2,141 research outputs found
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
Weakly-Supervised Temporal Localization via Occurrence Count Learning
We propose a novel model for temporal detection and localization which allows
the training of deep neural networks using only counts of event occurrences as
training labels. This powerful weakly-supervised framework alleviates the
burden of the imprecise and time-consuming process of annotating event
locations in temporal data. Unlike existing methods, in which localization is
explicitly achieved by design, our model learns localization implicitly as a
byproduct of learning to count instances. This unique feature is a direct
consequence of the model's theoretical properties. We validate the
effectiveness of our approach in a number of experiments (drum hit and piano
onset detection in audio, digit detection in images) and demonstrate
performance comparable to that of fully-supervised state-of-the-art methods,
despite much weaker training requirements.Comment: Accepted at ICML 201
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
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