Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PatchNet: Unsupervised Object Discovery based on Patch Embedding   We demonstrate that frequently appearing objects can be discovered by
training randomly sampled patches from a small number of images (100 to 200) by
self-supervision. Key to this approach is the pattern space, a latent space of
patterns that represents all possible sub-images of the given image data. The
distance structure in the pattern space captures the co-occurrence of patterns
due to the frequent objects. The pattern space embedding is learned by
minimizing the contrastive loss between randomly generated adjacent patches. To
prevent the embedding from learning the background, we modulate the contrastive
loss by color-based object saliency and background dissimilarity. The learned
distance structure serves as object memory, and the frequent objects are simply
discovered by clustering the pattern vectors from the random patches sampled
for inference. Our image representation based on image patches naturally
handles the position and scale invariance property that is crucial to
multi-object discovery. The method has been proven surprisingly effective, and
successfully applied to finding multiple human faces and bodies from natural
images.

2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph