Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Reason from General Concepts to Fine-grained Tokens for
  Discriminative Phrase Detection   Phrase detection requires methods to identify if a phrase is relevant to an
image and then localize it if applicable. A key challenge in training more
discriminative phrase detection models is sampling hard-negatives. This is
because few phrases are annotated of the nearly infinite variations that may be
applicable. To address this problem, we introduce PFP-Net, a phrase detector
that differentiates between phrases through two novel methods. First, we group
together phrases of related objects into coarse groups of visually coherent
concepts (eg animals vs automobiles), and then train our PFP-Net to
discriminate between them according to their concept membership. Second, for
phrases containing fine grained mutually-exclusive tokens (eg colors), we force
the model into selecting only one applicable phrase for each region. We
evaluate our approach on the Flickr30K Entities and RefCOCO+ datasets, where we
improve mAP over the state-of-the-art by 1-1.5 points over all phrases on this
challenging task. When considering only the phrases affected by our
fine-grained reasoning module, we improve by 1-4 points on both datasets.

11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
2
The categories of this paper are: math
10
The categories of this paper are: q-bio