9,837 research outputs found
DAP3D-Net: Where, What and How Actions Occur in Videos?
Action parsing in videos with complex scenes is an interesting but
challenging task in computer vision. In this paper, we propose a generic 3D
convolutional neural network in a multi-task learning manner for effective Deep
Action Parsing (DAP3D-Net) in videos. Particularly, in the training phase,
action localization, classification and attributes learning can be jointly
optimized on our appearancemotion data via DAP3D-Net. For an upcoming test
video, we can describe each individual action in the video simultaneously as:
Where the action occurs, What the action is and How the action is performed. To
well demonstrate the effectiveness of the proposed DAP3D-Net, we also
contribute a new Numerous-category Aligned Synthetic Action dataset, i.e.,
NASA, which consists of 200; 000 action clips of more than 300 categories and
with 33 pre-defined action attributes in two hierarchical levels (i.e.,
low-level attributes of basic body part movements and high-level attributes
related to action motion). We learn DAP3D-Net using the NASA dataset and then
evaluate it on our collected Human Action Understanding (HAU) dataset.
Experimental results show that our approach can accurately localize, categorize
and describe multiple actions in realistic videos
New Planar P-time Computable Six-Vertex Models and a Complete Complexity Classification
We discover new P-time computable six-vertex models on planar graphs beyond
Kasteleyn's algorithm for counting planar perfect matchings. We further prove
that there are no more: Together, they exhaust all P-time computable six-vertex
models on planar graphs, assuming #P is not P. This leads to the following
exact complexity classification: For every parameter setting in
for the six-vertex model, the partition function is either (1) computable in
P-time for every graph, or (2) #P-hard for general graphs but computable in
P-time for planar graphs, or (3) #P-hard even for planar graphs. The
classification has an explicit criterion. The new P-time cases in (2) provably
cannot be subsumed by Kasteleyn's algorithm. They are obtained by a non-local
connection to #CSP, defined in terms of a "loop space".
This is the first substantive advance toward a planar Holant classification
with not necessarily symmetric constraints. We introduce M\"obius
transformation on as a powerful new tool in hardness proofs for
counting problems.Comment: 61 pages, 16 figures. An extended abstract appears in SODA 202
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