7,826 research outputs found
Personalization of Saliency Estimation
Most existing saliency models use low-level features or task descriptions
when generating attention predictions. However, the link between observer
characteristics and gaze patterns is rarely investigated. We present a novel
saliency prediction technique which takes viewers' identities and personal
traits into consideration when modeling human attention. Instead of only
computing image salience for average observers, we consider the interpersonal
variation in the viewing behaviors of observers with different personal traits
and backgrounds. We present an enriched derivative of the GAN network, which is
able to generate personalized saliency predictions when fed with image stimuli
and specific information about the observer. Our model contains a generator
which generates grayscale saliency heat maps based on the image and an observer
label. The generator is paired with an adversarial discriminator which learns
to distinguish generated salience from ground truth salience. The discriminator
also has the observer label as an input, which contributes to the
personalization ability of our approach. We evaluate the performance of our
personalized salience model by comparison with a benchmark model along with
other un-personalized predictions, and illustrate improvements in prediction
accuracy for all tested observer groups
Unsupervised Domain Adaptation on Reading Comprehension
Reading comprehension (RC) has been studied in a variety of datasets with the
boosted performance brought by deep neural networks. However, the
generalization capability of these models across different domains remains
unclear. To alleviate this issue, we are going to investigate unsupervised
domain adaptation on RC, wherein a model is trained on labeled source domain
and to be applied to the target domain with only unlabeled samples. We first
show that even with the powerful BERT contextual representation, the
performance is still unsatisfactory when the model trained on one dataset is
directly applied to another target dataset. To solve this, we provide a novel
conditional adversarial self-training method (CASe). Specifically, our approach
leverages a BERT model fine-tuned on the source dataset along with the
confidence filtering to generate reliable pseudo-labeled samples in the target
domain for self-training. On the other hand, it further reduces domain
distribution discrepancy through conditional adversarial learning across
domains. Extensive experiments show our approach achieves comparable accuracy
to supervised models on multiple large-scale benchmark datasets.Comment: 8 pages, 6 figures, 5 tables, Accepted by AAAI 202
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