Panoptic Segmentation Forecasting
Colin Graber,Grace Tsai,Michael Firman,Gabriel J. Brostow,Alexander G. Schwing +4 more
- 19 Jun 2021
- pp 2279-2288
TL;DR: Panoptic segmentation as mentioned in this paper decomposes a dynamic scene into individual "things" and background "stuff" and predicts the appearance of future image frames using a two-component model: one component learns the dynamic of the background stuff by anticipating odometry, the other one anticipates the dynamics of detected things.
read more
Abstract: Our goal is to forecast the near future given a set of recent observations. We think this ability to forecast, i.e., to anticipate, is integral for the success of autonomous agents which need not only passively analyze an observation but also must react to it in real-time. Importantly, accurate forecasting hinges upon the chosen scene decomposition. We think that superior forecasting can be achieved by decomposing a dynamic scene into individual ‘things’ and background ‘stuff’. Background ‘stuff’ largely moves because of camera motion, while foreground ‘things’ move because of both camera and individual object motion. Following this decomposition, we introduce panoptic segmentation forecasting. Panoptic segmentation forecasting opens up a middle-ground between existing extremes, which either forecast instance trajectories or predict the appearance of future image frames. To address this task we develop a two-component model: one component learns the dynamics of the background stuff by anticipating odometry, the other one anticipates the dynamics of detected things. We establish a leaderboard for this novel task, and validate a state-of-the-art model that outperforms available baselines.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art
TL;DR: The various types of medical images and segmentation techniques and the assessment criteria for segmentation outcomes in kidney tumor segmentation are discussed, highlighting their building blocks and various strategies.
40
A survey on deep learning-based panoptic segmentation
Xinye Li,Shuai Gao,Ding Chen +2 more
TL;DR: This article summarizes the basic ideas of the panoptic segmentation method based on deep learning and classifies the current image panoptIC segmentation into four categories: top-down, bottom-up methods, single-path methods and other methods.
33
A Comprehensive Review of Modern Object Segmentation Approaches
TL;DR: Many deep learning-based approaches have been developed for image-level object recognition and pixel-level scene understanding, with the latter requiring a much denser annotation of scenes with a large set of objects as discussed by the authors .
25
Forecasting Future Instance Segmentation with Learned Optical Flow and Warping
Andrea Ciamarra,Federico Becattini,Lorenzo Seidenari,Alberto Del Bimbo +3 more
- 15 Nov 2022
TL;DR: In this paper , the authors investigate the use of optical flow for predicting future semantic segmentation and propose a model that forecasts flow fields autoregressively. And such predictions are then used to guide the inference of a learned warping function that moves instance segmentations on to future frames.
Low-Latency LiDAR Semantic Segmentation
23 Oct 2022
TL;DR: Li et al. as mentioned in this paper proposed a real-time semantic segmentation method that combines SalsaNext and semantic forecasting, which predicts the results of future segmentation, and they quantitatively evaluate their method using the Semantic-KITTI dataset, which comprises point cloud data acquired from the LiDAR sensor.
2
References
•Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
- 01 Jan 2015
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
138.5K
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger,Philipp Fischer,Thomas Brox +2 more
- 05 Oct 2015
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
•Posted Content
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
82.5K
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
- 06 Sep 2014
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Fully convolutional networks for semantic segmentation
Jonathan Long,Evan Shelhamer,Trevor Darrell +2 more
- 07 Jun 2015
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.