7 research outputs found
FLODCAST: Flow and Depth Forecasting via Multimodal Recurrent Architectures
Forecasting motion and spatial positions of objects is of fundamental
importance, especially in safety-critical settings such as autonomous driving.
In this work, we address the issue by forecasting two different modalities that
carry complementary information, namely optical flow and depth. To this end we
propose FLODCAST a flow and depth forecasting model that leverages a multitask
recurrent architecture, trained to jointly forecast both modalities at once. We
stress the importance of training using flows and depth maps together,
demonstrating that both tasks improve when the model is informed of the other
modality. We train the proposed model to also perform predictions for several
timesteps in the future. This provides better supervision and leads to more
precise predictions, retaining the capability of the model to yield outputs
autoregressively for any future time horizon. We test our model on the
challenging Cityscapes dataset, obtaining state of the art results for both
flow and depth forecasting. Thanks to the high quality of the generated flows,
we also report benefits on the downstream task of segmentation forecasting,
injecting our predictions in a flow-based mask-warping framework.Comment: Submitted to Pattern Recognitio
Forecasting Future Instance Segmentation with Learned Optical Flow and Warping
For an autonomous vehicle it is essential to observe the ongoing dynamics of
a scene and consequently predict imminent future scenarios to ensure safety to
itself and others. This can be done using different sensors and modalities. In
this paper we investigate the usage of optical flow for predicting future
semantic segmentations. To do so we propose a model that forecasts flow fields
autoregressively. Such predictions are then used to guide the inference of a
learned warping function that moves instance segmentations on to future frames.
Results on the Cityscapes dataset demonstrate the effectiveness of optical-flow
methods.Comment: Paper published as Poster at ICIAP2
Deepfake detection by exploiting surface anomalies: the SurFake approach
The ever-increasing use of synthetically generated content in different
sectors of our everyday life, one for all media information, poses a strong
need for deepfake detection tools in order to avoid the proliferation of
altered messages. The process to identify manipulated content, in particular
images and videos, is basically performed by looking for the presence of some
inconsistencies and/or anomalies specifically due to the fake generation
process. Different techniques exist in the scientific literature that exploit
diverse ad-hoc features in order to highlight possible modifications. In this
paper, we propose to investigate how deepfake creation can impact on the
characteristics that the whole scene had at the time of the acquisition. In
particular, when an image (video) is captured the overall geometry of the scene
(e.g. surfaces) and the acquisition process (e.g. illumination) determine a
univocal environment that is directly represented by the image pixel values;
all these intrinsic relations are possibly changed by the deepfake generation
process. By resorting to the analysis of the characteristics of the surfaces
depicted in the image it is possible to obtain a descriptor usable to train a
CNN for deepfake detection: we refer to such an approach as SurFake.
Experimental results carried out on the FF++ dataset for different kinds of
deepfake forgeries and diverse deep learning models confirm that such a feature
can be adopted to discriminate between pristine and altered images;
furthermore, experiments witness that it can also be combined with visual data
to provide a certain improvement in terms of detection accuracy
Narrative medicine educational project to improve the care of patients with chronic obstructive pulmonary disease
Chronic obstructive pulmonary disease (COPD) is characterised by a progressive loss of pulmonary function. Often patients do not adhere to inhaled therapies and this leads clinicians to switch treatments in order to improve control of the symptoms. Narrative medicine is a useful approach that helps healthcare professionals to think over the doctor–patient relationship and how patients live with their disease. The aim of this training project was to teach pulmonologists the basics of narrative medicine: to carefully listen to patients and to practice reflective writing in their relationship with them. Training on narrative medicine and parallel charts was provided through a webinar and a weekly newsletter. Across 362 narratives, written by 74 Italian pulmonologists, 92% of patients had activity limitations at their first visit. The main factor influencing the effectiveness and adherence to therapy was a positive doctor–patient relationship; indeed, if such relationship is difficult, only 21% of patients are able to resume all their activities. After learning the narrative approach, clinicians became aware of the need to spend more time listening to patients, to reflect through writing and to understand more deeply the motivations that lead people towards adherence to new therapies