Search Results for author:
Found 27 papers, 8 papers with code
TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network
To alleviate label scarcity in Named Entity Recognition (NER) task, distantly supervised NER methods are widely applied to automatically label data and identify entities.
Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes
For visual representation, a representation driven by a combination of the imageaugmented forward dynamics and the reward is acquired.
Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media
Building models to detect vaccine attitudes on social media is challenging because of the composite, often intricate aspects involved, and the limited availability of annotated data.
3D Object Tracking with Transformer
By using cross-attention, the transformer decoder fuses features and includes more target cues into the current point cloud feature to compute the region attentions, which makes the similarity computing more efficient.
Unrestricted Adversarial Attacks on ImageNet Competition
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input.
PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds
Our code is open-sourced for the robotics community at https://github. com/shanjiayao/PTT.
3D-SiamRPN: An End-to-End Learning Method for Real-Time 3D Single Object Tracking Using Raw Point Cloud
Then, to fuse the information of features in the two branches and obtain their similarity, we propose two cross correlation modules, named Pointcloud-wise and Point-wise respectively.
Implementing an Improved Test of Matrix Rank in Stata
We develop a Stata command, bootranktest, for implementing the matrix rank test of Chen and Fang (2019) in linear instrumental variable regression models.
Deep Differential Amplifier for Extractive Summarization
In this paper, we conceptualize the single-document extractive summarization as a rebalance problem and present a deep differential amplifier framework.
A Unifying Framework for Testing Shape Restrictions
This paper makes the following original contributions.
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
However, the representation ability of such models is limited due to: (1) shallow GNNs are incapable to capture long-range spatial correlations, (2) only spatial connections are considered and a mass of semantic connections are ignored, which are of great importance for a comprehensive understanding of traffic networks.
Ranked #3 on
Traffic Prediction
on PeMS07
NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection
Non-Maximum Suppression (NMS) is essential for object detection and affects the evaluation results by incorporating False Positives (FP) and False Negatives (FN), especially in crowd occlusion scenes.
Ranked #4 on
Pedestrian Detection
on Caltech
A Query-Driven Topic Model
Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus.
Improved Signed Distance Function for 2D Real-time SLAM and Accurate Localization
In this paper, we propose an improved Signed Distance Function (SDF) for both 2D SLAM and pure localization to improve the accuracy of mapping and localization.
TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting
Further analysis indicates that the locality and globality of the traffic networks are critical to traffic flow prediction and the proposed TSSRGCN model can adapt to the various temporal traffic patterns.
Event-VPR: End-to-End Weakly Supervised Network Architecture for Event-based Visual Place Recognition
In this paper, we propose an end-to-end visual place recognition network for event cameras, which can achieve good place recognition performance in challenging environments.
Inference for Large-Scale Linear Systems with Known Coefficients
This paper considers the problem of testing whether there exists a non-negative solution to a possibly under-determined system of linear equations with known coefficients.
Deep Patch-based Human Segmentation
3D human segmentation has seen noticeable progress in re-cent years.
OpenArray v1.0: a simple operator library for the decoupling of ocean modeling and parallel computing
In this work, we design a simple computing library to bridge the gap and decouple the work of ocean modeling from parallel computing.
A Projection Framework for Testing Shape Restrictions That Form Convex Cones
Based on a monotonicity property afforded by such a geometric structure, we construct a bootstrap procedure that, unlike many studies in nonstandard settings, dispenses with estimation of local parameter spaces, and the critical values are obtained in a way as simple as computing the test statistic.
Precision annealing Monte Carlo methods for statistical data assimilation and machine learning
In this paper, we develop a systematic formulation based on Monte Carlo sampling to achieve such information transfer.
DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image
Recovering 3D human body shape and pose from 2D images is a challenging task due to high complexity and flexibility of human body, and relatively less 3D labeled data.
Machine Learning of Time Series Using Time-delay Embedding and Precision Annealing
We proceed from a scalar time series $s(t_n); t_n = t_0 + n \Delta t$ and using methods of nonlinear time series analysis show how to produce a $D_E > 1$ dimensional time delay embedding space in which the time series has no false neighbors as does the observed $s(t_n)$ time series.
Joint Entity Linking with Deep Reinforcement Learning
Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base.
Parallel and Scalable Heat Methods for Geodesic Distance Computation
In this paper, we propose a parallel and scalable approach for geodesic distance computation on triangle meshes.
Graphics
RGB-D SLAM in Dynamic Environments Using Point Correlations
This method utilizes the correlation between map points to separate points that are part of the static scene and points that are part of different moving objects into different groups.
Efficient Image Categorization with Sparse Fisher Vector
In object recognition, Fisher vector (FV) representation is one of the state-of-art image representations ways at the expense of dense, high dimensional features and increased computation time.
