ترغب بنشر مسار تعليمي؟ اضغط هنا

From Lost to Found: Discover Missing UI Design Semantics through Recovering Missing Tags

117   0   0.0 ( 0 )
 نشر من قبل Sidong Feng
 تاريخ النشر 2020
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

Design sharing sites provide UI designers with a platform to share their works and also an opportunity to get inspiration from others designs. To facilitate management and search of millions of UI design images, many design sharing sites adopt collaborative tagging systems by distributing the work of categorization to the community. However, designers often do not know how to properly tag one design image with compact textual description, resulting in unclear, incomplete, and inconsistent tags for uploaded examples which impede retrieval, according to our empirical study and interview with four professional designers. Based on a deep neural network, we introduce a novel approach for encoding both the visual and textual information to recover the missing tags for existing UI examples so that they can be more easily found by text queries. We achieve 82.72% accuracy in the tag prediction. Through a simulation test of 5 queries, our system on average returns hundreds more results than the default Dribbble search, leading to better relatedness, diversity and satisfaction.



قيم البحث

اقرأ أيضاً

A general method for recovering missing DCT coefficients in DCT-transformed images is presented in this work. We model the DCT coefficients recovery problem as an optimization problem and recover all missing DCT coefficients via linear programming. T he visual quality of the recovered image gradually decreases as the number of missing DCT coefficients increases. For some images, the quality is surprisingly good even when more than 10 most significant DCT coefficients are missing. When only the DC coefficient is missing, the proposed algorithm outperforms existing methods according to experimental results conducted on 200 test images. The proposed recovery method can be used for cryptanalysis of DCT based selective encryption schemes and other applications.
Ultra-wideband (UWB) radar systems nowadays typical operate in the low frequency spectrum to achieve penetration capability. However, this spectrum is also shared by many others communication systems, which causes missing information in the frequency bands. To recover this missing spectral information, we propose a generative adversarial network, called SARGAN, that learns the relationship between original and missing band signals by observing these training pairs in a clever way. Initial results shows that this approach is promising in tackling this challenging missing band problem.
Speech is understood better by using visual context; for this reason, there have been many attempts to use images to adapt automatic speech recognition (ASR) systems. Current work, however, has shown that visually adapted ASR models only use images a s a regularization signal, while completely ignoring their semantic content. In this paper, we present a set of experiments where we show the utility of the visual modality under noisy conditions. Our results show that multimodal ASR models can recover words which are masked in the input acoustic signal, by grounding its transcriptions using the visual representations. We observe that integrating visual context can result in up to 35% relative improvement in masked word recovery. These results demonstrate that end-to-end multimodal ASR systems can become more robust to noise by leveraging the visual context.
UI design is an integral part of software development. For many developers who do not have much UI design experience, exposing them to a large database of real-application UI designs can help them quickly build up a realistic understanding of the des ign space for a software feature and get design inspirations from existing applications. However, existing keyword-based, image-similarity-based, and component-matching-based methods cannot reliably find relevant high-fidelity UI designs in a large database alike to the UI wireframe that the developers sketch, in face of the great variations in UI designs. In this article, we propose a deep-learning-based UI design search engine to fill in the gap. The key innovation of our search engine is to train a wireframe image autoencoder using a large database of real-application UI designs, without the need for labeling relevant UI designs. We implement our approach for Android UI design search, and conduct extensive experiments with artificially created relevant UI designs and human evaluation of UI design search results. Our experiments confirm the superior performance of our search engine over existing image-similarity or component-matching-based methods and demonstrate the usefulness of our search engine in real-world UI design tasks.
Searches for supersymmetry (SUSY) often rely on a combination of hard physics objects (jets, leptons) along with large missing transverse energy to separate New Physics from Standard Model hard processes. We consider a class of ``double-invisible SUS Y scenarios: where squarks, stops and sbottoms have a three-body decay into two (rather than one) invisible final-state particles. This occurs naturally when the LSP carries an additional conserved quantum number under which other superpartners are not charged. In these topologies, the available energy is diluted into invisible particles, reducing the observed missing energy and visible energy. This can lead to sizable changes in the sensitivity of existing searches, dramatically changing the qualitative constraints on superpartners. In particular, for m_LSP>160 GeV, we find no robust constraints from the LHC at any squark mass for any generation, while for lighter LSPs we find significant reductions in constraints. If confirmed by a full reanalysis from the collaborations, such scenarios allow for the possibility of significantly more natural SUSY models. While not realized in the MSSM, such phenomenology occurs naturally in models with mixed sneutrinos, Dirac gauginos and NMSSM-like models.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا