72,791 research outputs found
Weakly-supervised Micro- and Macro-expression Spotting Based on Multi-level Consistency
Most micro- and macro-expression spotting methods in untrimmed videos suffer
from the burden of video-wise collection and frame-wise annotation.
Weakly-supervised expression spotting (WES) based on video-level labels can
potentially mitigate the complexity of frame-level annotation while achieving
fine-grained frame-level spotting. However, we argue that existing
weakly-supervised methods are based on multiple instance learning (MIL)
involving inter-modality, inter-sample, and inter-task gaps. The inter-sample
gap is primarily from the sample distribution and duration. Therefore, we
propose a novel and simple WES framework, MC-WES, using multi-consistency
collaborative mechanisms that include modal-level saliency, video-level
distribution, label-level duration and segment-level feature consistency
strategies to implement fine frame-level spotting with only video-level labels
to alleviate the above gaps and merge prior knowledge. The modal-level saliency
consistency strategy focuses on capturing key correlations between raw images
and optical flow. The video-level distribution consistency strategy utilizes
the difference of sparsity in temporal distribution. The label-level duration
consistency strategy exploits the difference in the duration of facial muscles.
The segment-level feature consistency strategy emphasizes that features under
the same labels maintain similarity. Experimental results on three challenging
datasets -- CAS(ME), CAS(ME), and SAMM-LV -- demonstrate that MC-WES is
comparable to state-of-the-art fully-supervised methods
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
Comparison between Suitable Priors for Additive Bayesian Networks
Additive Bayesian networks are types of graphical models that extend the
usual Bayesian generalized linear model to multiple dependent variables through
the factorisation of the joint probability distribution of the underlying
variables. When fitting an ABN model, the choice of the prior of the parameters
is of crucial importance. If an inadequate prior - like a too weakly
informative one - is used, data separation and data sparsity lead to issues in
the model selection process. In this work a simulation study between two weakly
and a strongly informative priors is presented. As weakly informative prior we
use a zero mean Gaussian prior with a large variance, currently implemented in
the R-package abn. The second prior belongs to the Student's t-distribution,
specifically designed for logistic regressions and, finally, the strongly
informative prior is again Gaussian with mean equal to true parameter value and
a small variance. We compare the impact of these priors on the accuracy of the
learned additive Bayesian network in function of different parameters. We
create a simulation study to illustrate Lindley's paradox based on the prior
choice. We then conclude by highlighting the good performance of the
informative Student's t-prior and the limited impact of the Lindley's paradox.
Finally, suggestions for further developments are provided.Comment: 8 pages, 4 figure
Robustness and Generalization
We derive generalization bounds for learning algorithms based on their
robustness: the property that if a testing sample is "similar" to a training
sample, then the testing error is close to the training error. This provides a
novel approach, different from the complexity or stability arguments, to study
generalization of learning algorithms. We further show that a weak notion of
robustness is both sufficient and necessary for generalizability, which implies
that robustness is a fundamental property for learning algorithms to work
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