It could be argued, though, that the grey part on the left of the foreground also feels and acts like negative space. The foreground mass, however, is much larger and heavier than both of them, so in hindsight, perhaps I should have given it a bit more space. In the image below, the two background masses have similar compositional weights, with the left mass being slightly more detailed and more prominent, and so the amount of space around it is a bit larger than that for the mass on the right. I thus chose to avoid adding too much negative around it and have it relatively close to the edges of the frame - do you agree with my choice?Ĭanon 5D2, Tamron 24-70mm F2.8, 1/200 sec, F4, ISO1600 The foreground element here is very large, but it's not very prominent - most of it is just as bright as its surroundings, with only the outer lava being brighter. Less space than this would make the framing too tight and render the image less appealing. You could look at it as though a heavy element has influence beyond its own borders, and forbids other information from competing for the viewer's attention in that perimeter.Ī double-headed lava dragon is a big, prominent and detailed mass, and thus needs a lot of space around it. This could be summed up very easily: the heavier the compositional weight of an element, the more space it needs between itself and other elements. Too little space and too much space are both counterproductive to the appeal of a composition, so we need to understand how the properties of an element determine the amount of space it needs around it. But contrary to what determines weight, space has only one property: how much of it there is-or isn't.
Space around one element doesn't only affect that element, it also affects how much space we need around other elements, lest we lose compositional balance. Negative space both accentuates an element and separates it from others, contributing to how much the viewer's eye is drawn to a specific location in an image. Just like the size, prominence and level of detail, the negative space around a compositional mass is intimately connected to its location and to how it interacts with other masses in an image. But there is much more to keeping an image appealing and balanced, and this time I'd like to discuss another important consideration: unoccupied space around the elements, or as it's better known: negative space.
Negative space image how to#
So far in this series, I've talked about the weights of compositional elements and how to use their properties to balance the composition. Canon 5D3, Tamron 24-70mm F2.8, 1/60 sec, F8, ISO100 Weissbad, Switzerland We demonstrate an average gain of about 5% over two different state-of-the-art methods in this domain.Negative space strongly enhances the drawing factor of this lone tree on a foggy background. Finally, we contribute to adversarial learning by incorporating our method in CSL. We close up to 68% of the robustness gap between CSL and its supervised counterpart. We devise a strategy to detect and remove false negative pairs that is simple, yet effective in improving model robustness with CSL training. Our finding is supported by extensive experiments for image and video classification using adversarial perturbations and other input corruptions. We establish that this is a result of the presence of false negative pairs in the training process, which increases model sensitivity to input perturbations. We identify the uniform distribution of data representation over a unit hypersphere in the CSL representation space as the key contributor to this phenomenon.
Our analysis of the problem reveals that CSL has intrinsically higher sensitivity to perturbations over supervised learning. We investigate this under the lens of adversarial robustness. However, it is still largely unknown if the nature of the representations induced by the two learning paradigms is similar. Contrastive self-supervised learning (CSL) has managed to match or surpass the performance of supervised learning in image and video classification.
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