Search Results for author:
Found 39 papers, 11 papers with code
Semi-supervised Semantic Segmentation via Strong-weak Dual-branch Network
To fully explore the potential of the weak labels, we propose to impose separate treatments of strong and weak annotations via a strong-weak dual-branch network, which discriminates the massive inaccurate weak supervisions from those strong ones.
PacGDC: Label-Efficient Generalizable Depth Completion with Projection Ambiguity and Consistency
This paper presents PacGDC, a label-efficient technique that enhances data diversity with minimal annotation effort for generalizable depth completion.
Real-World Depth Recovery via Structure Uncertainty Modeling and Inaccurate GT Depth Fitting
Furthermore, a structure uncertainty module is designed to explicitly identify the misaligned structure for input raw depth maps to better generalize in unseen scenarios.
Multimodal Image Matching based on Frequency-domain Information of Local Energy Response
The core of FILER is the local energy response model based on frequency-domain information, which can overcome the effect of nonlinear intensity differences.
Variable-frame CNNLSTM for Breast Nodule Classification using Ultrasound Videos
These findings validate the effectiveness of our approach in classifying variable-frame ultrasound videos and suggest potential applications in other medical imaging modalities.
PUGS: Zero-shot Physical Understanding with Gaussian Splatting
We propose a new method that reconstructs 3D objects using the Gaussian splatting representation and predicts various physical properties in a zero-shot manner.
SEC-Prompt:SEmantic Complementary Prompting for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) presents a significant challenge in machine learning, requiring models to integrate new classes from limited examples while preserving performance on previously learned classes.
New Emerged Security and Privacy of Pre-trained Model: a Survey and Outlook
With the taxonomy analysis, we capture the unique security and privacy issues of pre-trained models, categorizing and summarizing existing security issues based on their characteristics.
Scale Propagation Network for Generalizable Depth Completion
Although deep learning based methods have made tremendous progress in this problem, these models cannot generalize well across different scenes that are unobserved in training, posing a fundamental limitation that yet to be overcome.
SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching
Automated nodule segmentation is essential for computer-assisted diagnosis in ultrasound images.
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various application scenarios.
Ranked #2 on
Speech Recognition
on AISHELL-1
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation
Especially, an asymmetric learning framework is developed by extending the aspect ratio annotations with two types of pseudo labels, i. e., conservative labels and radical labels, to train two asymmetric segmentation networks simultaneously.
CTC Blank Triggered Dynamic Layer-Skipping for Efficient CTC-based Speech Recognition
Deploying end-to-end speech recognition models with limited computing resources remains challenging, despite their impressive performance.
G2-MonoDepth: A General Framework of Generalized Depth Inference from Monocular RGB+X Data
This paper investigates a unified task of monocular depth inference, which infers high-quality depth maps from all kinds of input raw data from various robots in unseen scenes.
Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification
To make full use of pixel-level and cell-level features dynamically, we propose an asymmetric co-training framework combining a deep graph convolutional network and a convolutional neural network for multi-class histopathological image classification.
FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene
It has long been an ill-posed problem to predict absolute depth maps from single images in real (unseen) indoor scenes.
Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator
Many researchers have focused on designing new forms of self-attention or introducing new parameters to overcome this limitation, however a large portion of them prohibits the model to inherit weights from large pretrained models.
Ranked #1 on
Open-Domain Question Answering
on ELI5
Is Writing Prompts Really Making Art?
In recent years Generative Machine Learning systems have advanced significantly.
Key-frame Guided Network for Thyroid Nodule Recognition using Ultrasound Videos
Ultrasound examination is widely used in the clinical diagnosis of thyroid nodules (benign/malignant).
Semi-Supervised Few-Shot Learning via Multi-Factor Clustering
And we exploit the pseudo labels of unlabeled samples by MFC to expand the support set for obtaining more distribution information.
Unsupervised Domain Adaptation for the Histopathological Cell Segmentation through Self-Ensembling
Meanwhile, we present a self-ensembling model to consider the source and the target domain together as a semi-supervised segmentation task to reduce the differences of outputs.
Breadth First Reasoning Graph for Multi-hop Question Answering
Recently Graph Neural Network (GNN) has been used as a promising tool in multi-hop question answering task.
Leaning Compact and Representative Features for Cross-Modality Person Re-Identification
This paper pays close attention to the cross-modality visible-infrared person re-identification (VI Re-ID) task, which aims to match pedestrian samples between visible and infrared modes.
Cross-Modality Person Re-identification
Knowledge Distillation
+2
Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition
(i) To learn more robust and discriminative features, we desire to adaptively fuse the contextual features from different layers.
Integrating Pre-trained Model into Rule-based Dialogue Management
Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems for their interpretablility.
HpGAN: Sequence Search with Generative Adversarial Networks
Sequences play an important role in many engineering applications and systems.
A Shared-Private Representation Model with Coarse-to-Fine Extraction for Target Sentiment Analysis
However, the relation between the target extraction task and the target classification task has not been well exploited.
Pedestrian re-identification based on Tree branch network with local and global learning
Deep part-based methods in recent literature have revealed the great potential of learning local part-level representation for pedestrian image in the task of person re-identification.
The Data Analysis Pipeline for the SDSS-IV MaNGA IFU Galaxy Survey: Overview
We summarize assessments of the precision and accuracy of our measurements as a function of signal-to-noise and other salient metrics, noting that additional analysis and discussion of emission-line diagnostics are provided in our companion paper, Belfiore et al.
Astrophysics of Galaxies
Toward Characteristic-Preserving Image-based Virtual Try-On Network
Second, to alleviate boundary artifacts of warped clothes and make the results more realistic, we employ a Try-On Module that learns a composition mask to integrate the warped clothes and the rendered image to ensure smoothness.
An MM Algorithm for Split Feasibility Problems
Furthermore, we show that the Euclidean norm appearing in the proximity function of the non-linear split feasibility problem can be replaced by arbitrary Bregman divergences.
Large-Margin Softmax Loss for Convolutional Neural Networks
Cross-entropy loss together with softmax is arguably one of the most common used supervision components in convolutional neural networks (CNNs).
Robust Elastic Net Regression
We propose a robust elastic net (REN) model for high-dimensional sparse regression and give its performance guarantees (both the statistical error bound and the optimization bound).
Jointly Learning Non-negative Projection and Dictionary with Discriminative Graph Constraints for Classification
Sparse coding with dictionary learning (DL) has shown excellent classification performance.
Efficient Face Alignment via Locality-constrained Representation for Robust Recognition
Practical face recognition has been studied in the past decades, but still remains an open challenge.
Structured Occlusion Coding for Robust Face Recognition
We propose the structured occlusion coding (SOC) to address occlusion problems.
Latent Dictionary Learning for Sparse Representation based Classification
Each dictionary atom is jointly learned with a latent vector, which associates this atom to the representation of different classes.
Collaborative Representation based Classification for Face Recognition
It is widely believed that the l1- norm sparsity constraint on coding coefficients plays a key role in the success of SRC, while its use of all training samples to collaboratively represent the query sample is rather ignored.
Regularized Robust Coding for Face Recognition
Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR).
