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Found 33 papers, 10 papers with code
Large Language Models as Automated Aligners for benchmarking Vision-Language Models
In this work, we address the limitations via Auto-Bench, which delves into exploring LLMs as proficient aligners, measuring the alignment between VLMs and human intelligence and value through automatic data curation and assessment.
NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion
To this end, we develop a normal-distance head that outputs pixel-level surface normal and distance.
MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model
Because the depth ground-truth is unavailable in the training phase, we develop a pseudo ground-truth diffusion process to assist the diffusion in MonoDiffusion.
IEBins: Iterative Elastic Bins for Monocular Depth Estimation
To alleviate the possible error accumulation during the iterative process, we utilize a novel elastic target bin to replace the original target bin, the width of which is adjusted elastically based on the depth uncertainty.
Ranked #13 on
Monocular Depth Estimation
on KITTI Eigen split
NDDepth: Normal-Distance Assisted Monocular Depth Estimation
Meanwhile, the normal and distance are regularized by a developed plane-aware consistency constraint.
Ranked #13 on
Monocular Depth Estimation
on KITTI Eigen split
Vision-Based Traffic Accident Detection and Anticipation: A Survey
We present the first survey on Vision-TAD in the deep learning era and the first-ever survey for Vision-TAA.
Unlimited Knowledge Distillation for Action Recognition in the Dark
To address this issue, we propose an unlimited knowledge distillation (UKD) in this paper.
Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering
Online unsupervised video object segmentation (UVOS) uses the previous frames as its input to automatically separate the primary object(s) from a streaming video without using any further manual annotation.
ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation
Image keypoints and descriptors play a crucial role in many visual measurement tasks.
URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation
Specifically, we use the depth estimates from the Transformer branch and the CNN branch as pseudo labels to teach each other.
Ranked #13 on
Monocular Depth Estimation
on KITTI Eigen split
Dual-Scale Single Image Dehazing Via Neural Augmentation
Model-based single image dehazing algorithms restore haze-free images with sharp edges and rich details for real-world hazy images at the expense of low PSNR and SSIM values for synthetic hazy images.
SMUDLP: Self-Teaching Multi-Frame Unsupervised Endoscopic Depth Estimation with Learnable Patchmatch
Unsupervised monocular trained depth estimation models make use of adjacent frames as a supervisory signal during the training phase.
Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (UVOS) aims at automatically separating the primary foreground object(s) from the background in a video sequence.
Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study
While action recognition (AR) has gained large improvements with the introduction of large-scale video datasets and the development of deep neural networks, AR models robust to challenging environments in real-world scenarios are still under-explored.
Adaptive Weighted Guided Image Filtering for Depth Enhancement in Shape-From-Focus
In order to preserve the edges accurately in the refined depth map, the guidance image is constructed from the multi-focus image sequence, and the coefficient of the AWGIF is utilized to suppress the noise while enhancing the fine depth details.
Ranked #8 on
Spectral Reconstruction
on ARAD-1K
Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation
Deep stereo matching has made significant progress in recent years.
DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks
The proposed method, named DSRGAN, includes a well designed detail extraction algorithm to capture the most important high frequency information from images.
ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction
The reprojection loss is then proposed to directly optimize these sub-pixel keypoints, and the dispersity peak loss is presented for accurate keypoints regularization.
Model-Based Single Image Deep Dehazing
Model-based single image dehazing algorithms restore images with sharp edges and rich details at the expense of low PSNR values.
FAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical Flow
A FAMINet, which consists of a feature extraction network (F), an appearance network (A), a motion network (M), and an integration network (I), is proposed in this study to address the abovementioned problem.
Probabilistic Spatial Distribution Prior Based Attentional Keypoints Matching Network
Keypoints matching is a pivotal component for many image-relevant applications such as image stitching, visual simultaneous localization and mapping (SLAM), and so on.
Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot
For the former challenge, a spatially varying convolution (SVC) is designed to process the Bayer images carried with varying exposures.
Novel Intensity Mapping Functions: Weighted Histogram Averaging
It is challenging to align the brightness distribution of the images with different exposures due to possible color distortion and loss of details in the brightest and darkest regions of input images.
Hybrid Saturation Restoration for LDR Images of HDR Scenes
It is an ill-posed problem to restore the saturated regions of the LDR image.
Multi-Scale Single Image Dehazing Using Laplacian and Gaussian Pyramids
Ambiguity between object radiance and haze and noise amplification in sky regions are two inherent problems of model driven single image dehazing.
Automatic Vocabulary and Graph Verification for Accurate Loop Closure Detection
Localizing pre-visited places during long-term simultaneous localization and mapping, i. e. loop closure detection (LCD), is a crucial technique to correct accumulated inconsistencies.
Loop Closure Detection
Simultaneous Localization and Mapping
Deep Inertial Odometry with Accurate IMU Preintegration
Inertial Measurement Units (IMUs) are interceptive modalities that provide ego-motion measurements independent of the environmental factors.
Single Image Brightening via Multi-Scale Exposure Fusion with Hybrid Learning
In this paper, a single image brightening algorithm is introduced to brighten such an image.
S&CNet: Monocular Depth Completion for Autonomous Systems and 3D Reconstruction
In this paper, a lightweight yet efficient network (S\&CNet) is proposed to obtain a good trade-off between efficiency and accuracy for the dense depth completion.
Exposure Interpolation by Combining Model-driven and Data-driven Methods
A natural question raised here is "Is there any space for conventional methods on these problems?"
High Speed Tracking With A Fourier Domain Kernelized Correlation Filter
This is achieved due to the attribute of Convolution Theorem that the correlation in spatial domain corresponds to an element-wise product in the Fourier domain, resulting in that the l1-norm optimization problem could be decomposed into multiple sub-optimization spaces in the Fourier domain.
Gradient-Free Learning Based on the Kernel and the Range Space
In this article, we show that solving the system of linear equations by manipulating the kernel and the range space is equivalent to solving the problem of least squares error approximation.
Structure-preserving Guided Retinal Image Filtering and Its Application for Optic Disc Analysis
It often obscures the details in the retinal images and posts challenges in retinal image processing and analysing tasks.
