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Found 18 papers, 2 papers with code
HW-TSC’s Participation at WMT 2021 Quality Estimation Shared Task
This paper presents our work in WMT 2021 Quality Estimation (QE) Shared Task.
Brain-inspired sparse training enables Transformers and LLMs to perform as fully connected
The Cannistraci-Hebb training (CHT) is a brain-inspired method for growing connectivity in DST.
Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models
The rapid growth of Large Language Models has driven demand for effective model compression techniques to reduce memory and computation costs.
DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs
In this paper, we introduce DuQuant, a novel approach that utilizes rotation and permutation transformations to more effectively mitigate both massive and normal outliers.
Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks
The growing computational demands posed by increasingly number of neural network's parameters necessitate low-memory-consumption training approaches.
ColoristaNet for Photorealistic Video Style Transfer
The style removal network removes the original image styles, and the style restoration network recovers image styles in a supervised manner.
The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation
This paper describes our work in participation of the IWSLT-2021 offline speech translation task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Breast Anatomy Enriched Tumor Saliency Estimation
Then we refine the layers by integrating a non-semantic breast anatomy model to solve the problems of incomplete mammary layers.
CrackGAN: Pavement Crack Detection Using Partially Accurate Ground Truths Based on Generative Adversarial Learning
Fully convolutional network is a powerful tool for per-pixel semantic segmentation/detection.
Fuzzy Semantic Segmentation of Breast Ultrasound Image with Breast Anatomy Constraints
Due to low cost, portable, no radiation, and high efficiency, breast ultrasound (BUS) imaging is the most popular approach for diagnosing early breast cancer.
Tumor Saliency Estimation for Breast Ultrasound Images via Breast Anatomy Modeling
First, we model breast anatomy and decompose breast ultrasound image into layers using Neutro-Connectedness; then utilize the layers to generate the foreground and background maps; and finally propose a novel objective function to estimate the tumor saliency by integrating the foreground map, background map, adaptive center bias, and region-based correlation cues.
A Hybrid Framework for Tumor Saliency Estimation
Automatic tumor segmentation of breast ultrasound (BUS) image is quite challenging due to the complicated anatomic structure of breast and poor image quality.
Abstaining Classification When Error Costs are Unequal and Unknown
BA2 aims to obtain the optimal area under the ROC curve (AUC) by constraining the reject rates of the positive and negative classes respectively.
Computer-Aided Knee Joint Magnetic Resonance Image Segmentation - A Survey
In this survey paper, we classify the existing methods by their principles and discuss the current research status and point out the future research trend in-depth.
BUSIS: A Benchmark for Breast Ultrasound Image Segmentation
Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Comput-er-Aided Diagnosis (CAD) systems.
Automatic Breast Ultrasound Image Segmentation: A Survey
Breast cancer is one of the leading causes of cancer death among women worldwide.
Neutro-Connectedness Cut
Interactive image segmentation is a challenging task and receives increasing attention recently; however, two major drawbacks exist in interactive segmentation approaches.
An algorithm for Left Atrial Thrombi detection using Transesophageal Echocardiography
And the dynamic features are formed by using the information of its neighbor frames in the sequence.
