Training a computer vision system to segment a novel class typically requires
collecting and painstakingly annotating lots of images with objects from that
class. Few-shot segmentation techniques reduce the required number of images to
learn to segment a new class, but careful annotations of object boundaries are
still required. On the other hand, interactive segmentation techniques only
focus on incrementally improving the segmentation of one object at a time
(typically, using clicks given by an expert) in a class-agnostic manner. We
combine the two concepts to drastically reduce the effort required to train
segmentation models for novel classes. Instead of trivially feeding interactive
segmentation masks as ground truth to a few-shot segmentation model, we propose
IFSENet, which can accept sparse supervision on a single or few support images
in the form of clicks to generate masks on support (training, at least clicked
upon once) as well as query (test, never clicked upon) images. To trade-off
effort for accuracy flexibly, the number of images and clicks can be
incrementally added to the support set to further improve the segmentation of
support as well as query images. The proposed model approaches the accuracy of
previous state-of-the-art few-shot segmentation models with considerably lower
annotation effort (clicks instead of maps), when tested on Pascal and SBD
datasets on query images. It also works well as an interactive segmentation
method on support images.

IFSENet 是一种结合了 few-shot 分割和交互式分割概念的模型，通过接受点击输入的方式在支持图像和查询图像上生成遮罩，极大地减少了训练新类别分割模型所需的注释工作量。

IFSENet：利用稀疏迭代实现交互式少样本分割的卓越性能

IFSENet : Harnessing Sparse Iterations for Interactive Few-shot  Segmentation Excellence

Association football is a complex and dynamic sport, with numerous actions
occurring simultaneously in each game. Analyzing football videos is challenging
and requires identifying subtle and diverse spatio-temporal patterns. Despite
recent advances in computer vision, current algorithms still face significant
challenges when learning from limited annotated data, lowering their
performance in detecting these patterns. In this paper, we propose an active
learning framework that selects the most informative video samples to be
annotated next, thus drastically reducing the annotation effort and
accelerating the training of action spotting models to reach the highest
accuracy at a faster pace. Our approach leverages the notion of uncertainty
sampling to select the most challenging video clips to train on next, hastening
the learning process of the algorithm. We demonstrate that our proposed active
learning framework effectively reduces the required training data for accurate
action spotting in football videos. We achieve similar performances for action
spotting with NetVLAD++ on SoccerNet-v2, using only one-third of the dataset,
indicating significant capabilities for reducing annotation time and improving
data efficiency. We further validate our approach on two new datasets that
focus on temporally localizing actions of headers and passes, proving its
effectiveness across different action semantics in football. We believe our
active learning framework for action spotting would support further
applications of action spotting algorithms and accelerate annotation campaigns
in the sports domain.

本文提出了一种主动学习框架，通过选择最具信息量的视频样本加速模型训练和优化算法，实现在减少注释工作量的同时提高在足球视频中的行为检测精度，适用于通过降低注释时间，提高数据效率来加速注释运动领域数据集的应用。

关于足球比赛视频中动作检测主动学习的研究

Towards Active Learning for Action Spotting in Association Football  Videos

Building a large image dataset with high-quality object masks for semantic
segmentation is costly and time consuming. In this paper, we introduce a
principled semi-supervised framework that only uses a small set of fully
supervised images (having semantic segmentation labels and box labels) and a
set of images with only object bounding box labels (we call it the weak set).
Our framework trains the primary segmentation model with the aid of an
ancillary model that generates initial segmentation labels for the weak set and
a self-correction module that improves the generated labels during training
using the increasingly accurate primary model. We introduce two variants of the
self-correction module using either linear or convolutional functions.
Experiments on the PASCAL VOC 2012 and Cityscape datasets show that our models
trained with a small fully supervised set perform similar to, or better than,
models trained with a large fully supervised set while requiring ~7x less
annotation effort.

本研究介绍了一种半监督框架，通过一个辅助模型和一个自我纠正模块，在只有一小部分完全有监督图像的基础上，使用具有目标边界框标签的图像和只有目标边界框标签的图像集（称为弱集），训练出高质量的语义分割模型，这种方法比传统大量完全有监督数据模型要求的标注工作量减少～7 倍。