Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Featureless: Bypassing feature extraction in action categorization   This method introduces an efficient manner of learning action categories
without the need of feature estimation. The approach starts from low-level
values, in a similar style to the successful CNN methods. However, rather than
extracting general image features, we learn to predict specific video
representations from raw video data. The benefit of such an approach is that at
the same computational expense it can predict 2 D video representations as well
as 3 D ones, based on motion. The proposed model relies on discriminative
Waldboost, which we enhance to a multiclass formulation for the purpose of
learning video representations. The suitability of the proposed approach as
well as its time efficiency are tested on the UCF11 action recognition dataset.

11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph