21 research outputs found
THE MICROWAVE SPECTRUM OF DICHLOROSILYLENE EXCITED VIBRATIONAL STATES
No full text1. M. Tanimoto, H. Takeo, C. Matsumura, M. Fujitake, and E. Hirota, J. Chem. Phys. 91, 2102 (1989).Author Institution: Department of Physics, Faculty of Science, Kanazawa Unviersity; Department of Physics, The Graduate University for Advanced StudiesThe microwave spectrum of dichlorosilylene in excited vibrational states has been measured in the millimeter-and submillimeter-wave regions, Rotational and centrifugal distortion constatnts were determined for the , and excited states of the isotopic species and for the and state of . Analysis of the Coriolis resonance between the and states of yielded values of the {D} Coriolis interaction constant with {F} constrained and of two higher-order terms and also an accurate value [] of the energy difference between the two excited vibrational states. The rotational constants of in the first excited states of the three normal vibrations were combined with those of the ground vibrational state reported in a previous to obtain the equilibrium structure, harmonic and cubic/third-order anharmonic potential constants
DiffusionSTR: Diffusion Model for Scene Text Recognition
No full textThis paper presents Diffusion Model for Scene Text Recognition
(DiffusionSTR), an end-to-end text recognition framework using diffusion models
for recognizing text in the wild. While existing studies have viewed the scene
text recognition task as an image-to-text transformation, we rethought it as a
text-text one under images in a diffusion model. We show for the first time
that the diffusion model can be applied to text recognition. Furthermore,
experimental results on publicly available datasets show that the proposed
method achieves competitive accuracy compared to state-of-the-art methods.Comment: Accepted to ICIP 202
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding
No full textThis paper proposes LayoutLLM, a more flexible document analysis method for
understanding imaged documents. Visually Rich Document Understanding tasks,
such as document image classification and information extraction, have gained
significant attention due to their importance. Existing methods have been
developed to enhance document comprehension by incorporating pre-training
awareness of images, text, and layout structure. However, these methods require
fine-tuning for each task and dataset, and the models are expensive to train
and operate. To overcome this limitation, we propose a new LayoutLLM that
integrates these with large-scale language models (LLMs). By leveraging the
strengths of existing research in document image understanding and LLMs'
superior language understanding capabilities, the proposed model, fine-tuned
with multimodal instruction datasets, performs an understanding of document
images in a single model. Our experiments demonstrate improvement over the
baseline model in various document analysis tasks.Comment: LREC-COLING 202
FA Team at the NTCIR-17 UFO Task
No full textThe FA team participated in the Table Data Extraction (TDE) and Text-to-Table Relationship Extraction (TTRE) task of the NTCIR-17 Understanding of Non-Financial Objects in Financial Reports (UFO). This paper reports our approach to solving the problem and discusses the official results. We successfully utilized various enhancement techniques based on the ELECTRA language model to extract valuable data from tables. Our efforts resulted in an impressive TDE accuracy rate of 93.43\%, positioning us in second place on the Leaderboard rankings. This outstanding achievement is a testament to our proposed approach's effectiveness. In the TTRE task, we proposed the rule-based method to extract meaningful relationships between the text and tables task and confirmed the performance
FA Team at the NTCIR-17 UFO Task
No full textThe FA team participated in the Table Data Extraction (TDE) and Text-to-Table Relationship Extraction (TTRE) task of the NTCIR-17 Understanding of Non-Financial Objects in Financial Reports (UFO). This paper reports our approach to solving the problem and discusses the official results. We successfully utilized various enhancement techniques based on the ELECTRA language model to extract valuable data from tables. Our efforts resulted in an impressive TDE accuracy rate of 93.43\%, positioning us in second place on the Leaderboard rankings. This outstanding achievement is a testament to our proposed approach's effectiveness. In the TTRE task, we proposed the rule-based method to extract meaningful relationships between the text and tables task and confirmed the performance.conference pape
FA Team at the NTCIR-17 UFO Task
No full textThe FA team participated in the Table Data Extraction (TDE) and Text-to-Table
Relationship Extraction (TTRE) tasks of the NTCIR-17 Understanding of
Non-Financial Objects in Financial Reports (UFO). This paper reports our
approach to solving the problems and discusses the official results. We
successfully utilized various enhancement techniques based on the ELECTRA
language model to extract valuable data from tables. Our efforts resulted in an
impressive TDE accuracy rate of 93.43 %, positioning us in second place on the
Leaderboard rankings. This outstanding achievement is a testament to our
proposed approach's effectiveness. In the TTRE task, we proposed the rule-based
method to extract meaningful relationships between the text and tables task and
confirmed the performance.Comment: To be appeared at the NTCIR-17 Conferenc
Improvement of estimation system of construction equipment’s type from field images by combination learning of construction equipment’s parts
No full textEstimation system of construction equipment from field image by combination learning of its parts
No full textTemporal Feature Enhancement Network with External Memory for Object Detection in Surveillance Video
No full text
