23,911 research outputs found
Fine-Grained Car Detection for Visual Census Estimation
Targeted socioeconomic policies require an accurate understanding of a
country's demographic makeup. To that end, the United States spends more than 1
billion dollars a year gathering census data such as race, gender, education,
occupation and unemployment rates. Compared to the traditional method of
collecting surveys across many years which is costly and labor intensive,
data-driven, machine learning driven approaches are cheaper and faster--with
the potential ability to detect trends in close to real time. In this work, we
leverage the ubiquity of Google Street View images and develop a computer
vision pipeline to predict income, per capita carbon emission, crime rates and
other city attributes from a single source of publicly available visual data.
We first detect cars in 50 million images across 200 of the largest US cities
and train a model to predict demographic attributes using the detected cars. To
facilitate our work, we have collected the largest and most challenging
fine-grained dataset reported to date consisting of over 2600 classes of cars
comprised of images from Google Street View and other web sources, classified
by car experts to account for even the most subtle of visual differences. We
use this data to construct the largest scale fine-grained detection system
reported to date. Our prediction results correlate well with ground truth
income data (r=0.82), Massachusetts department of vehicle registration, and
sources investigating crime rates, income segregation, per capita carbon
emission, and other market research. Finally, we learn interesting
relationships between cars and neighborhoods allowing us to perform the first
large scale sociological analysis of cities using computer vision techniques.Comment: AAAI 201
Temporal Localization of Fine-Grained Actions in Videos by Domain Transfer from Web Images
We address the problem of fine-grained action localization from temporally
untrimmed web videos. We assume that only weak video-level annotations are
available for training. The goal is to use these weak labels to identify
temporal segments corresponding to the actions, and learn models that
generalize to unconstrained web videos. We find that web images queried by
action names serve as well-localized highlights for many actions, but are
noisily labeled. To solve this problem, we propose a simple yet effective
method that takes weak video labels and noisy image labels as input, and
generates localized action frames as output. This is achieved by cross-domain
transfer between video frames and web images, using pre-trained deep
convolutional neural networks. We then use the localized action frames to train
action recognition models with long short-term memory networks. We collect a
fine-grained sports action data set FGA-240 of more than 130,000 YouTube
videos. It has 240 fine-grained actions under 85 sports activities. Convincing
results are shown on the FGA-240 data set, as well as the THUMOS 2014
localization data set with untrimmed training videos.Comment: Camera ready version for ACM Multimedia 201
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers
Following the major success of neural language models (LMs) such as BERT or
GPT-2 on a variety of language understanding tasks, recent work focused on
injecting (structured) knowledge from external resources into these models.
While on the one hand, joint pretraining (i.e., training from scratch, adding
objectives based on external knowledge to the primary LM objective) may be
prohibitively computationally expensive, post-hoc fine-tuning on external
knowledge, on the other hand, may lead to the catastrophic forgetting of
distributional knowledge. In this work, we investigate models for complementing
the distributional knowledge of BERT with conceptual knowledge from ConceptNet
and its corresponding Open Mind Common Sense (OMCS) corpus, respectively, using
adapter training. While overall results on the GLUE benchmark paint an
inconclusive picture, a deeper analysis reveals that our adapter-based models
substantially outperform BERT (up to 15-20 performance points) on inference
tasks that require the type of conceptual knowledge explicitly present in
ConceptNet and OMCS
Rude waiter but mouthwatering pastries! An exploratory study into Dutch aspect-based sentiment analysis
The fine-grained task of automatically detecting all sentiment expressions within a given document and the aspects to which they refer is known as aspect-based sentiment analysis. In this paper we present the first full aspect-based sentiment analysis pipeline for Dutch
and apply it to customer reviews. To this purpose, we collected reviews from two different domains, i.e. restaurant and smartphone reviews. Both corpora have been manually annotated using newly developed guidelines that comply to standard practices in the field. For our experimental pipeline we perceive aspect-based sentiment analysis as a task consisting of three main subtasks which have to be tackled incrementally: aspect term extraction, aspect category classification and polarity classification. First experiments on our Dutch restaurant corpus reveal that this is indeed a feasible approach that yields promising results
Characterizing Location-based Mobile Tracking in Mobile Ad Networks
Mobile apps nowadays are often packaged with third-party ad libraries to
monetize user data
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
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