scholarly journals Fine-Grained Video-Text Retrieval With Hierarchical Graph Reasoning

2020 ◽  
Author(s):  
Shizhe Chen ◽  
Yida Zhao ◽  
Qin Jin ◽  
Qi Wu
Keyword(s):  
Text Retrieval ◽  
Fine Grained ◽  
Hierarchical Graph

scholarly journals Dig into Multi-modal Cues for Video Retrieval with Hierarchical Alignment

2021 ◽  
Author(s):  
Wenzhe Wang ◽  
Mengdan Zhang ◽  
Runnan Chen ◽  
Guanyu Cai ◽  
Penghao Zhou ◽  
...  
Keyword(s):  
Video Retrieval ◽  
State Of The Art ◽  
Text Retrieval ◽  
Local Alignment ◽  
Global Alignment ◽  
Retrieval Task ◽  
Fine Grained ◽  
Global View ◽  
High Level ◽  
Semantic Correspondence

Multi-modal cues presented in videos are usually beneficial for the challenging video-text retrieval task on internet-scale datasets. Recent video retrieval methods take advantage of multi-modal cues by aggregating them to holistic high-level semantics for matching with text representations in a global view. In contrast to this global alignment, the local alignment of detailed semantics encoded within both multi-modal cues and distinct phrases is still not well conducted. Thus, in this paper, we leverage the hierarchical video-text alignment to fully explore the detailed diverse characteristics in multi-modal cues for fine-grained alignment with local semantics from phrases, as well as to capture a high-level semantic correspondence. Specifically, multi-step attention is learned for progressively comprehensive local alignment and a holistic transformer is utilized to summarize multi-modal cues for global alignment. With hierarchical alignment, our model outperforms state-of-the-art methods on three public video retrieval datasets.

Hybrid Deep Neural Network-Based Cross-Modal Image and Text Retrieval Method for Large-Scale Data

2020 ◽  
pp. 2150018
Author(s):  
Baohua Qiang ◽  
Ruidong Chen ◽  
Yuan Xie ◽  
Mingliang Zhou ◽  
Riwei Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Deep Neural Network ◽  
Text Retrieval ◽  
Retrieval Method ◽  
Multimodal Data ◽  
Hierarchical Learning ◽  
Fine Grained ◽  
Large Scale Data ◽  
Scale Data

In this paper, we propose the hybrid deep neural network-based cross-modal image and text retrieval method to explore complex cross-modal correlation by considering multi-layer learning. First, we propose intra-modal and inter-modal representations to achieve a complementary single-modal representation that preserves the correlation between the modalities. Second, we build an association between different modalities through hierarchical learning to further mine the fine-grained latent semantic association among multimodal data. The experimental results show that our algorithm substantially enhances retrieval performance and consistently outperforms four comparison methods.

Application of an image management system in microscopy

1992 ◽  
Vol 50 (2) ◽  
pp. 1052-1053
Author(s):  
Richard S. Chemock
Keyword(s):  
Data Storage ◽  
Cost Savings ◽  
Image Data ◽  
Analytical Techniques ◽  
Operational Mode ◽  
Fine Grained ◽  
Image Recording ◽  
Primary Output ◽  
Capacity Data ◽  
Image Management System

One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.

Use of fractals to describe microstructures of porous thick-film platinum electrodes

1992 ◽  
Vol 50 (1) ◽  
pp. 314-315
Author(s):  
Steven D. Toteda
Keyword(s):  
Fractal Dimension ◽  
Power Plants ◽  
Electrical Response ◽  
Cubic Phase ◽  
Platinum Electrodes ◽  
Fine Grained ◽  
Impedance Response ◽  
Platinum Particles ◽  
Energy Surfaces ◽  
Highly Porous

Zirconia oxygen sensors, in such applications as power plants and automobiles, generally utilize platinum electrodes for the catalytic reaction of dissociating O2 at the surface. The microstructure of the platinum electrode defines the resulting electrical response. The electrode must be porous enough to allow the oxygen to reach the zirconia surface while still remaining electrically continuous. At low sintering temperatures, the platinum is highly porous and fine grained. The platinum particles sinter together as the firing temperatures are increased. As the sintering temperatures are raised even further, the surface of the platinum begins to facet with lower energy surfaces. These microstructural changes can be seen in Figures 1 and 2, but the goal of the work is to characterize the microstructure by its fractal dimension and then relate the fractal dimension to the electrical response. The sensors were fabricated from zirconia powder stabilized in the cubic phase with 8 mol% percent yttria. Each substrate was sintered for 14 hours at 1200°C. The resulting zirconia pellets, 13mm in diameter and 2mm in thickness, were roughly 97 to 98 percent of theoretical density. The Engelhard #6082 platinum paste was applied to the zirconia disks after they were mechanically polished ( diamond). The electrodes were then sintered at temperatures ranging from 600°C to 1000°C. Each sensor was tested to determine the impedance response from 1Hz to 5,000Hz. These frequencies correspond to the electrode at the test temperature of 600°C.

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