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Found 27 papers, 6 papers with code
Transferable Attack for Semantic Segmentation
We analysis performance of semantic segmentation models wrt.
Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learning
In this case, salient objects are typically non-camouflaged, and camouflaged objects are usually not salient.
A Revisit of the Normalized Eight-Point Algorithm and A Self-Supervised Deep Solution
The normalized eight-point algorithm has been widely viewed as the cornerstone in two-view geometry computation, where the seminal Hartley's normalization has greatly improved the performance of the direct linear transformation algorithm.
Masked Representation Learning for Domain Generalized Stereo Matching
Experimental results on multi-datasets show that: (1) our method can be easily plugged into the current various stereo matching models to improve generalization performance; (2) our method can reduce the significant volatility of generalization performance among different training epochs; (3) we find that the current methods prefer to choose the best results among different training epochs as generalization performance, but it is impossible to select the best performance by ground truth in practice.
Learning a Task-specific Descriptor for Robust Matching of 3D Point Clouds
In this paper, we propose to learn a robust task-specific feature descriptor to consistently describe the correct point correspondence under interference.
Context-Aware Video Reconstruction for Rolling Shutter Cameras
Then, a refinement scheme is proposed to guide the GS frame synthesis along with bilateral occlusion masks to produce high-fidelity GS video frames at arbitrary times.
A Representation Separation Perspective to Correspondences-free Unsupervised 3D Point Cloud Registration
Existing correspondences-free methods generally learn the holistic representation of the entire point cloud, which is fragile for partial and noisy point clouds.
VRNet: Learning the Rectified Virtual Corresponding Points for 3D Point Cloud Registration
3D point cloud registration is fragile to outliers, which are labeled as the points without corresponding points.
End-to-end Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registration
Even though considerable progress has been made in deep learning-based 3D point cloud processing, how to obtain accurate correspondences for robust registration remains a major challenge because existing hard assignment methods cannot deal with outliers naturally.
Dense Uncertainty Estimation
Deep neural networks can be roughly divided into deterministic neural networks and stochastic neural networks. The former is usually trained to achieve a mapping from input space to output space via maximum likelihood estimation for the weights, which leads to deterministic predictions during testing.
SUNet: Symmetric Undistortion Network for Rolling Shutter Correction
The vast majority of modern consumer-grade cameras employ a rolling shutter mechanism, leading to image distortions if the camera moves during image acquisition.
Pattern Ensembling for Spatial Trajectory Reconstruction
Digital sensing provides an unprecedented opportunity to assess and understand mobility.
Class Attention Network for Semantic Segmentation of Remote Sensing Images
In this paper, we proposed a novel class attention module and decomposition-fusion strategy to cope with imbalanced labels.
NLCA-Net v2 for Stereo Matching in ECCV'20 Robust Vision Challenge
The network architecture used in this RVC, called as NLCA-Net v2, is consists of four parts: feature extraction, cost volume construction, feature matching, and refinement, as shown in Fig.
Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods
Vision-based monocular human pose estimation, as one of the most fundamental and challenging problems in computer vision, aims to obtain posture of the human body from input images or video sequences.
Self-supervised Modal and View Invariant Feature Learning
By exploring the inherent multi-modality attributes of 3D objects, in this paper, we propose to jointly learn modal-invariant and view-invariant features from different modalities including image, point cloud, and mesh with heterogeneous networks for 3D data.
Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences
Specifically, 2D image features of rendered images from different views are extracted by a 2D convolutional neural network, and 3D point cloud features are extracted by a graph convolution neural network.
MSDC-Net: Multi-Scale Dense and Contextual Networks for Automated Disparity Map for Stereo Matching
The multi-scale residual 3D convolution module learns the different scale geometry context from the cost volume which aggregated by the multi-scale fusion 2D convolution module.
Multi-scale Cross-form Pyramid Network for Stereo Matching
The network consists of three modules: Multi-Scale 2D local feature extraction module, Cross-form spatial pyramid module and Multi-Scale 3D Feature Matching and Fusion module.
Deep Edge-Aware Saliency Detection
There has been profound progress in visual saliency thanks to the deep learning architectures, however, there still exist three major challenges that hinder the detection performance for scenes with complex compositions, multiple salient objects, and salient objects of diverse scales.
Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference
Extensive experiments on the NYU Depth V2 and KITTI datasets show the superiority of our method compared with current state-of-the-art methods.
Dense Non-rigid Structure-from-Motion Made Easy - A Spatial-Temporal Smoothness based Solution
Second, we propose to exploit the spatial smoothness by resorting to the Laplacian of the 3D non-rigid shape.
Integrated Deep and Shallow Networks for Salient Object Detection
Then the results from deep FCNN and RBD are concatenated to feed into a shallow network to map the concatenated feature maps to saliency maps.
Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inference
This paper proposes a new residual convolutional neural network (CNN) architecture for single image depth estimation.
Skeleton Boxes: Solving skeleton based action detection with a single deep convolutional neural network
However, due to the difficulty in representing the 3D skeleton video and the lack of training data, action detection from streaming 3D skeleton video still lags far behind its recognition counterpart and image based object detection.
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn
Especially on the largest and challenge NTU RGB+D, UTD-MHAD, and MSRC-12 dataset, our method outperforms other methods by a large margion, which proves the efficacy of the proposed method.
Ranked #84 on
Skeleton Based Action Recognition
on NTU RGB+D
Depth and Surface Normal Estimation From Monocular Images Using Regression on Deep Features and Hierarchical CRFs
Predicting the depth (or surface normal) of a scene from single monocular color images is a challenging task.
