Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences
  for Urban Scene Segmentation   Supervised learning in large discriminative models is a mainstay for modern
computer vision. Such an approach necessitates investing in large-scale
human-annotated datasets for achieving state-of-the-art results. In turn, the
efficacy of supervised learning may be limited by the size of the human
annotated dataset. This limitation is particularly notable for image
segmentation tasks, where the expense of human annotation is especially large,
yet large amounts of unlabeled data may exist. In this work, we ask if we may
leverage semi-supervised learning in unlabeled video sequences and extra images
to improve the performance on urban scene segmentation, simultaneously tackling
semantic, instance, and panoptic segmentation. The goal of this work is to
avoid the construction of sophisticated, learned architectures specific to
label propagation (e.g., patch matching and optical flow). Instead, we simply
predict pseudo-labels for the unlabeled data and train subsequent models with
both human-annotated and pseudo-labeled data. The procedure is iterated for
several times. As a result, our Naive-Student model, trained with such simple
yet effective iterative semi-supervised learning, attains state-of-the-art
results at all three Cityscapes benchmarks, reaching the performance of 67.8%
PQ, 42.6% AP, and 85.2% mIOU on the test set. We view this work as a notable
step towards building a simple procedure to harness unlabeled video sequences
and extra images to surpass state-of-the-art performance on core computer
vision tasks.

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The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an