Search Results for author:
Found 9 papers, 6 papers with code
Point-VOS: Pointing Up Video Object Segmentation
We propose a novel Point-VOS task with a spatio-temporally sparse point-wise annotation scheme that substantially reduces the annotation effort.
AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation
In an iterative process, the model assigns each data point to an object (or the background), while the user corrects errors in the resulting segmentation and feeds them back into the model.
DynaMITe: Dynamic Query Bootstrapping for Multi-object Interactive Segmentation Transformer
We introduce a more efficient approach, called DynaMITe, in which we represent user interactions as spatio-temporal queries to a Transformer decoder with a potential to segment multiple object instances in a single iteration.
4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation
Our voting-based tracklet generation method followed by geometric feature-based aggregation generates significantly improved panoptic LiDAR segmentation quality when compared to modeling the entire 4D volume using Gaussian probability distributions.
D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
We further show that D^2Conv3D out-performs trivial extensions of existing dilated and deformable convolutions to 3D.
D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
We further show that D2Conv3D out-performs trivial extensions of existing dilated and deformable convolutions to 3D.
Making a Case for 3D Convolutions for Object Segmentation in Videos
On the other hand, 3D convolutional networks have been successfully applied for video classification tasks, but have not been leveraged as effectively to problems involving dense per-pixel interpretation of videos compared to their 2D convolutional counterparts and lag behind the aforementioned networks in terms of performance.
Ranked #13 on
Unsupervised Video Object Segmentation
on DAVIS 2016 val
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos
In this paper, we propose a different approach that is well-suited to a variety of tasks involving instance segmentation in videos.
Ranked #5 on
Unsupervised Video Object Segmentation
on DAVIS 2017 (val)
(using extra training data)
Iteratively Trained Interactive Segmentation
Deep learning requires large amounts of training data to be effective.
