3,409 research outputs found
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets
Visual question answering (Visual QA) has attracted a lot of attention
lately, seen essentially as a form of (visual) Turing test that artificial
intelligence should strive to achieve. In this paper, we study a crucial
component of this task: how can we design good datasets for the task? We focus
on the design of multiple-choice based datasets where the learner has to select
the right answer from a set of candidate ones including the target (\ie the
correct one) and the decoys (\ie the incorrect ones). Through careful analysis
of the results attained by state-of-the-art learning models and human
annotators on existing datasets, we show that the design of the decoy answers
has a significant impact on how and what the learning models learn from the
datasets. In particular, the resulting learner can ignore the visual
information, the question, or both while still doing well on the task. Inspired
by this, we propose automatic procedures to remedy such design deficiencies. We
apply the procedures to re-construct decoy answers for two popular Visual QA
datasets as well as to create a new Visual QA dataset from the Visual Genome
project, resulting in the largest dataset for this task. Extensive empirical
studies show that the design deficiencies have been alleviated in the remedied
datasets and the performance on them is likely a more faithful indicator of the
difference among learning models. The datasets are released and publicly
available via http://www.teds.usc.edu/website_vqa/.Comment: Accepted for Oral Presentation at NAACL-HLT 201
A study of The Impact of Perceived Psychological Contract and Perceived Fairness as Mediators Between Structural Assurance Constructs and Purchasing Intention in B2C E-commerce
This study examines whether perceived psychological contract and perceived fairness can serve as mediators between certain website design features (retailer guarantees and seals of approval guarantees) and perceived trust and risk beliefs. Based on the psychological contract theory and organizational justice theory, this research would examine the nomological validities of perceived psychological contract and perceived fairness in business-to-consumer e-commerce context. The results could demonstrate how perceived trust and perceived risk can be influenced by certain design features, and help online retailers improve their website design and business practices
The Effectiveness of Customer Service Policies on Intentions in Business-To-Consumer E-Commerce: A Psychological Contract Perspective
This study examines the influence of customer service policies on consumer purchase intentions in business-to-consumer electronic commerce. Prior research findings propose that practitioners can utilize these policies to influence customers\u27 purchase intentions. Based on the psychological contract theory, this study conducts an experiment to examine whether customer service policies can influence consumers\u27 intentions through their perceived psychological contract with the vendors. The experiment finds that a customer can form psychological contract beliefs with a vendor based on the vendor\u27s customer service policies. These psychological contract beliefs may influence purchase intentions through trusting beliefs. These results should help web vendors realize the importance of customer service policies and improve their website design strategies
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild
Zero-shot learning (ZSL) methods have been studied in the unrealistic setting
where test data are assumed to come from unseen classes only. In this paper, we
advocate studying the problem of generalized zero-shot learning (GZSL) where
the test data's class memberships are unconstrained. We show empirically that
naively using the classifiers constructed by ZSL approaches does not perform
well in the generalized setting. Motivated by this, we propose a simple but
effective calibration method that can be used to balance two conflicting
forces: recognizing data from seen classes versus those from unseen ones. We
develop a performance metric to characterize such a trade-off and examine the
utility of this metric in evaluating various ZSL approaches. Our analysis
further shows that there is a large gap between the performance of existing
approaches and an upper bound established via idealized semantic embeddings,
suggesting that improving class semantic embeddings is vital to GZSL.Comment: ECCV2016 camera-read
Large-Margin Determinantal Point Processes
Determinantal point processes (DPPs) offer a powerful approach to modeling
diversity in many applications where the goal is to select a diverse subset. We
study the problem of learning the parameters (the kernel matrix) of a DPP from
labeled training data. We make two contributions. First, we show how to
reparameterize a DPP's kernel matrix with multiple kernel functions, thus
enhancing modeling flexibility. Second, we propose a novel parameter estimation
technique based on the principle of large margin separation. In contrast to the
state-of-the-art method of maximum likelihood estimation, our large-margin loss
function explicitly models errors in selecting the target subsets, and it can
be customized to trade off different types of errors (precision vs. recall).
Extensive empirical studies validate our contributions, including applications
on challenging document and video summarization, where flexibility in modeling
the kernel matrix and balancing different errors is indispensable.Comment: 15 page
- …