The importance of time in visual attention models

Predicting visual attention is a very active field in the computer vision community. Visual attention is a mechanism of the visual system that can select relevant areas within a scene. Models for saliency prediction are intended to automatically predict which regions are likely to be attended by a human observer. Traditionally, ground truth saliency maps are built using only the spatial position of the fixation points, being these fixation points the locations where an observer fixates the gaze when viewing a scene. In this work we explore encoding the temporal information as well, and assess it in the application of prediction saliency maps with deep neural networks. It has been observed that the later fixations in a scanpath are usually selected randomly during visualization, specially in those images with few regions of interest. Therefore, computer vision models have difficulties learning to predict them. In this work, we explore a temporal weighting over the saliency maps to better cope with this random behaviour. The newly proposed saliency representation assigns different weights depending on the position in the sequence of gaze fixations, giving more importance to early timesteps than later ones. We used this maps to train MLNet, a state of the art for predicting saliency maps. MLNet predictions were evaluated and compared to the results obtained when the model has been trained using traditional saliency maps.Finally, we show how the temporally weighted saliency maps brought some improvement when used to weight the visual features in an image retrieval tas

Where Is More Important Than How in Coastal and Marine Ecosystems Restoration

Restoration is considered an effective strategy to accelerate the recovery of biological communities at local scale. However, the effects of restoration actions in the marine ecosystems are still unpredictable.We performed a global analysis of published literature to identify the factors increasing the probability of restoration success in coastal and marine systems. Our results confirm that the majority of active restoration initiatives are still concentrated in the northern hemisphere and that most of information gathered from restoration efforts derives from a relatively small subset of species. The analysis also indicates that many studies are still experimental in nature, covering small spatial and temporal scales. Despite the limits of assessing restoration effectiveness in absence of a standardized definition of success, the context (degree of human impact, ecosystem type, habitat) of where the restoration activity is undertaken is of greater relevance to a successful outcome than how (method) the restoration is carried out. Contrary to expectations, we found that restoration is not necessarily more successful closer to protected areas (PA) and in areas of moderate human impact. This result can be motivated by the limits in assessing the success of interventions and by the tendency of selecting areas in more obvious need of restoration, where the potential of actively restoring a degraded site is more evident. Restoration sites prioritization considering human uses and conservation status present in the region is of vital importance to obtain the intended outcomes and galvanize further actions

The importance of time in visual attention models

Predicting visual attention is a very active field in the computer vision community. Visual attention is a mechanism of the visual system that can select relevant areas within a scene. Models for saliency prediction are intended to automatically predict which regions are likely to be attended by a human observer. Traditionally, ground truth saliency maps are built using only the spatial position of the fixation points, being these fixation points the locations where an observer fixates the gaze when viewing a scene. In this work we explore encoding the temporal information as well, and assess it in the application of prediction saliency maps with deep neural networks. It has been observed that the later fixations in a scanpath are usually selected randomly during visualization, specially in those images with few regions of interest. Therefore, computer vision models have difficulties learning to predict them. In this work, we explore a temporal weighting over the saliency maps to better cope with this random behaviour. The newly proposed saliency representation assigns different weights depending on the position in the sequence of gaze fixations, giving more importance to early timesteps than later ones. We used this maps to train MLNet, a state of the art for predicting saliency maps. MLNet predictions were evaluated and compared to the results obtained when the model has been trained using traditional saliency maps.Finally, we show how the temporally weighted saliency maps brought some improvement when used to weight the visual features in an image retrieval tas

Where Is More Important Than How in Coastal and Marine Ecosystems Restoration

14 pages, 6 figures, 1 table, supplementary material https://www.frontiersin.org/articles/10.3389/fmars.2021.626843/full#supplementary-materialRestoration is considered an effective strategy to accelerate the recovery of biological communities at local scale. However, the effects of restoration actions in the marine ecosystems are still unpredictable. We performed a global analysis of published literature to identify the factors increasing the probability of restoration success in coastal and marine systems. Our results confirm that the majority of active restoration initiatives are still concentrated in the northern hemisphere and that most of information gathered from restoration efforts derives from a relatively small subset of species. The analysis also indicates that many studies are still experimental in nature, covering small spatial and temporal scales. Despite the limits of assessing restoration effectiveness in absence of a standardized definition of success, the context (degree of human impact, ecosystem type, habitat) of where the restoration activity is undertaken is of greater relevance to a successful outcome than how (method) the restoration is carried out. Contrary to expectations, we found that restoration is not necessarily more successful closer to protected areas (PA) and in areas of moderate human impact. This result can be motivated by the limits in assessing the success of interventions and by the tendency of selecting areas in more obvious need of restoration, where the potential of actively restoring a degraded site is more evident. Restoration sites prioritization considering human uses and conservation status present in the region is of vital importance to obtain the intended outcomes and galvanize further actions.Research funded by the EU project MERCES of the European Union's Horizon 2020 research (Grant agreement No. 689518, http://www.merces-project.eu).Research funded by the EU project MERCES of the European Union's Horizon 2020 research (Grant agreement No. 689518, http://www.merces-project.eu)Peer reviewe