470,729 research outputs found
Rethinking Multi-Modal Alignment in Video Question Answering from Feature and Sample Perspectives
Reasoning about causal and temporal event relations in videos is a new
destination of Video Question Answering (VideoQA).The major stumbling block to
achieve this purpose is the semantic gap between language and video since they
are at different levels of abstraction. Existing efforts mainly focus on
designing sophisticated architectures while utilizing frame- or object-level
visual representations. In this paper, we reconsider the multi-modal alignment
problem in VideoQA from feature and sample perspectives to achieve better
performance. From the view of feature,we break down the video into trajectories
and first leverage trajectory feature in VideoQA to enhance the alignment
between two modalities. Moreover, we adopt a heterogeneous graph architecture
and design a hierarchical framework to align both trajectory-level and
frame-level visual feature with language feature. In addition, we found that
VideoQA models are largely dependent on language priors and always neglect
visual-language interactions. Thus, two effective yet portable training
augmentation strategies are designed to strengthen the cross-modal
correspondence ability of our model from the view of sample. Extensive results
show that our method outperforms all the state-of-the-art models on the
challenging NExT-QA benchmark, which demonstrates the effectiveness of the
proposed method
FF2: A Feature Fusion Two-Stream Framework for Punctuation Restoration
To accomplish punctuation restoration, most existing methods focus on
introducing extra information (e.g., part-of-speech) or addressing the class
imbalance problem. Recently, large-scale transformer-based pre-trained language
models (PLMS) have been utilized widely and obtained remarkable success.
However, the PLMS are trained on the large dataset with marks, which may not
fit well with the small dataset without marks, causing the convergence to be
not ideal. In this study, we propose a Feature Fusion two-stream framework
(FF2) to bridge the gap. Specifically, one stream leverages a pre-trained
language model to capture the semantic feature, while another auxiliary module
captures the feature at hand. We also modify the computation of multi-head
attention to encourage communication among heads. Then, two features with
different perspectives are aggregated to fuse information and enhance context
awareness. Without additional data, the experimental results on the popular
benchmark IWSLT demonstrate that FF2 achieves new SOTA performance, which
verifies that our approach is effective.Comment: 5pages. arXiv admin note: substantial text overlap with
arXiv:2203.1248
Reparation in evolutionary algorithms for multi-objective feature selection in large software product lines
Software Product Lines Engineering is the area of software engineering that aims to systematise the modelling, creation and improvement of groups of interconnected software systems by formally expressing possible alternative products in the form of Feature Models. Deriving a software product/system from a feature model is called Feature Configuration. Engineers select the subset of features (software components) from a feature model that suits their needs, while respecting the underlying relationships/constraints of the system–which is challenging on its own. Since there exist several (and often antagonistic) perspectives on which the quality of software could be assessed, the problem is even more challenging as it becomes a multi-objective optimisation problem. Current multi-objective feature selection in software product line approaches (e.g., SATIBEA) combine the scalability of a genetic algorithm (IBEA) with a solution reparation approach based on a SAT solver or one of its derivatives. In this paper, we propose MILPIBEA, a novel hybrid algorithm which combines IBEA with the accuracy of a mixed-integer linear programming (MILP) reparation. We show that the MILP reparation modifies fewer features from the original infeasible solutions than the SAT reparation and in a shorter time. We also demonstrate that MILPIBEA outperforms SATIBEA on average on various multi-objective performance metrics, especially on the largest feature models. The other major challenge in software engineering in general and in software product lines, in particular, is evolution. While the change in software components is common in the software engineering industry, the particular case of multi-objective optimisation of evolving software product lines is not well-tackled yet. We show that MILPIBEA is not only able to better take advantage of the evolution than SATIBEA, but it is also the one that continues to improve the quality of the solutions when SATIBEA stagnates. Overall, IBEA performs better when combined with MILP instead of SAT reparation when optimising the multi-objective feature selection in large and evolving software product lines
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
Incremental Consistency Checking in Delta-oriented UML-Models for Automation Systems
Automation systems exist in many variants and may evolve over time in order
to deal with different environment contexts or to fulfill changing customer
requirements. This induces an increased complexity during design-time as well
as tedious maintenance efforts. We already proposed a multi-perspective
modeling approach to improve the development of such systems. It operates on
different levels of abstraction by using well-known UML-models with activity,
composite structure and state chart models. Each perspective was enriched with
delta modeling to manage variability and evolution. As an extension, we now
focus on the development of an efficient consistency checking method at several
levels to ensure valid variants of the automation system. Consistency checking
must be provided for each perspective in isolation, in-between the perspectives
as well as after the application of a delta.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857
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