Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DILEMMA: Self-Supervised Shape and Texture Learning with Transformers   There is a growing belief that deep neural networks with a shape bias may
exhibit better generalization capabilities than models with a texture bias,
because shape is a more reliable indicator of the object category. However, we
show experimentally that existing measures of shape bias are not stable
predictors of generalization and argue that shape discrimination should not
come at the expense of texture discrimination. Thus, we propose a pseudo-task
to explicitly boost both shape and texture discriminability in models trained
via self-supervised learning. For this purpose, we train a ViT to detect which
input token has been combined with an incorrect positional embedding. To retain
texture discrimination, the ViT is also trained as in MoCo with a
student-teacher architecture and a contrastive loss over an extra learnable
class token. We call our method DILEMMA, which stands for Detection of
Incorrect Location EMbeddings with MAsked inputs. We evaluate our method
through fine-tuning on several datasets and show that it outperforms MoCoV3 and
DINO. Moreover, we show that when downstream tasks are strongly reliant on
shape (such as in the YOGA-82 pose dataset), our pre-trained features yield a
significant gain over prior work. Code will be released upon publication.

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The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph