1,082 research outputs found
Regularizing Face Verification Nets For Pain Intensity Regression
Limited labeled data are available for the research of estimating facial
expression intensities. For instance, the ability to train deep networks for
automated pain assessment is limited by small datasets with labels of
patient-reported pain intensities. Fortunately, fine-tuning from a
data-extensive pre-trained domain, such as face verification, can alleviate
this problem. In this paper, we propose a network that fine-tunes a
state-of-the-art face verification network using a regularized regression loss
and additional data with expression labels. In this way, the expression
intensity regression task can benefit from the rich feature representations
trained on a huge amount of data for face verification. The proposed
regularized deep regressor is applied to estimate the pain expression intensity
and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving
the state-of-the-art performance. A weighted evaluation metric is also proposed
to address the imbalance issue of different pain intensities.Comment: 5 pages, 3 figure; Camera-ready version to appear at IEEE ICIP 201
Attentive Single-Tasking of Multiple Tasks
In this work we address task interference in universal networks by
considering that a network is trained on multiple tasks, but performs one task
at a time, an approach we refer to as "single-tasking multiple tasks". The
network thus modifies its behaviour through task-dependent feature adaptation,
or task attention. This gives the network the ability to accentuate the
features that are adapted to a task, while shunning irrelevant ones. We further
reduce task interference by forcing the task gradients to be statistically
indistinguishable through adversarial training, ensuring that the common
backbone architecture serving all tasks is not dominated by any of the
task-specific gradients. Results in three multi-task dense labelling problems
consistently show: (i) a large reduction in the number of parameters while
preserving, or even improving performance and (ii) a smooth trade-off between
computation and multi-task accuracy. We provide our system's code and
pre-trained models at http://vision.ee.ethz.ch/~kmaninis/astmt/.Comment: CVPR 2019 Camera Read
Loss Guided Activation for Action Recognition in Still Images
One significant problem of deep-learning based human action recognition is
that it can be easily misled by the presence of irrelevant objects or
backgrounds. Existing methods commonly address this problem by employing
bounding boxes on the target humans as part of the input, in both training and
testing stages. This requirement of bounding boxes as part of the input is
needed to enable the methods to ignore irrelevant contexts and extract only
human features. However, we consider this solution is inefficient, since the
bounding boxes might not be available. Hence, instead of using a person
bounding box as an input, we introduce a human-mask loss to automatically guide
the activations of the feature maps to the target human who is performing the
action, and hence suppress the activations of misleading contexts. We propose a
multi-task deep learning method that jointly predicts the human action class
and human location heatmap. Extensive experiments demonstrate our approach is
more robust compared to the baseline methods under the presence of irrelevant
misleading contexts. Our method achieves 94.06\% and 40.65\% (in terms of mAP)
on Stanford40 and MPII dataset respectively, which are 3.14\% and 12.6\%
relative improvements over the best results reported in the literature, and
thus set new state-of-the-art results. Additionally, unlike some existing
methods, we eliminate the requirement of using a person bounding box as an
input during testing.Comment: Accepted to appear in ACCV 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
Deep Unsupervised Similarity Learning using Partially Ordered Sets
Unsupervised learning of visual similarities is of paramount importance to
computer vision, particularly due to lacking training data for fine-grained
similarities. Deep learning of similarities is often based on relationships
between pairs or triplets of samples. Many of these relations are unreliable
and mutually contradicting, implying inconsistencies when trained without
supervision information that relates different tuples or triplets to each
other. To overcome this problem, we use local estimates of reliable
(dis-)similarities to initially group samples into compact surrogate classes
and use local partial orders of samples to classes to link classes to each
other. Similarity learning is then formulated as a partial ordering task with
soft correspondences of all samples to classes. Adopting a strategy of
self-supervision, a CNN is trained to optimally represent samples in a mutually
consistent manner while updating the classes. The similarity learning and
grouping procedure are integrated in a single model and optimized jointly. The
proposed unsupervised approach shows competitive performance on detailed pose
estimation and object classification.Comment: Accepted for publication at IEEE Computer Vision and Pattern
Recognition 201
Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation is receiving great attention due to
its low human annotation cost. In this paper, we aim to tackle bounding box
supervised semantic segmentation, i.e., training accurate semantic segmentation
models using bounding box annotations as supervision. To this end, we propose
Affinity Attention Graph Neural Network (GNN). Following previous
practices, we first generate pseudo semantic-aware seeds, which are then formed
into semantic graphs based on our newly proposed affinity Convolutional Neural
Network (CNN). Then the built graphs are input to our GNN, in which an
affinity attention layer is designed to acquire the short- and long- distance
information from soft graph edges to accurately propagate semantic labels from
the confident seeds to the unlabeled pixels. However, to guarantee the
precision of the seeds, we only adopt a limited number of confident pixel seed
labels for GNN, which may lead to insufficient supervision for training.
To alleviate this issue, we further introduce a new loss function and a
consistency-checking mechanism to leverage the bounding box constraint, so that
more reliable guidance can be included for the model optimization. Experiments
show that our approach achieves new state-of-the-art performances on Pascal VOC
2012 datasets (val: 76.5\%, test: 75.2\%). More importantly, our approach can
be readily applied to bounding box supervised instance segmentation task or
other weakly supervised semantic segmentation tasks, with state-of-the-art or
comparable performance among almot all weakly supervised tasks on PASCAL VOC or
COCO dataset. Our source code will be available at
https://github.com/zbf1991/A2GNN.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence (TAPMI 2021
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