Search Results for author:
Found 21 papers, 3 papers with code
VisPercep: A Vision-Language Approach to Enhance Visual Perception for People with Blindness and Low Vision
By combining the prompt and input image, a large vision-language model (i. e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing the environmental objects and scenes, relevant to the prompt.
The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models
By carefully designing the framework and the prompts, we are able to overcome a number of challenges, including bug-specific modeling, the large problem scope, the non-deterministic nature of LLMs, etc.
Testing the Number of Components in Finite Mixture Normal Regression Model with Panel Data
We apply our EM test to estimate the number of production technology types for the finite mixture Cobb-Douglas production function model studied by Kasahara et al. (2022) used the panel data of the Japanese and Chilean manufacturing firms.
Understanding the Impact of Image Quality and Distance of Objects to Object Detection Performance
We further assess the impact of the distance of an object to the camera on the detection accuracy and show that higher spatial resolution enables a greater detection range.
1st Place Solution to ECCV 2022 Challenge on Out of Vocabulary Scene Text Understanding: End-to-End Recognition of Out of Vocabulary Words
Our oCLIP-based model achieves 28. 59\% in h-mean which ranks 1st in end-to-end OOV word recognition track of OOV Challenge in ECCV2022 TiE Workshop.
Detect and Approach: Close-Range Navigation Support for People with Blindness and Low Vision
These functions enable the system to suggest an initial navigation path, continuously update the path as the user moves, and offer timely recommendation about the correction of the user's path.
Runner-Up Solution to ECCV 2022 Challenge on Out of Vocabulary Scene Text Understanding: Cropped Word Recognition
This report presents our 2nd place solution to ECCV 2022 challenge on Out-of-Vocabulary Scene Text Understanding (OOV-ST) : Cropped Word Recognition.
Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting
Our network consists of an image encoder and a character-aware text encoder that extract visual and textual features, respectively, as well as a visual-textual decoder that models the interaction among textual and visual features for learning effective scene text representations.
Optical Character Recognition
Optical Character Recognition (OCR)
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Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster
The vision evaluation is combined with a detailed full-stack wireless network simulation to determine the distribution of throughputs and delays with real navigation paths and ray-tracing from new high-resolution 3D models in an urban environment.
KS-GNN: Keywords Search over Incomplete Graphs via Graphs Neural Network
Keyword search is a fundamental task to retrieve information that is the most relevant to the query keywords.
3D Meta-Segmentation Neural Network
Based on the learned information of task distribution, our meta part segmentation learner is able to dynamically update the part segmentation learner with optimal parameters which enable our part segmentation learner to rapidly adapt and have great generalization ability on new part segmentation tasks.
Meta-Learning 3D Shape Segmentation Functions
We show in experiments that our meta-learning approach, denoted as Meta-3DSeg, leads to improvements on unsupervised 3D shape segmentation over the conventional designs of deep neural networks for 3D shape segmentation functions.
3D Unsupervised Region-Aware Registration Transformer
This paper concerns the research problem of point cloud registration to find the rigid transformation to optimally align the source point set with the target one.
3D Meta-Registration: Meta-learning 3D Point Cloud Registration Functions
Learning robust 3D point cloud registration functions with deep neural networks has emerged as a powerful paradigm in recent years, offering promising performance in producing spatial geometric transformations for each pair of 3D point clouds.
3D Meta-Registration: Learning to Learn Registration of 3D Point Clouds
In this paper, we propose a meta-learning based 3D registration model, named 3D Meta-Registration, that is capable of rapidly adapting and well generalizing to new 3D registration tasks for unseen 3D point clouds.
Inductive Link Prediction for Nodes Having Only Attribute Information
In attributed graphs, both the structure and attribute information can be utilized for link prediction.
Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks
Keywords: Multivariate Time Series, Change-point Detection, Graph Neural Networks
Exploiting Sentence Embedding for Medical Question Answering
The learning framework consists of two main parts: 1) a sentence embedding producing module, and 2) a scoring module.
TransG : A Generative Mixture Model for Knowledge Graph Embedding
Recently, knowledge graph embedding, which projects symbolic entities and relations into continuous vector space, has become a new, hot topic in artificial intelligence.
TransA: An Adaptive Approach for Knowledge Graph Embedding
Knowledge representation is a major topic in AI, and many studies attempt to represent entities and relations of knowledge base in a continuous vector space.
Knowlege Graph Embedding by Flexible Translation
TransF regards relation as translation between head entity vector and tail entity vector with flexible magnitude.
