Search Results for author:
Found 31 papers, 14 papers with code
The intelligent prediction and assessment of financial information risk in the cloud computing model
Cloud computing (cloud computing) is a kind of distributed computing, referring to the network "cloud" will be a huge data calculation and processing program into countless small programs, and then, through the system composed of multiple servers to process and analyze these small programs to get the results and return to the user.
Implementation of an AI-based MRD evaluation and prediction model for multiple myeloma
With the application of hematopoietic stem cell transplantation and new drugs, the progression-free survival rate and overall survival rate of multiple myeloma have been greatly improved, but it is still considered as a kind of disease that cannot be completely cured.
Towards Accurate Post-training Quantization for Reparameterized Models
Our framework outperforms previous methods by approximately 1\% for 8-bit PTQ and 2\% for 6-bit PTQ, showcasing its superior performance.
EL-VIT: Probing Vision Transformer with Interactive Visualization
Therefore, a visualization system is needed to assist ViT users in understanding its functionality.
Continual Learning for Image Segmentation with Dynamic Query
Image segmentation based on continual learning exhibits a critical drop of performance, mainly due to catastrophic forgetting and background shift, as they are required to incorporate new classes continually.
Improving Vision-and-Language Reasoning via Spatial Relations Modeling
Further, we design two pre-training tasks named object position regression (OPR) and spatial relation classification (SRC) to learn to reconstruct the spatial relation graph respectively.
Dual Grained Quantization: Efficient Fine-Grained Quantization for LLM
Fine-grained quantization has smaller quantization loss, consequently achieving superior performance.
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models
In this paper, we introduce a data-free and parameter-efficient fine-tuning framework for low-bit diffusion models, dubbed EfficientDM, to achieve QAT-level performance with PTQ-like efficiency.
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation.
A Surrogate Data Assimilation Model for the Estimation of Dynamical System in a Limited Area
We propose a novel learning-based surrogate data assimilation (DA) model for efficient state estimation in a limited area.
DMNER: Biomedical Entity Recognition by Detection and Matching
Biomedical named entity recognition (BNER) serves as the foundation for numerous biomedical text mining tasks.
A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension
Most existing cross-modal language-to-video retrieval (VR) research focuses on single-modal input from video, i. e., visual representation, while the text is omnipresent in human environments and frequently critical to understand video.
Experimental Design for Any $p$-Norm
We prove that a randomized local search approach provides a unified algorithm to solve this problem for all $p$.
DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models
In contrast, synthetic data can be freely available using a generative model (e. g., DALL-E, Stable Diffusion).
BiViT: Extremely Compressed Binary Vision Transformers
To solve this, we propose Softmax-aware Binarization, which dynamically adapts to the data distribution and reduces the error caused by binarization.
GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state
In this paper, we propose GoSum, a novel graph and reinforcement learning based extractive model for long-paper summarization.
Ranked #4 on
Text Summarization
on Pubmed
BiViT: Extremely Compressed Binary Vision Transformer
To solve this, we propose Softmax-aware Binarization, which dynamically adapts to the data distribution and reduces the error caused by binarization.
Real-time End-to-End Video Text Spotter with Contrastive Representation Learning
Our contributions are three-fold: 1) CoText simultaneously address the three tasks (e. g., text detection, tracking, recognition) in a real-time end-to-end trainable framework.
Binarizing by Classification: Is soft function really necessary?
Extensive experiments demonstrate that the proposed method yields surprising performance both in image classification and human pose estimation tasks.
Ranked #1 on
Binarization
on ImageNet
(Top 1 Accuracy metric)
Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization
In this paper, we present a simple yet effective data-free quantization method with accurate activation clipping and adaptive batch normalization.
End-to-End Video Text Spotting with Transformer
Recent video text spotting methods usually require the three-staged pipeline, i. e., detecting text in individual images, recognizing localized text, tracking text streams with post-processing to generate final results.
The Observability in Unobservable Systems
In this paper, we introduce the concept of observability of targeted state variables for systems that may not be fully observable.
Contrastive Learning of Semantic and Visual Representations for Text Tracking
Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video.
A Bilingual, OpenWorld Video Text Dataset and End-to-end Video Text Spotter with Transformer
Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data.
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection
Surprisingly, by varying the granularity of division on feature maps, we are able to modulate the reconstruction capability of the model for both normal and abnormal samples.
Polygon-free: Unconstrained Scene Text Detection with Box Annotations
For example, without using polygon annotations, PSENet achieves an 80. 5% F-score on TotalText [3] (vs. 80. 9% of fully supervised counterpart), 31. 1% better than training directly with upright bounding box annotations, and saves 80%+ labeling costs.
A Local Search Framework for Experimental Design
We present a local search framework to design and analyze both combinatorial algorithms and rounding algorithms for experimental design problems.
TextCohesion: Detecting Text for Arbitrary Shapes
In this paper, we propose a pixel-wise method named TextCohesion for scene text detection, which splits a text instance into five key components: a Text Skeleton and four Directional Pixel Regions.
Ranked #1 on
Curved Text Detection
on SCUT-CTW1500
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss
Since the proposal of fully convolutional neural network (FCNN), it has been widely used in semantic segmentation because of its high accuracy of pixel-wise classification as well as high precision of localization.
An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network
The core of our method is to extract features of images based on convolutional neural network and achieve classification by classifier.
