4,195 research outputs found
Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints
Action detection and temporal segmentation of actions in videos are topics of
increasing interest. While fully supervised systems have gained much attention
lately, full annotation of each action within the video is costly and
impractical for large amounts of video data. Thus, weakly supervised action
detection and temporal segmentation methods are of great importance. While most
works in this area assume an ordered sequence of occurring actions to be given,
our approach only uses a set of actions. Such action sets provide much less
supervision since neither action ordering nor the number of action occurrences
are known. In exchange, they can be easily obtained, for instance, from
meta-tags, while ordered sequences still require human annotation. We introduce
a system that automatically learns to temporally segment and label actions in a
video, where the only supervision that is used are action sets. An evaluation
on three datasets shows that our method still achieves good results although
the amount of supervision is significantly smaller than for other related
methods.Comment: CVPR 201
Weakly-Supervised Action Segmentation with Iterative Soft Boundary Assignment
In this work, we address the task of weakly-supervised human action
segmentation in long, untrimmed videos. Recent methods have relied on expensive
learning models, such as Recurrent Neural Networks (RNN) and Hidden Markov
Models (HMM). However, these methods suffer from expensive computational cost,
thus are unable to be deployed in large scale. To overcome the limitations, the
keys to our design are efficiency and scalability. We propose a novel action
modeling framework, which consists of a new temporal convolutional network,
named Temporal Convolutional Feature Pyramid Network (TCFPN), for predicting
frame-wise action labels, and a novel training strategy for weakly-supervised
sequence modeling, named Iterative Soft Boundary Assignment (ISBA), to align
action sequences and update the network in an iterative fashion. The proposed
framework is evaluated on two benchmark datasets, Breakfast and Hollywood
Extended, with four different evaluation metrics. Extensive experimental
results show that our methods achieve competitive or superior performance to
state-of-the-art methods.Comment: CVPR 201
Weakly Supervised Action Learning with RNN based Fine-to-coarse Modeling
We present an approach for weakly supervised learning of human actions. Given
a set of videos and an ordered list of the occurring actions, the goal is to
infer start and end frames of the related action classes within the video and
to train the respective action classifiers without any need for hand labeled
frame boundaries. To address this task, we propose a combination of a
discriminative representation of subactions, modeled by a recurrent neural
network, and a coarse probabilistic model to allow for a temporal alignment and
inference over long sequences. While this system alone already generates good
results, we show that the performance can be further improved by approximating
the number of subactions to the characteristics of the different action
classes. To this end, we adapt the number of subaction classes by iterating
realignment and reestimation during training. The proposed system is evaluated
on two benchmark datasets, the Breakfast and the Hollywood extended dataset,
showing a competitive performance on various weak learning tasks such as
temporal action segmentation and action alignment
Unsupervised Learning from Narrated Instruction Videos
We address the problem of automatically learning the main steps to complete a
certain task, such as changing a car tire, from a set of narrated instruction
videos. The contributions of this paper are three-fold. First, we develop a new
unsupervised learning approach that takes advantage of the complementary nature
of the input video and the associated narration. The method solves two
clustering problems, one in text and one in video, applied one after each other
and linked by joint constraints to obtain a single coherent sequence of steps
in both modalities. Second, we collect and annotate a new challenging dataset
of real-world instruction videos from the Internet. The dataset contains about
800,000 frames for five different tasks that include complex interactions
between people and objects, and are captured in a variety of indoor and outdoor
settings. Third, we experimentally demonstrate that the proposed method can
automatically discover, in an unsupervised manner, the main steps to achieve
the task and locate the steps in the input videos.Comment: Appears in: 2016 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2016). 21 page
Action Recognition from Single Timestamp Supervision in Untrimmed Videos
Recognising actions in videos relies on labelled supervision during training,
typically the start and end times of each action instance. This supervision is
not only subjective, but also expensive to acquire. Weak video-level
supervision has been successfully exploited for recognition in untrimmed
videos, however it is challenged when the number of different actions in
training videos increases. We propose a method that is supervised by single
timestamps located around each action instance, in untrimmed videos. We replace
expensive action bounds with sampling distributions initialised from these
timestamps. We then use the classifier's response to iteratively update the
sampling distributions. We demonstrate that these distributions converge to the
location and extent of discriminative action segments. We evaluate our method
on three datasets for fine-grained recognition, with increasing number of
different actions per video, and show that single timestamps offer a reasonable
compromise between recognition performance and labelling effort, performing
comparably to full temporal supervision. Our update method improves top-1 test
accuracy by up to 5.4%. across the evaluated datasets.Comment: CVPR 201
- …