Search Results for author:
Found 10 papers, 6 papers with code
SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow
Increasing the annotation efficiency of trajectory annotations from videos has the potential to enable the next generation of data-hungry tracking algorithms to thrive on large-scale datasets.
Better Call SAL: Towards Learning to Segment Anything in Lidar
We propose $\texttt{SAL}$ ($\texttt{S}$egment $\texttt{A}$nything in $\texttt{L}$idar) method consisting of a text-promptable zero-shot model for segmenting and classifying any object in Lidar, and a pseudo-labeling engine that facilitates model training without manual supervision.
NOVIS: A Case for End-to-End Near-Online Video Instance Segmentation
Until recently, the Video Instance Segmentation (VIS) community operated under the common belief that offline methods are generally superior to a frame by frame online processing.
Ranked #5 on
Video Instance Segmentation
on YouTube-VIS validation
(using extra training data)
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis
To highlight the importance of privacy issues and motivate future research, we motivate and introduce the Pedestrian Dataset De-Identification (PDI) task.
DeVIS: Making Deformable Transformers Work for Video Instance Segmentation
To reason about all VIS subtasks jointly over multiple frames, we present temporal multi-scale deformable attention with instance-aware object queries.
Ranked #15 on
Video Instance Segmentation
on YouTube-VIS 2021
TrackFormer: Multi-Object Tracking with Transformers
The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about track initialization, identity, and spatio-temporal trajectories.
Ranked #1 on
Multi-Object Tracking
on MOT17
(e2e-MOT metric)
Make One-Shot Video Object Segmentation Efficient Again
In the semi-supervised setting, the first mask of each object is provided at test time.
Ranked #43 on
Semi-Supervised Video Object Segmentation
on YouTube-VOS 2018
(using extra training data)
Tracking without bells and whistles
Therefore, we motivate our approach as a new tracking paradigm and point out promising future research directions.
Ranked #1 on
Online Multi-Object Tracking
on 2D MOT 2015
Lifting Layers: Analysis and Applications
A lifting layer increases the dimensionality of the input, naturally yields a linear spline when combined with a fully connected layer, and therefore closes the gap between low and high dimensional approximation problems.
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems
While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks.
