Search Results for author:
Found 29 papers, 7 papers with code
Multimodal Sarcasm Target Identification in Tweets
Sarcasm is important to sentiment analysis on social media.
Indeterminate Probability Neural Network
Besides, for our proposed neural network framework, the output of neural network is defined as probability events, and based on the statistical analysis of these events, the inference model for classification task is deduced.
PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation
Conversational Causal Emotion Entailment (C2E2) is a task that aims at recognizing the causes corresponding to a target emotion in a conversation.
Prior land surface reflectance-based sandstorm detection from space using deep learning
Based on the dataset, the difference between the reflectance observed by the satellite and the corresponding LSR is generated, which is used as a characteristic parameter of sandstorm detection with the deep learning method.
Using EBGAN for Anomaly Intrusion Detection
The generator in IDS-EBGAN is responsible for converting the original malicious network traffic in the training set into adversarial malicious examples.
VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention
These multi-view methods either refine the proposals predicted from single view via fused features, or fuse the features without considering the global spatial context; their performance is limited consequently.
RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NER
We integrate soft or hard gates to select visual clues and propose a multitask algorithm to train on the MNER datasets.
Ranked #5 on
Multi-modal Named Entity Recognition
on Twitter-15
Multi-modal Named Entity Recognition
named-entity-recognition
+2
Learning Category-level Shape Saliency via Deep Implicit Surface Networks
Given the large shape variations among different instances of a same category, we are formally interested in developing a quantity defined for individual points on a continuous object surface; the quantity specifies how individual surface points contribute to the formation of the shape as the category.
RIVA: A Pre-trained Tweet Multimodal Model Based on Text-image Relation for Multimodal NER
In the multimodal NER task, the experimental results show the significance of text-related visual features for the visual-linguistic model and our approach achieves SOTA performance on the MNER datasets.
Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization
We proposed a pair of cross-view transformers to transform the feature maps into the other view and introduce cross-view consistency loss on them.
Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps
To test GPNet, we contribute a synthetic dataset of 6-DOF object grasps; evaluation is conducted using rule-based criteria, simulation test, and real test.
Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation
We propose a simple, easy-to-optimize distillation method for learning the conditional predictive distribution of a pre-trained dropout model for fast, sample-free uncertainty estimation in computer vision tasks.
Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles
As for vehicles, their trajectories are significantly influenced by the lane geometry and how to effectively use the lane information is of active interest.
HRDNet: High-resolution Detection Network for Small Objects
By extracting various features from high to low resolutions, the MD-IPN is able to improve the performance of small object detection as well as maintaining the performance of middle and large objects.
Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots
Accurate 3D object detection in LiDAR based point clouds suffers from the challenges of data sparsity and irregularities.
Ranked #3 on
3D Object Detection
on KITTI Pedestrians Moderate
IPG-Net: Image Pyramid Guidance Network for Small Object Detection
In this paper, except for top-down combining of information for shallow layers, we propose a novel network called Image Pyramid Guidance Network (IPG-Net) to make sure both the spatial information and semantic information are abundant for each layer.
Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning
In sponsored search, keyword recommendations help advertisers to achieve much better performance within limited budget.
TOI-CNN: a Solution of Information Extraction on Chinese Insurance Policy
The advantage of TOI pooling layer is that the nested elements from one sentence could share computation and context in the forward and backward passes.
Coupled Recurrent Network (CRN)
To learn from these heterogenous input sources, existing methods reply on two-stream architectural designs that contain independent, parallel streams of Recurrent Neural Networks (RNNs).
Action Recognition In Videos
Multi-Person Pose Estimation
+2
Lattice Long Short-Term Memory for Human Action Recognition
This method effectively enhances the ability to model dynamics across time and addresses the non-stationary issue of long-term motion dynamics without significantly increasing the model complexity.
Fine-Grained Categorization via CNN-Based Automatic Extraction and Integration of Object-Level and Part-Level Features
Fine-grained categorization can benefit from part-based features which reveal subtle visual differences between object categories.
Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning
We investigate different levels and types of supervision (learning from linguistic examples vs. learning from a given set of metaphorical mappings vs. learning without annotation) in flat and hierarchical, unconstrained and constrained clustering settings.
Feedback Networks
Currently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer.
Human Action Recognition using Factorized Spatio-Temporal Convolutional Networks
Human actions in video sequences are three-dimensional (3D) spatio-temporal signals characterizing both the visual appearance and motion dynamics of the involved humans and objects.
DL-SFA: Deeply-Learned Slow Feature Analysis for Action Recognition
In this paper, we propose to combine SFA with deep learning techniques to learn hierarchical representations from the video data itself.
Native Language Identification Using Large, Longitudinal Data
Native Language Identification (NLI) is a task aimed at determining the native language (L1) of learners of second language (L2) on the basis of their written texts.
BIG-bench Machine Learning
Native Language Identification
+1
