Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tackling the Unannotated: Scene Graph Generation with Bias-Reduced
  Models   Predicting a scene graph that captures visual entities and their interactions
in an image has been considered a crucial step towards full scene
comprehension. Recent scene graph generation (SGG) models have shown their
capability of capturing the most frequent relations among visual entities.
However, the state-of-the-art results are still far from satisfactory, e.g.
models can obtain 31% in overall recall R@100, whereas the likewise important
mean class-wise recall mR@100 is only around 8% on Visual Genome (VG). The
discrepancy between R and mR results urges to shift the focus from pursuing a
high R to a high mR with a still competitive R. We suspect that the observed
discrepancy stems from both the annotation bias and sparse annotations in VG,
in which many visual entity pairs are either not annotated at all or only with
a single relation when multiple ones could be valid. To address this particular
issue, we propose a novel SGG training scheme that capitalizes on self-learned
knowledge. It involves two relation classifiers, one offering a less biased
setting for the other to base on. The proposed scheme can be applied to most of
the existing SGG models and is straightforward to implement. We observe
significant relative improvements in mR (between +6.6% and +20.4%) and
competitive or better R (between -2.4% and 0.3%) across all standard SGG tasks.

5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con