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CURRICULUM LEARNING FOR
RECURRENT VIDEO OBJECT SEGMENTATION
Co-directors: Xavier Giró Nieto and Carles Ventura Royo
Author: Maria Gonzàlez Calabuig
Introduction
Dataset
The model
Experiment sets
Techniques
Qualitative results
YouTube-VOS
Conclusions
CONTENTS
INTRODUCTION
INTRODUCTION
Curriculum Learning for Recurrent VOS - 4 of 144
Curriculum Learning:
Methodology inspired by the learning process of humans. The training data is presented in a
meaningful way, from simple to complex concepts.
Yoshua Bengio et al. “Curriculum Learning”, ICML. 2019.
INTRODUCTION
Curriculum Learning for Recurrent VOS - 5 of 144
Curriculum Learning:
Methodology inspired by the learning process of humans. The training data is presented in a
meaningful way, from simple to complex concepts.
4 curriculums
Yoshua Bengio et al. “Curriculum Learning”, ICML. 2019.
INTRODUCTION
Curriculum Learning for Recurrent VOS - 6 of 144
Curriculum Learning:
Methodology inspired by the learning process of humans. The training data is presented in a
meaningful way, from simple to complex concepts.
4 curriculums
THE DATASET
Yoshua Bengio et al. “Curriculum Learning”, ICML. 2019.
INTRODUCTION
Curriculum Learning for Recurrent VOS - 7 of 144
Curriculum Learning:
Methodology inspired by the learning process of humans. The training data is presented in a
meaningful way, from simple to complex concepts.
4 curriculums
THE DATASET THE MODEL
Yoshua Bengio et al. “Curriculum Learning”, ICML. 2019.
INTRODUCTION
Curriculum Learning for Recurrent VOS - 8 of 144
THE TASK
Semi-supervised or “one-shot” Video Object Segmentation
INTRODUCTION
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THE TASK
Semi-supervised or “one-shot” Video Object Segmentation
INTRODUCTION
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THE TASK
Semi-supervised or “one-shot” Video Object Segmentation
INTRODUCTION
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THE TASK
Estimated by the modelGiven to the model
Semi-supervised or “one-shot” Video Object Segmentation
DATASET
KITTI-MOTS
DATASET
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Andreas Geiger, Philip Lenz, and Raquel Urtasun. “Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite”, CVPR 2012.
KITTI-MOTS
DATASET
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Andreas Geiger, Philip Lenz, and Raquel Urtasun. “Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite”, CVPR 2012.
Its video sequences present challenges:
KITTI-MOTS
DATASET
Curriculum Learning for Recurrent VOS - 15 of 144
Its video sequences present challenges:
Andreas Geiger, Philip Lenz, and Raquel Urtasun. “Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite”, CVPR 2012.
KITTI-MOTS
DATASET
Curriculum Learning for Recurrent VOS - 16 of 144
Its video sequences present challenges:
Andreas Geiger, Philip Lenz, and Raquel Urtasun. “Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite”, CVPR 2012.
KITTI-MOTS
DATASET
Curriculum Learning for Recurrent VOS - 17 of 144
Its video sequences present challenges:
Andreas Geiger, Philip Lenz, and Raquel Urtasun. “Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite”, CVPR 2012.
THE MODEL
THE MODEL
End-to-End Recurrent Network for video object segmentation: RVOS
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Carles Ventura, Miriam Bellver, Andreu Girbau, Amaia Salvador, Ferran Marques and Xavier Giro-i-Nieto. “RVOS: End-to-End Recurrent Network
for Video Object Segmentation”, CVPR 2019.
THE MODEL
End-to-End Recurrent Network for video object segmentation: RVOS
Curriculum Learning for Recurrent VOS - 20 of 144
Athar, A., Mahadevan, S., Oˇsep, A., Leal-Taix´e, L., Leibe, B.: Stem-seg: Spatio-temporal embeddings for instance segmentation in videos., ECCV (2020)
EXPERIMENT SETS
SETS OF EXPERIMENTS
All techniques tested on two sets of experiments:
Resolution Batch Size Length clip
287x950 2 3
Resolution Batch Size Length clip
256x448 4 5
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METRICS
The results have been evaluated on the official metrics of the MOTS Challenge.
- sMOTSA has been defined as the reference metric:
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Paul Voigtlaender et al. “MOTS: Multi-Object Tracking and Segmentation”, CVPR 2019.
METRICS
The results have been evaluated on the official metrics of the MOTS Challenge.
- sMOTSA has been defined as the reference metric:
Curriculum Learning for Recurrent VOS - 25 of 144
Paul Voigtlaender et al. “MOTS: Multi-Object Tracking and Segmentation”, CVPR 2019.
EVALUATION METHOD
Proposal: Evaluation averaged per sequence
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Sequences
SCHEDULE
SAMPLING
SCHEDULE SAMPLING
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VOS requires information about the previous step.
SCHEDULE SAMPLING
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Train using the model’s outputs.
SCHEDULE SAMPLING
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Train using the ground-truth annotations.
SCHEDULE SAMPLING
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TEACHER FORCING
SCHEDULE SAMPLING
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TEACHER FORCING
Fast and efficient
SCHEDULE SAMPLING
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TEACHER FORCING
Fast and efficient Leads to exposure bias
SCHEDULE SAMPLING
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time
SCHEDULE SAMPLING
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time
Schedule Sampling
SCHEDULE SAMPLING
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Schedule Sampling
Linear
SCHEDULE SAMPLING
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Schedule Sampling
Linear Step
SCHEDULE SAMPLING
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Schedule Sampling
Linear
Forward
Step
Forward
SCHEDULE SAMPLING
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Schedule Sampling
Linear
Forward
Step
Forward
SCHEDULE SAMPLING
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Schedule Sampling
Linear
Forward
Step
Forward
SCHEDULE SAMPLING
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Schedule Sampling
Linear
Forward Inverse
Step
Forward Inverse
SCHEDULE SAMPLING
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Schedule Sampling
Linear
Forward Inverse
Step
Forward Inverse
SCHEDULE SAMPLING
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Schedule Sampling
Linear
Forward Inverse
Step
Forward Inverse
SCHEDULE SAMPLING
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Schedule Sampling
Linear
Forward Inverse
Step
Forward Inverse
SCHEDULE SAMPLING
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SCHEDULE SAMPLING
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RESULTS ON THE FORWARD STRATEGIES
SCHEDULE SAMPLING
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RESULTS ON THE INVERSE STRATEGIES
SCHEDULE SAMPLING
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OVERVIEW
SCHEDULE SAMPLING
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OVERVIEW
SCHEDULE SAMPLING
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OVERVIEW
SCHEDULE SAMPLING
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OVERVIEW
FRAME SKIPPING
FRAME SKIPPING
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KITTI-MOTS has slow-motion video sequences.
frame #1
FRAME SKIPPING
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KITTI-MOTS has slow-motion video sequences.
frame #1
frame #2
FRAME SKIPPING
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KITTI-MOTS has slow-motion video sequences.
frame #1
frame #2
frame #3
FRAME SKIPPING
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KITTI-MOTS has slow-motion video sequences.
frame #1
frame #2
frame #3
frame #4
FRAME SKIPPING
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KITTI-MOTS has slow-motion video sequences.
frame #1
frame #2
frame #3
frame #4
frame #5
FRAME SKIPPING
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KITTI-MOTS has slow-motion video sequences.
frame #1
frame #2
frame #3
frame #4
frame #5
frame #6
FRAME SKIPPING
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Ideally:
…
...
.
…
…
..
N
fram
es of the sequence
FRAME SKIPPING
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Ideally:
But we have limitations (e.g. memory constraints)
…
…
..
N
fram
es of the sequence
…
...
.
FRAME SKIPPING
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FRAME SKIPPING
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FRAME SKIPPING
Frame Skipping
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FRAME SKIPPING
Frame Skipping
From 0 to 9
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FRAME SKIPPING
Frame Skipping
From 0 to 9
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time
FRAME SKIPPING
Frame Skipping
From 0 to 9
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time
FRAME SKIPPING
Frame Skipping
From 0 to 9
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time
FRAME SKIPPING
Frame Skipping
From 0 to 9
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time
...
FRAME SKIPPING
Frame Skipping
From 0 to 9 From 1 to 5
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time
FRAME SKIPPING
Frame Skipping
From 0 to 9 From 1 to 5
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time
FRAME SKIPPING
Frame Skipping
From 0 to 9 From 1 to 5
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time
FRAME SKIPPING
Frame Skipping
From 0 to 9 From 1 to 5
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time
...
FRAME SKIPPING
Frame Skipping
From 0 to 9 From 1 to 5
All training All training
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FRAME SKIPPING
Frame Skipping
From 0 to 9 From 1 to 5
All training All training
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FRAME SKIPPING
Frame Skipping
From 0 to 9 From 1 to 5
All training All training
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FRAME SKIPPING
Frame Skipping
From 0 to 9 From 1 to 5
All training
First half
training
All training
First half
training
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FRAME SKIPPING
Frame Skipping
From 0 to 9 From 1 to 5
All training
First half
training
All training
First half
training
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FRAME SKIPPING
Frame Skipping
From 0 to 9 From 1 to 5
All training
First half
training
All training
First half
training
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FRAME SKIPPING
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RESULTS ON THE FRAME SKIPPING APPLIED DURING ALL TRAINING
FRAME SKIPPING
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RESULTS ON THE FRAME SKIPPING APPLIED ONLY WITH GROUND-TRUTH
FRAME SKIPPING
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OVERVIEW
FRAME SKIPPING
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OVERVIEW
TEMPORAL AND
SPATIAL
RECURRENCES
TEMPORAL AND SPATIAL RECURRENCES
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KITTI-MOTS is a crowded dataset:
TEMPORAL AND SPATIAL RECURRENCES
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time (frame sequence)
space(objectsequence)
TEMPORAL AND SPATIAL RECURRENCES
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time (frame sequence)
TEMPORAL RECURRENCE
TEMPORAL AND SPATIAL RECURRENCES
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space(objectsequence)
SPATIAL RECURRENCE
TEMPORAL AND SPATIAL RECURRENCES
Proposed curriculum:
Curriculum Learning for Recurrent VOS - 87 of 144
TEMPORAL AND SPATIAL RECURRENCES
Temporal and Spatial
Recurrence
Only temporal during
the first half of training
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TEMPORAL AND SPATIAL RECURRENCES
Temporal and Spatial
Recurrence
Spatio-temporal during
all training
Only temporal during
the first half of training
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TEMPORAL AND SPATIAL RECURRENCES
Temporal and Spatial
Recurrence
Spatio-temporal during
all training
Only temporal during
all training
Only temporal during
the first half of training
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TEMPORAL AND SPATIAL RECURRENCES
Temporal and Spatial
Recurrence
Spatio-temporal during
all training
Only temporal during
all training
Only temporal during
the first half of training
Only temporal during the
second half of training
Curriculum Learning for Recurrent VOS - 91 of 144
TEMPORAL AND SPATIAL RECURRENCES
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TEMPORAL AND SPATIAL RECURRENCES
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TEMPORAL AND SPATIAL RECURRENCES
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Ground-truth
TEMPORAL AND SPATIAL RECURRENCES
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Only Spatio-Temporal
Ground-truth
TEMPORAL AND SPATIAL RECURRENCES
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Only Spatio-Temporal Only Temporal
Ground-truth
TEMPORAL AND SPATIAL RECURRENCES
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Only Spatio-Temporal Only Temporal
Only Temporal first half
Ground-truth
TEMPORAL AND SPATIAL RECURRENCES
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Only Spatio-Temporal Only Temporal
Only Temporal first half Only Temporal second half
Ground-truth
TEMPORAL AND SPATIAL RECURRENCES
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LOSS
PENALIZATION
BY OBJECT AREA
LOSS PENALIZATION BY OBJECT AREA
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KITTI-MOTS contains instances with different resolution:
LOSS PENALIZATION BY OBJECT AREA
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KITTI-MOTS contains instances with different resolution:
LOSS PENALIZATION BY OBJECT AREA
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KITTI-MOTS contains instances with different resolution:
LOSS PENALIZATION BY OBJECT AREA
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An hypothesis is made:
DIFFICULT
LOSS PENALIZATION BY OBJECT AREA
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An hypothesis is made:
DIFFICULT EASY
LOSS PENALIZATION BY OBJECT AREA
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A curriculum is created:
time
LOSS PENALIZATION BY OBJECT AREA
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A curriculum is created:
time
LOSS PENALIZATION BY OBJECT AREA
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LOSS PENALIZATION BY OBJECT AREA
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Resolution Batch Size Length clip
287x950 2 3
Resolution Batch Size Length clip
256x448 4 5
LOSS PENALIZATION BY OBJECT AREA
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Resolution Batch Size Length clip
287x950 2 3
Resolution Batch Size Length clip
256x448 4 5
LAST MINUTE
RESULTS
LAST MINUTE RESULTS
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QUALITATIVE
RESULTS
YouTube-VOS
YouTube-VOS
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Ning Xu et al. “YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark”, ECCV 2018
YouTube-VOS
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- Training parameters:
Resolution Batch Size Length clip
256x448 4 5
YouTube-VOS
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- Training parameters:
- Evaluated with the official metrics of the YouTube-VOS challenge.
Resolution Batch Size Length clip
256x448 4 5
YouTube-VOS
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- Training parameters:
- Evaluated with the official metrics of the YouTube-VOS challenge.
Resolution Batch Size Length clip
256x448 4 5
YouTube-VOS
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- Training parameters:
- Evaluated with the official metrics of the YouTube-VOS challenge.
Resolution Batch Size Length clip
256x448 4 5
YouTube-VOS
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Forward Linear Inverse Linear
Forward Step Inverse Linear
YouTube-VOS
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Results on KITTI-MOTS Results on YouTube-VOS
YouTube-VOS
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adapted
Frame skipping from 0 to 3From 0 to 9
YouTube-VOS
Curriculum Learning for Recurrent VOS - 127 of 144
Results on YouTube-VOSResults on KITTI-MOTS
CONCLUSIONS
CONCLUSIONS
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SCHEDULE SAMPLING FRAME SKIPPING
LOSS PENALIZATION BY OBJECT AREATEMPORAL AND SPATIAL RECURRENCES
CONCLUSIONS
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SCHEDULE SAMPLING FRAME SKIPPING
LOSS PENALIZATION BY OBJECT AREATEMPORAL AND SPATIAL RECURRENCES
CONCLUSIONS
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SCHEDULE SAMPLING FRAME SKIPPING
LOSS PENALIZATION BY OBJECT AREATEMPORAL AND SPATIAL RECURRENCES
CONCLUSIONS
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SCHEDULE SAMPLING FRAME SKIPPING
LOSS PENALIZATION BY OBJECT AREATEMPORAL AND SPATIAL RECURRENCES
CONCLUSIONS
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SCHEDULE SAMPLING FRAME SKIPPING
LOSS PENALIZATION BY OBJECT AREATEMPORAL AND SPATIAL RECURRENCES
CONCLUSIONS
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Importance of knowing the dataset.
KITTI-MOTS YouTube-VOS
CONCLUSIONS
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Importance of knowing the dataset.
KITTI-MOTS YouTube-VOS
CONCLUSIONS
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Importance of knowing the dataset.
KITTI-MOTS YouTube-VOS
FUTURE WORK
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FUTURE WORK
Curriculum Learning for Recurrent VOS - 138 of 144
Schedule Sampling
FUTURE WORK
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Schedule Sampling Frame Skipping
FUTURE WORK
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Schedule Sampling Frame Skipping
Loss penalization
by object area
FUTURE WORK
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Schedule Sampling Frame Skipping
Loss penalization
by object area
Other curriculums
FUTURE WORK
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Schedule Sampling Frame Skipping
Loss penalization
by object area
Other curriculums
Combination of the
best curriculums
WORKSHOP SUBMISSIONS
Curriculum Learning for Recurrent VOS - 143 of 144
Acceptance Notification: August 3, 2020
PAD2020
Curriculum Learning for Recurrent Video Object Segmentation
Maria Gonzalez Calabuig
Barcelona, 24th July 2020

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