3,809 research outputs found
Multimodal Content Analysis for Effective Advertisements on YouTube
The rapid advances in e-commerce and Web 2.0 technologies have greatly
increased the impact of commercial advertisements on the general public. As a
key enabling technology, a multitude of recommender systems exists which
analyzes user features and browsing patterns to recommend appealing
advertisements to users. In this work, we seek to study the characteristics or
attributes that characterize an effective advertisement and recommend a useful
set of features to aid the designing and production processes of commercial
advertisements. We analyze the temporal patterns from multimedia content of
advertisement videos including auditory, visual and textual components, and
study their individual roles and synergies in the success of an advertisement.
The objective of this work is then to measure the effectiveness of an
advertisement, and to recommend a useful set of features to advertisement
designers to make it more successful and approachable to users. Our proposed
framework employs the signal processing technique of cross modality feature
learning where data streams from different components are employed to train
separate neural network models and are then fused together to learn a shared
representation. Subsequently, a neural network model trained on this joint
feature embedding representation is utilized as a classifier to predict
advertisement effectiveness. We validate our approach using subjective ratings
from a dedicated user study, the sentiment strength of online viewer comments,
and a viewer opinion metric of the ratio of the Likes and Views received by
each advertisement from an online platform.Comment: 11 pages, 5 figures, ICDM 201
AMC: Attention guided Multi-modal Correlation Learning for Image Search
Given a user's query, traditional image search systems rank images according
to its relevance to a single modality (e.g., image content or surrounding
text). Nowadays, an increasing number of images on the Internet are available
with associated meta data in rich modalities (e.g., titles, keywords, tags,
etc.), which can be exploited for better similarity measure with queries. In
this paper, we leverage visual and textual modalities for image search by
learning their correlation with input query. According to the intent of query,
attention mechanism can be introduced to adaptively balance the importance of
different modalities. We propose a novel Attention guided Multi-modal
Correlation (AMC) learning method which consists of a jointly learned hierarchy
of intra and inter-attention networks. Conditioned on query's intent,
intra-attention networks (i.e., visual intra-attention network and language
intra-attention network) attend on informative parts within each modality; a
multi-modal inter-attention network promotes the importance of the most
query-relevant modalities. In experiments, we evaluate AMC models on the search
logs from two real world image search engines and show a significant boost on
the ranking of user-clicked images in search results. Additionally, we extend
AMC models to caption ranking task on COCO dataset and achieve competitive
results compared with recent state-of-the-arts.Comment: CVPR 201
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Scene Graph Generation with External Knowledge and Image Reconstruction
Scene graph generation has received growing attention with the advancements
in image understanding tasks such as object detection, attributes and
relationship prediction,~\etc. However, existing datasets are biased in terms
of object and relationship labels, or often come with noisy and missing
annotations, which makes the development of a reliable scene graph prediction
model very challenging. In this paper, we propose a novel scene graph
generation algorithm with external knowledge and image reconstruction loss to
overcome these dataset issues. In particular, we extract commonsense knowledge
from the external knowledge base to refine object and phrase features for
improving generalizability in scene graph generation. To address the bias of
noisy object annotations, we introduce an auxiliary image reconstruction path
to regularize the scene graph generation network. Extensive experiments show
that our framework can generate better scene graphs, achieving the
state-of-the-art performance on two benchmark datasets: Visual Relationship
Detection and Visual Genome datasets.Comment: 10 pages, 5 figures, Accepted in CVPR 201
Semantic bottleneck for computer vision tasks
This paper introduces a novel method for the representation of images that is
semantic by nature, addressing the question of computation intelligibility in
computer vision tasks. More specifically, our proposition is to introduce what
we call a semantic bottleneck in the processing pipeline, which is a crossing
point in which the representation of the image is entirely expressed with
natural language , while retaining the efficiency of numerical representations.
We show that our approach is able to generate semantic representations that
give state-of-the-art results on semantic content-based image retrieval and
also perform very well on image classification tasks. Intelligibility is
evaluated through user centered experiments for failure detection
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
Modeling textual or visual information with vector representations trained
from large language or visual datasets has been successfully explored in recent
years. However, tasks such as visual question answering require combining these
vector representations with each other. Approaches to multimodal pooling
include element-wise product or sum, as well as concatenation of the visual and
textual representations. We hypothesize that these methods are not as
expressive as an outer product of the visual and textual vectors. As the outer
product is typically infeasible due to its high dimensionality, we instead
propose utilizing Multimodal Compact Bilinear pooling (MCB) to efficiently and
expressively combine multimodal features. We extensively evaluate MCB on the
visual question answering and grounding tasks. We consistently show the benefit
of MCB over ablations without MCB. For visual question answering, we present an
architecture which uses MCB twice, once for predicting attention over spatial
features and again to combine the attended representation with the question
representation. This model outperforms the state-of-the-art on the Visual7W
dataset and the VQA challenge.Comment: Accepted to EMNLP 201
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