Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs   Structured video representation in the form of dynamic scene graphs is an
effective tool for several video understanding tasks. Compared to the task of
scene graph generation from images, dynamic scene graph generation is more
challenging due to the temporal dynamics of the scene and the inherent temporal
fluctuations of predictions. We show that capturing long-term dependencies is
the key to effective generation of dynamic scene graphs. We present the
detect-track-recognize paradigm by constructing consistent long-term object
tracklets from a video, followed by transformers to capture the dynamics of
objects and visual relations. Experimental results demonstrate that our Dynamic
Scene Graph Detection Transformer (DSG-DETR) outperforms state-of-the-art
methods by a significant margin on the benchmark dataset Action Genome. We also
perform ablation studies and validate the effectiveness of each component of
the proposed approach.

17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio