Search Results for author:
Found 51 papers, 21 papers with code
DCT-Centered Temporal Relation Extraction
Most previous work on temporal relation extraction only focused on extracting the temporal relations among events or suffered from the issue of different expressions of events, timexes and Document Creation Time (DCT).
Ranked #1 on
Temporal Relation Classification
on TB-Dense
RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Model
RSBuilding is designed to enhance cross-scene generalization and task universality.
CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues
Accurate identification of protein nucleic-acid-binding residues poses a significant challenge with important implications for various biological processes and drug design.
ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing
De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research.
CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities
Event coreference resolution (ECR) aims to group event mentions referring to the same real-world event into clusters.
Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language Model
Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold, and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials.
Representation Disparity-aware Distillation for 3D Object Detection
In this paper, we focus on developing knowledge distillation (KD) for compact 3D detectors.
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs
One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS by taking advantage of the strengths of each in a unified framework, while searching the 1-bit CNNs is more challenging due to the more complicated processes involved.
Bi-ViT: Pushing the Limit of Vision Transformer Quantization
Vision transformers (ViTs) quantization offers a promising prospect to facilitate deploying large pre-trained networks on resource-limited devices.
AF2-Mutation: Adversarial Sequence Mutations against AlphaFold2 on Protein Tertiary Structure Prediction
Deep learning-based approaches, such as AlphaFold2 (AF2), have significantly advanced protein tertiary structure prediction, achieving results comparable to real biological experimental methods.
Q-DETR: An Efficient Low-Bit Quantized Detection Transformer
At the upper level, we introduce a new foreground-aware query matching scheme to effectively transfer the teacher information to distillation-desired features to minimize the conditional information entropy.
Implicit Diffusion Models for Continuous Super-Resolution
IDM integrates an implicit neural representation and a denoising diffusion model in a unified end-to-end framework, where the implicit neural representation is adopted in the decoding process to learn continuous-resolution representation.
Ranked #1 on
Image Super-Resolution
on CelebA-HQ 128x128
Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews
This framework is designed for general fossil identification and it is expected to see applications to other fossil datasets in future work.
Resilient Binary Neural Network
In this paper, we introduce a Resilient Binary Neural Network (ReBNN) to mitigate the frequent oscillation for better BNNs' training.
Target-Referenced Reactive Grasping for Dynamic Objects
Reactive grasping, which enables the robot to successfully grasp dynamic moving objects, is of great interest in robotics.
Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer
The large pre-trained vision transformers (ViTs) have demonstrated remarkable performance on various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-constrained devices.
Distance Map Supervised Landmark Localization for MR-TRUS Registration
We then use the predicted landmarks to generate the affine transformation matrix, which outperforms the clinicians' manual rigid registration by a significant margin in terms of TRE.
IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors
This explains why existing KD methods are less effective for 1-bit detectors, caused by a significant information discrepancy between the real-valued teacher and the 1-bit student.
Recurrent Bilinear Optimization for Binary Neural Networks
To address this issue, Recurrent Bilinear Optimization is proposed to improve the learning process of BNNs (RBONNs) by associating the intrinsic bilinear variables in the back propagation process.
Genome-wide nucleotide-resolution model of single-strand break site reveals species evolutionary hierarchy
Besides, SSBlazer is a lightweight model with robust cross-species generalization ability in the cross-species evaluation, which enables the large-scale genome-wide application in diverse species.
An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection
However, existing deep learning based models struggle to simultaneously achieve the requirements of both high precision and real-time performance.
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baseline
However, the majority of current grasping algorithms would fail in this case since they heavily rely on the depth image, while ordinary depth sensors usually fail to produce accurate depth information for transparent objects owing to the reflection and refraction of light.
Ranked #1 on
Transparent Object Depth Estimation
on TransCG
TerViT: An Efficient Ternary Vision Transformer
Vision transformers (ViTs) have demonstrated great potential in various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-constrained devices.
Neural Content Extraction for Poster Generation of Scientific Papers
Then we propose a three-step framework to tackle this task and focus on the content extraction step in this study.
POEM: 1-bit Point-wise Operations based on Expectation-Maximization for Efficient Point Cloud Processing
Real-time point cloud processing is fundamental for lots of computer vision tasks, while still challenged by the computational problem on resource-limited edge devices.
Collage: Seamless Integration of Deep Learning Backends with Automatic Placement
Given the fast-evolving nature of the DL ecosystem, this manual approach often slows down continuous innovations across different layers; it prevents hardware vendors from the fast deployment of their cutting-edge libraries, DL framework developers must repeatedly adjust their hand-coded rules to accommodate new versions of libraries, and machine learning practitioners need to wait for the integration of new technologies and often encounter unsatisfactory performance.
End-to-end Ultrasound Frame to Volume Registration
Fusing intra-operative 2D transrectal ultrasound (TRUS) image with pre-operative 3D magnetic resonance (MR) volume to guide prostate biopsy can significantly increase the yield.
Cross-modal Attention for MRI and Ultrasound Volume Registration
In the past few years, convolutional neural networks (CNNs) have been proved powerful in extracting image features crucial for image registration.
Layer-Wise Searching for 1-Bit Detectors
At each layer, it exploits a differentiable binarization search (DBS) to minimize the angular error in a student-teacher framework.
RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images
In the first stage, an encoder-decoder like convolutional neural network Angle-View Net(AVN) is proposed to predict the SO(3) orientation of the gripper at every location of the image.
FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data
Our scalable real-time forecasting system FlashP (Flash Prediction) is built based on this idea, with two major challenges to be resolved in this paper: first, we need to figure out how approximate aggregations affect the fitting of forecasting models, and forecasting results; and second, accordingly, what sampling algorithms we should use to obtain these approximate aggregations and how large the samples are.
A Neural Local Coherence Analysis Model for Clarity Text Scoring
Local coherence relation between two phrases/sentences such as cause-effect and contrast gives a strong influence of whether a text is well-structured or not.
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results.
Transducer Adaptive Ultrasound Volume Reconstruction
However, such algorithms are specific to particular transducers and scanning trajectories associated with the training data, which may not be generalized to other image acquisition settings.
Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS
We prove that the reproducing kernel Hilbert spaces (RKHS) of a deep neural tangent kernel and the Laplace kernel include the same set of functions, when both kernels are restricted to the sphere $\mathbb{S}^{d-1}$.
The 1st Tiny Object Detection Challenge:Methods and Results
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.
Two-Stage Maximum Score Estimator
We characterize the asymptotic distribution of the TSMS estimator, which features phase transitions depending on the dimension and thus the convergence rate of the first-stage estimation.
Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning
Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information.
Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs
We study estimation of a gradient-sparse parameter vector $\boldsymbol{\theta}^* \in \mathbb{R}^p$, having strong gradient-sparsity $s^*:=\|\nabla_G \boldsymbol{\theta}^*\|_0$ on an underlying graph $G$.
Logical Differencing in Dyadic Network Formation Models with Nontransferable Utilities
This paper considers a semiparametric model of dyadic network formation under nontransferable utilities (NTU).
Unified Multi-scale Feature Abstraction for Medical Image Segmentation
Automatic medical image segmentation, an essential component of medical image analysis, plays an importantrole in computer-aided diagnosis.
Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese
In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics.
Iterative Alpha Expansion for estimating gradient-sparse signals from linear measurements
We consider estimating a piecewise-constant image, or a gradient-sparse signal on a general graph, from noisy linear measurements.
Shubnikov-de Haas and de Haas-van Alphen oscillations in topological semimetal CaAl4
An electron-type quasi-2D Fermi surface is found by the angle-dependent Shubnikov-de Haas oscillations, de Haas-van Alphen oscillations and the first-principles calculations.
Materials Science Mesoscale and Nanoscale Physics
MCDTB: A Macro-level Chinese Discourse TreeBank
In view of the differences between the annotations of micro and macro discourse rela-tionships, this paper describes the relevant experiments on the construction of the Macro Chinese Discourse Treebank (MCDTB), a higher-level Chinese discourse corpus.
Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse
The task of nuclearity recognition in Chinese discourse remains challenging due to the demand for more deep semantic information.
Learning Deep Similarity Metric for 3D MR-TRUS Registration
Conclusion: A similarity metric that is learned using a deep neural network can be used to assess the quality of any given image registration and can be used in conjunction with the aforementioned optimization framework to perform automatic registration that is robust to poor initialization.
Adversarial Image Registration with Application for MR and TRUS Image Fusion
Robust and accurate alignment of multimodal medical images is a very challenging task, which however is very useful for many clinical applications.
An optimal hierarchical clustering approach to segmentation of mobile LiDAR point clouds
The main contribution of this paper is that we succeed to optimize the combination of clusters in the hierarchical clustering.
Road Curb Extraction from Mobile LiDAR Point Clouds
Automatic extraction of road curbs from uneven, unorganized, noisy and massive 3D point clouds is a challenging task.
