34 research outputs found
Connectionist Temporal Modeling for Weakly Supervised Action Labeling
We propose a weakly-supervised framework for action labeling in video, where
only the order of occurring actions is required during training time. The key
challenge is that the per-frame alignments between the input (video) and label
(action) sequences are unknown during training. We address this by introducing
the Extended Connectionist Temporal Classification (ECTC) framework to
efficiently evaluate all possible alignments via dynamic programming and
explicitly enforce their consistency with frame-to-frame visual similarities.
This protects the model from distractions of visually inconsistent or
degenerated alignments without the need of temporal supervision. We further
extend our framework to the semi-supervised case when a few frames are sparsely
annotated in a video. With less than 1% of labeled frames per video, our method
is able to outperform existing semi-supervised approaches and achieve
comparable performance to that of fully supervised approaches.Comment: To appear in ECCV 201
Weakly Supervised Action Localization by Sparse Temporal Pooling Network
We propose a weakly supervised temporal action localization algorithm on
untrimmed videos using convolutional neural networks. Our algorithm learns from
video-level class labels and predicts temporal intervals of human actions with
no requirement of temporal localization annotations. We design our network to
identify a sparse subset of key segments associated with target actions in a
video using an attention module and fuse the key segments through adaptive
temporal pooling. Our loss function is comprised of two terms that minimize the
video-level action classification error and enforce the sparsity of the segment
selection. At inference time, we extract and score temporal proposals using
temporal class activations and class-agnostic attentions to estimate the time
intervals that correspond to target actions. The proposed algorithm attains
state-of-the-art results on the THUMOS14 dataset and outstanding performance on
ActivityNet1.3 even with its weak supervision.Comment: Accepted to CVPR 201
Going Deeper with Semantics: Video Activity Interpretation using Semantic Contextualization
A deeper understanding of video activities extends beyond recognition of
underlying concepts such as actions and objects: constructing deep semantic
representations requires reasoning about the semantic relationships among these
concepts, often beyond what is directly observed in the data. To this end, we
propose an energy minimization framework that leverages large-scale commonsense
knowledge bases, such as ConceptNet, to provide contextual cues to establish
semantic relationships among entities directly hypothesized from video signal.
We mathematically express this using the language of Grenander's canonical
pattern generator theory. We show that the use of prior encoded commonsense
knowledge alleviate the need for large annotated training datasets and help
tackle imbalance in training through prior knowledge. Using three different
publicly available datasets - Charades, Microsoft Visual Description Corpus and
Breakfast Actions datasets, we show that the proposed model can generate video
interpretations whose quality is better than those reported by state-of-the-art
approaches, which have substantial training needs. Through extensive
experiments, we show that the use of commonsense knowledge from ConceptNet
allows the proposed approach to handle various challenges such as training data
imbalance, weak features, and complex semantic relationships and visual scenes.Comment: Accepted to WACV 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
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