4,022 research outputs found
Unsupervised Action Proposal Ranking through Proposal Recombination
Recently, action proposal methods have played an important role in action
recognition tasks, as they reduce the search space dramatically. Most
unsupervised action proposal methods tend to generate hundreds of action
proposals which include many noisy, inconsistent, and unranked action
proposals, while supervised action proposal methods take advantage of
predefined object detectors (e.g., human detector) to refine and score the
action proposals, but they require thousands of manual annotations to train.
Given the action proposals in a video, the goal of the proposed work is to
generate a few better action proposals that are ranked properly. In our
approach, we first divide action proposal into sub-proposal and then use
Dynamic Programming based graph optimization scheme to select the optimal
combinations of sub-proposals from different proposals and assign each new
proposal a score. We propose a new unsupervised image-based actioness detector
that leverages web images and employs it as one of the node scores in our graph
formulation. Moreover, we capture motion information by estimating the number
of motion contours within each action proposal patch. The proposed method is an
unsupervised method that neither needs bounding box annotations nor video level
labels, which is desirable with the current explosion of large-scale action
datasets. Our approach is generic and does not depend on a specific action
proposal method. We evaluate our approach on several publicly available trimmed
and un-trimmed datasets and obtain better performance compared to several
proposal ranking methods. In addition, we demonstrate that properly ranked
proposals produce significantly better action detection as compared to
state-of-the-art proposal based methods
Deep Discrete Hashing with Self-supervised Pairwise Labels
Hashing methods have been widely used for applications of large-scale image
retrieval and classification. Non-deep hashing methods using handcrafted
features have been significantly outperformed by deep hashing methods due to
their better feature representation and end-to-end learning framework. However,
the most striking successes in deep hashing have mostly involved discriminative
models, which require labels. In this paper, we propose a novel unsupervised
deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image
retrieval and classification. In the proposed framework, we address two main
problems: 1) how to directly learn discrete binary codes? 2) how to equip the
binary representation with the ability of accurate image retrieval and
classification in an unsupervised way? We resolve these problems by introducing
an intermediate variable and a loss function steering the learning process,
which is based on the neighborhood structure in the original space.
Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17)
demonstrate that our DDH significantly outperforms existing hashing methods by
large margin in terms of~mAP for image retrieval and object recognition. Code
is available at \url{https://github.com/htconquer/ddh}
Localizing Actions from Video Labels and Pseudo-Annotations
The goal of this paper is to determine the spatio-temporal location of
actions in video. Where training from hard to obtain box annotations is the
norm, we propose an intuitive and effective algorithm that localizes actions
from their class label only. We are inspired by recent work showing that
unsupervised action proposals selected with human point-supervision perform as
well as using expensive box annotations. Rather than asking users to provide
point supervision, we propose fully automatic visual cues that replace manual
point annotations. We call the cues pseudo-annotations, introduce five of them,
and propose a correlation metric for automatically selecting and combining
them. Thorough evaluation on challenging action localization datasets shows
that we reach results comparable to results with full box supervision. We also
show that pseudo-annotations can be leveraged during testing to improve weakly-
and strongly-supervised localizers.Comment: BMV
Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource
Word embeddings have recently seen a strong increase in interest as a result
of strong performance gains on a variety of tasks. However, most of this
research also underlined the importance of benchmark datasets, and the
difficulty of constructing these for a variety of language-specific tasks.
Still, many of the datasets used in these tasks could prove to be fruitful
linguistic resources, allowing for unique observations into language use and
variability. In this paper we demonstrate the performance of multiple types of
embeddings, created with both count and prediction-based architectures on a
variety of corpora, in two language-specific tasks: relation evaluation, and
dialect identification. For the latter, we compare unsupervised methods with a
traditional, hand-crafted dictionary. With this research, we provide the
embeddings themselves, the relation evaluation task benchmark for use in
further research, and demonstrate how the benchmarked embeddings prove a useful
unsupervised linguistic resource, effectively used in a downstream task.Comment: in LREC 201
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