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Found 13 papers, 8 papers with code
SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields
Neural radiance fields (NeRF) has attracted considerable attention for their exceptional ability in synthesizing novel views with high fidelity.
Synchronizing Vision and Language: Bidirectional Token-Masking AutoEncoder for Referring Image Segmentation
While these methods match language features with image features to effectively identify likely target objects, they often struggle to correctly understand contextual information in complex and ambiguous sentences and scenes.
Guided Slot Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence.
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation
In recent works, two approaches for UVOS have been discussed that can be divided into: appearance and appearance-motion-based methods, which have limitations respectively.
DP-NeRF: Deblurred Neural Radiance Field with Physical Scene Priors
The few studies that have investigated NeRF for blurred images have not considered geometric and appearance consistency in 3D space, which is one of the most important factors in 3D reconstruction.
Boundary-aware Camouflaged Object Detection via Deformable Point Sampling
The proposed DPS-Net utilizes a Deformable Point Sampling transformer (DPS transformer) that can effectively capture sparse local boundary information of significant object boundaries in COD using a deformable point sampling method.
Dual Prototype Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos.
Ranked #1 on
Unsupervised Video Object Segmentation
on FBMS test
Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition
Graph convolutional networks (GCNs) are the most commonly used methods for skeleton-based action recognition and have achieved remarkable performance.
Ranked #4 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Expanded Adaptive Scaling Normalization for End to End Image Compression
Then, we increase the receptive field to make the adaptive rescaling module consider the spatial correlation.
MKConv: Multidimensional Feature Representation for Point Cloud Analysis
In this paper, we present Multidimensional Kernel Convolution (MKConv), a novel convolution operator that learns to transform the point feature representation from a vector to a multidimensional matrix.
Ranked #13 on
3D Part Segmentation
on ShapeNet-Part
Robust Lane Detection via Expanded Self Attention
Modern deep learning methods achieve high performance in lane detection, but it is still difficult to accurately detect lanes in challenging situations such as congested roads and extreme lighting conditions.
Ranked #42 on
Lane Detection
on CULane
Regularization Strategy for Point Cloud via Rigidly Mixed Sample
Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks.
Ranked #3 on
3D Point Cloud Classification
on ModelNet40-C
False Positive Removal for 3D Vehicle Detection with Penetrated Point Classifier
Recently, researchers have been leveraging LiDAR point cloud for higher accuracy in 3D vehicle detection.
