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Found 25 papers, 12 papers with code
Exploring Patient Data Requirements in Training Effective AI Models for MRI-based Breast Cancer Classification
Through large-scale experiments on various patient sizes in the training set, we show that medical institutions do not need a decade's worth of MRI images to train an AI model that performs competitively with the state-of-the-art, provided the model leverages foundation models.
360 in the Wild: Dataset for Depth Prediction and View Synthesis
The large abundance of perspective camera datasets facilitated the emergence of novel learning-based strategies for various tasks, such as camera localization, single image depth estimation, or view synthesis.
Standalone 16-bit Neural Network Training: Missing Study for Hardware-Limited Deep Learning Practitioners
Given the widespread availability of 16-bit across GPUs, these findings are especially valuable for machine learning practitioners with limited hardware resources to make informed decisions.
A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering
The Segment Anything Model (SAM), developed by Meta AI Research, represents a significant breakthrough in computer vision, offering a robust framework for image and video segmentation.
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era
Generative AI has made significant progress in recent years, with text-guided content generation being the most practical as it facilitates interaction between human instructions and AI-generated content (AIGC).
InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning
For scalable autonomous driving, a robust map-based localization system, independent of GPS, is fundamental.
Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selection
In this work, we propose a new domain adaptation framework for semantic segmentation with annotated points via active selection.
Keypoints Tracking via Transformer Networks
In this thesis, we propose a pioneering work on sparse keypoints tracking across images using transformer networks.
MC-Calib: A generic and robust calibration toolbox for multi-camera systems
In this paper, we present MC-Calib, a novel and robust toolbox dedicated to the calibration of complex synchronized multi-camera systems using an arbitrary number of fiducial marker-based patterns.
Deep Point Cloud Reconstruction
In particular, we further improve the performance of transformer by a newly proposed module called amplified positional encoding.
PointMixer: MLP-Mixer for Point Cloud Understanding
MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and transformer.
Ranked #24 on
Semantic Segmentation
on S3DIS Area5
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation
Experiments on KITTI, Cityscapes, and Waymo Open Dataset demonstrate the relevance of our approach and show that our method outperforms state-of-the-art algorithms for the tasks of self-supervised monocular depth estimation, object motion segmentation, monocular scene flow estimation, and visual odometry.
VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction
To reconstruct a 3D scene from a set of calibrated views, traditional multi-view stereo techniques rely on two distinct stages: local depth maps computation and global depth maps fusion.
Restoration of Video Frames from a Single Blurred Image with Motion Understanding
We formulate video restoration from a single blurred image as an inverse problem by setting clean image sequence and their respective motion as latent factors, and the blurred image as an observation.
Stereo Object Matching Network
This paper presents a stereo object matching method that exploits both 2D contextual information from images as well as 3D object-level information.
Motion-blurred Video Interpolation and Extrapolation
Abrupt motion of camera or objects in a scene result in a blurry video, and therefore recovering high quality video requires two types of enhancements: visual enhancement and temporal upsampling.
Optical Flow Estimation from a Single Motion-blurred Image
A flow estimator network is then used to estimate optical flow from the decoded features in a coarse-to-fine manner.
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
Finally, to decrease the intra-domain gap, we propose to employ a self-supervised adaptation technique from the easy to the hard split.
Ranked #2 on
Domain Adaptation
on Synscapes-to-Cityscapes
Camera Exposure Control for Robust Robot Vision with Noise-Aware Image Quality Assessment
In this paper, we propose a noise-aware exposure control algorithm for robust robot vision.
DeepCalib: a deep learning approach for automatic intrinsic calibration of wide field-of-view cameras
Calibration of wide field-of-view cameras is a fundamental step for numerous visual media production applications, such as 3D reconstruction, image undistortion, augmented reality and camera motion estimation.
Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution
Keypoint detection usually results in a large number of keypoints which are mostly clustered, redundant, and noisy.
Intelligent Assistant for People with Low Vision Abilities
This paper proposes a wearable system for visually impaired people that can be utilized to obtain an extensive feedback about their surrounding environment.
Light-weight place recognition and loop detection using road markings
In this paper, we propose an efficient algorithm for robust place recognition and loop detection using camera information only.
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks
We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN).
Ranked #73 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
