Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Investigating Modality Bias in Audio Visual Video Parsing   We focus on the audio-visual video parsing (AVVP) problem that involves
detecting audio and visual event labels with temporal boundaries. The task is
especially challenging since it is weakly supervised with only event labels
available as a bag of labels for each video. An existing state-of-the-art model
for AVVP uses a hybrid attention network (HAN) to generate cross-modal features
for both audio and visual modalities, and an attentive pooling module that
aggregates predicted audio and visual segment-level event probabilities to
yield video-level event probabilities. We provide a detailed analysis of
modality bias in the existing HAN architecture, where a modality is completely
ignored during prediction. We also propose a variant of feature aggregation in
HAN that leads to an absolute gain in F-scores of about 2% and 1.6% for visual
and audio-visual events at both segment-level and event-level, in comparison to
the existing HAN model.

18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
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The categories of this paper are: q-alg
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The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con