The fourth edition of the "VIPriors: Visual Inductive Priors for
Data-Efficient Deep Learning" workshop features two data-impaired challenges.
These challenges address the problem of training deep learning models for
computer vision tasks with limited data. Participants are limited to training
models from scratch using a low number of training samples and are not allowed
to use any form of transfer learning. We aim to stimulate the development of
novel approaches that incorporate inductive biases to improve the data
efficiency of deep learning models. Significant advancements are made compared
to the provided baselines, where winning solutions surpass the baselines by a
considerable margin in both tasks. As in previous editions, these achievements
are primarily attributed to heavy use of data augmentation policies and large
model ensembles, though novel prior-based methods seem to contribute more to
successful solutions compared to last year. This report highlights the key
aspects of the challenges and their outcomes.

第四届 “VIPriors：视觉归纳先验的高效深度学习” 研讨会涵盖了两个数据受限挑战，旨在通过引入归纳偏差改善深度学习模型的数据效率，取得显著进展，重点介绍了这些挑战及其结果。

VIPriors 4：用于数据高效深度学习的视觉归纳先验挑战

VIPriors 4: Visual Inductive Priors for Data-Efficient Deep Learning  Challenges

Instance segmentation is applied widely in image editing, image analysis and
autonomous driving, etc. However, insufficient data is a common problem in
practical applications. The Visual Inductive Priors(VIPriors) Instance
Segmentation Challenge has focused on this problem. VIPriors for Data-Efficient
Computer Vision Challenges ask competitors to train models from scratch in a
data-deficient setting, but there are some visual inductive priors that can be
used. In order to address the VIPriors instance segmentation problem, we
designed a Task-Specific Data Augmentation(TS-DA) strategy and Inference
Processing(TS-IP) strategy. The main purpose of task-specific data augmentation
strategy is to tackle the data-deficient problem. And in order to make the most
of visual inductive priors, we designed a task-specific inference processing
strategy. We demonstrate the applicability of proposed method on VIPriors
Instance Segmentation Challenge. The segmentation model applied is Hybrid Task
Cascade based detector on the Swin-Base based CBNetV2 backbone. Experimental
results demonstrate that proposed method can achieve a competitive result on
the test set of 2022 VIPriors Instance Segmentation Challenge, with 0.531
AP@0.50:0.95.

本文旨在解决实际应用中数据不足的问题，通过提出 Task-Specific Data Augmentation (TS-DA) 策略和 Inference Processing (TS-IP) 策略的方法，充分利用视觉归纳因素，提高 VIPriors Instance Segmentation Challenge 上的表现。经过实验证明，提出的方法在 2022 年 VIPriors Instance Segmentation Challenge 的测试集中达到竞争水平，并取得了 0.531 的 AP@0.50:0.95。

VIPriors 实例分割挑战赛的任务特定数据增强和推理处理

Task-Specific Data Augmentation and Inference Processing for VIPriors  Instance Segmentation Challenge

Deep-Learning-based video recognition has shown promising improvements along
with the development of large-scale datasets and spatiotemporal network
architectures. In image recognition, learning spatially invariant features is a
key factor in improving recognition performance and robustness. Data
augmentation based on visual inductive priors, such as cropping, flipping,
rotating, or photometric jittering, is a representative approach to achieve
these features. Recent state-of-the-art recognition solutions have relied on
modern data augmentation strategies that exploit a mixture of augmentation
operations. In this study, we extend these strategies to the temporal dimension
for videos to learn temporally invariant or temporally localizable features to
cover temporal perturbations or complex actions in videos. Based on our novel
temporal data augmentation algorithms, video recognition performances are
improved using only a limited amount of training data compared to the
spatial-only data augmentation algorithms, including the 1st Visual Inductive
Priors (VIPriors) for data-efficient action recognition challenge. Furthermore,
learned features are temporally localizable that cannot be achieved using
spatial augmentation algorithms. Our source code is available at
this https URL

本研究基于深度学习和视觉归纳先验，提出一种在视频数据上进行时空数据扩充与学习的方法，即 “时序扩充”，以提高视频识别性能和鲁棒性。通过这种方法，能够在更少的训练数据上，实现超越空间扩充算法的识别效果，并实现无法用空间算法实现的时序本地化特征学习。