Search Results for author:
Found 22 papers, 4 papers with code
Adaptive Threshold Selective Self-Attention for Chinese NER
Recently, Transformer has achieved great success in Chinese named entity recognition (NER) owing to its good parallelism and ability to model long-range dependencies, which utilizes self-attention to encode context.
Chinese Named Entity Recognition
named-entity-recognition
+2
RSGT: Relational Structure Guided Temporal Relation Extraction
In this paper, we propose RSGT: Relational Structure Guided Temporal Relation Extraction to extract the relational structure features that can fit for both inter-sentence and intra-sentence relations.
Ranked #1 on
Temporal Relation Classification
on MATRES
AbsGS: Recovering Fine Details for 3D Gaussian Splatting
In this work, we present a comprehensive analysis of the cause of aforementioned artifacts, namely gradient collision, which prevents large Gaussians in over-reconstructed regions from splitting.
Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Prediction
In the inductive setting where test TKGs contain emerging entities, the latest methods are based on symbolic rules or pre-trained language models (PLMs).
Towards Vision Transformer Unrolling Fixed-Point Algorithm: a Case Study on Image Restoration
It provides a promising way to introduce the Transformer in low-level vision tasks.
IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verification
Then we integrate the multi-view contextual information to encode the evidence sentences to handle the task.
Multi-Outputs Is All You Need For Deblur
Therefore, we propose to make the network learn the distribution of feasible solutions, and design based on this consideration a novel multi-head output architecture and corresponding loss function for distribution learning.
Ranked #5 on
Image Deblurring
on GoPro
SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples
To alleviate feature suppression, we propose contrastive learning for unsupervised sentence embedding with soft negative samples (SNCSE).
Argumentation Mining on Essays at Multi Scales
Recent research mainly models the task as a sequence tagging problem and deal with all the argumentation components at word level.
Fixed-size Objects Encoding for Visual Relationship Detection
Instead, we propose a novel method to encode all background objects in each image by using one fixed-size vector (i. e., FBE vector).
Towards Precise End-to-end Weakly Supervised Object Detection Network
It is challenging for weakly supervised object detection network to precisely predict the positions of the objects, since there are no instance-level category annotations.
Heavy-ball Algorithms Always Escape Saddle Points
In this paper, we answer a question: can the nonconvex heavy-ball algorithms with random initialization avoid saddle points?
Visual Confusion Label Tree For Image Classification
In order to optimize models and meet the requirement mentioned above, we propose a method that replaces the fully-connected layers of convolution neural network models with a tree classifier.
Visual Tree Convolutional Neural Network in Image Classification
In image classification, Convolutional Neural Network(CNN) models have achieved high performance with the rapid development in deep learning.
IF-TTN: Information Fused Temporal Transformation Network for Video Action Recognition
Focusing on discriminate spatiotemporal feature learning, we propose Information Fused Temporal Transformation Network (IF-TTN) for action recognition on top of popular Temporal Segment Network (TSN) framework.
Exploring Frame Segmentation Networks for Temporal Action Localization
The proposed FSN can make dense predictions at frame-level for a video clip using both spatial and temporal context information.
DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks
Most of previous methods mainly consider to drop features from input data and hidden layers, such as Dropout, Cutout and DropBlocks.
Learning Generic Diffusion Processes for Image Restoration
On the contrary, the regularization term learned via discriminative approaches are usually trained for a specific image restoration problem, and fail in the problem for which it is not trained.
Exploring Temporal Preservation Networks for Precise Temporal Action Localization
A newly proposed work exploits Convolutional-Deconvolutional-Convolutional (CDC) filters to upsample the predictions of 3D ConvNets, making it possible to perform per-frame action predictions and achieving promising performance in terms of temporal action localization.
Open-Ended Question Answering
Temporal Action Localization
+1
Learning Non-local Image Diffusion for Image Denoising
In order to preserve the expected property that end-to-end training is available, we exploit the NSS prior by a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising.
Weakly supervised object detection using pseudo-strong labels
Object detection is an import task of computer vision. A variety of methods have been proposed, but methods using the weak labels still do not have a satisfactory result. In this paper, we propose a new framework that using the weakly supervised method's output as the pseudo-strong labels to train a strongly supervised model. One weakly supervised method is treated as black-box to generate class-specific bounding boxes on train dataset. A de-noise method is then applied to the noisy bounding boxes. Then the de-noised pseudo-strong labels are used to train a strongly object detection network. The whole framework is still weakly supervised because the entire process only uses the image-level labels. The experiment results on PASCAL VOC 2007 prove the validity of our framework, and we get result 43. 4% on mean average precision compared to 39. 5% of the previous best result and 34. 5% of the initial method, respectively. And this frame work is simple and distinct, and is promising to be applied to other method easily.
Relative distance features for gait recognition with Kinect
This study focuses on human recognition with gait feature obtained by Kinect and shows that gait feature can effectively distinguish from different human beings through a novel representation -- relative distance-based gait features.
