7,810 research outputs found
Zero-Annotation Object Detection with Web Knowledge Transfer
Object detection is one of the major problems in computer vision, and has
been extensively studied. Most of the existing detection works rely on
labor-intensive supervision, such as ground truth bounding boxes of objects or
at least image-level annotations. On the contrary, we propose an object
detection method that does not require any form of human annotation on target
tasks, by exploiting freely available web images. In order to facilitate
effective knowledge transfer from web images, we introduce a multi-instance
multi-label domain adaption learning framework with two key innovations. First
of all, we propose an instance-level adversarial domain adaptation network with
attention on foreground objects to transfer the object appearances from web
domain to target domain. Second, to preserve the class-specific semantic
structure of transferred object features, we propose a simultaneous transfer
mechanism to transfer the supervision across domains through pseudo strong
label generation. With our end-to-end framework that simultaneously learns a
weakly supervised detector and transfers knowledge across domains, we achieved
significant improvements over baseline methods on the benchmark datasets.Comment: Accepted in ECCV 201
Learning Intelligent Dialogs for Bounding Box Annotation
We introduce Intelligent Annotation Dialogs for bounding box annotation. We
train an agent to automatically choose a sequence of actions for a human
annotator to produce a bounding box in a minimal amount of time. Specifically,
we consider two actions: box verification, where the annotator verifies a box
generated by an object detector, and manual box drawing. We explore two kinds
of agents, one based on predicting the probability that a box will be
positively verified, and the other based on reinforcement learning. We
demonstrate that (1) our agents are able to learn efficient annotation
strategies in several scenarios, automatically adapting to the image
difficulty, the desired quality of the boxes, and the detector strength; (2) in
all scenarios the resulting annotation dialogs speed up annotation compared to
manual box drawing alone and box verification alone, while also outperforming
any fixed combination of verification and drawing in most scenarios; (3) in a
realistic scenario where the detector is iteratively re-trained, our agents
evolve a series of strategies that reflect the shifting trade-off between
verification and drawing as the detector grows stronger.Comment: This paper appeared at CVPR 201
Weakly Supervised Localization using Deep Feature Maps
Object localization is an important computer vision problem with a variety of
applications. The lack of large scale object-level annotations and the relative
abundance of image-level labels makes a compelling case for weak supervision in
the object localization task. Deep Convolutional Neural Networks are a class of
state-of-the-art methods for the related problem of object recognition. In this
paper, we describe a novel object localization algorithm which uses
classification networks trained on only image labels. This weakly supervised
method leverages local spatial and semantic patterns captured in the
convolutional layers of classification networks. We propose an efficient beam
search based approach to detect and localize multiple objects in images. The
proposed method significantly outperforms the state-of-the-art in standard
object localization data-sets with a 8 point increase in mAP scores
Audio Event Detection using Weakly Labeled Data
Acoustic event detection is essential for content analysis and description of
multimedia recordings. The majority of current literature on the topic learns
the detectors through fully-supervised techniques employing strongly labeled
data. However, the labels available for majority of multimedia data are
generally weak and do not provide sufficient detail for such methods to be
employed. In this paper we propose a framework for learning acoustic event
detectors using only weakly labeled data. We first show that audio event
detection using weak labels can be formulated as an Multiple Instance Learning
problem. We then suggest two frameworks for solving multiple-instance learning,
one based on support vector machines, and the other on neural networks. The
proposed methods can help in removing the time consuming and expensive process
of manually annotating data to facilitate fully supervised learning. Moreover,
it can not only detect events in a recording but can also provide temporal
locations of events in the recording. This helps in obtaining a complete
description of the recording and is notable since temporal information was
never known in the first place in weakly labeled data.Comment: ACM Multimedia 201
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