hybrid fusion of light and ultrasound, photoacoustic imaging (PAI) has demonstrated high potential in various biomedical applications recently, especially in revealing the functional and molecular information to improve diagnostic accuracy.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
Accelerated and Deep Expectation Maximization for One-Bit MIMO-OFDM Detection
In this study we analyze the convergence rate of EM for a class of approximate maximum-likelihood OMOD formulations, or, in a broader sense, a class of problems involving regression from quantized data.
Rethinking the Misalignment Problem in Dense Object Detection
On the basis of SALT and SDR loss, we propose SALT-Net, which explicitly exploits task-aligned point-set features for accurate detection results.
L3C-Stereo: Lossless Compression for Stereo Images
To tackle this, we propose L3C-Stereo, a multi-scale lossless compression model consisting of two main modules: the warping module and the probability estimation module.
Objects as Extreme Points
We also propose a new metric to measure the similarity between two groups of extreme points, namely, Extreme Intersection over Union (EIoU), and incorporate this EIoU as a new regression loss.
A novel multimodal approach for hybrid brain-computer interface
It has been already shown in literature that the hybrid of EEG and NIRS has better results than their respective individual signals.
Human-Computer Interaction Signal Processing
H-OWAN: Multi-distorted Image Restoration with Tensor 1x1 Convolution
It is a challenging task to restore images from their variants with combined distortions.
