Search Results for author:
Found 19 papers, 7 papers with code
JOADAA: joint online action detection and action anticipation
By combining action anticipation and online action detection, our approach can cover the missing dependencies of future information in online action detection.
Dual Effects of the US-China Trade War and COVID-19 on United States Imports: Transfer of China's industrial chain?
However, the worldwide lockdowns over pandemic have reversed this trend as, over this period, the U. S. effectively imported more goods directly from China and indirectly through Southeast Asian exporters that imported from China.
Moderately Distributional Exploration for Domain Generalization
We show that MODE can endow models with provable generalization performance on unknown target domains.
Company Competition Graph
Based on the output from our system, we construct a knowledge graph with more than 700 nodes and 1200 edges.
Form 10-K Itemization
With the segmented Item sections, NLP techniques can directly apply on those Item sections related to downstream tasks.
Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection
Video anomaly detection in surveillance systems with only video-level labels (i. e. weakly-supervised) is challenging.
THORN: Temporal Human-Object Relation Network for Action Recognition
In this work, we present an end-to-end network: THORN, that can leverage important human-object and object-object interactions to predict actions.
MS-TCT: Multi-Scale Temporal ConvTransformer for Action Detection
Action detection is an essential and challenging task, especially for densely labelled datasets of untrimmed videos.
Ranked #2 on
Action Detection
on TSU
CTRN: Class-Temporal Relational Network for Action Detection
Action detection is an essential and challenging task, especially for densely labelled datasets of untrimmed videos.
Ranked #2 on
Action Detection
on Multi-THUMOS
Learning an Augmented RGB Representation with Cross-Modal Knowledge Distillation for Action Detection
On the other hand, sequence-level distillation encourages the student to learn the temporal knowledge from the teacher, which consists of transferring the Global Contextual Relations and the Action Boundary Saliency.
VPN++: Rethinking Video-Pose embeddings for understanding Activities of Daily Living
But the cost of computing 3D poses from RGB stream is high in the absence of appropriate sensors.
Ranked #9 on
Action Recognition
on NTU RGB+D 120
(using extra training data)
Form 10-Q Itemization
The quarterly financial statement, or Form 10-Q, is one of the most frequently required filings for US public companies to disclose financial and other important business information.
PDAN: Pyramid Dilated Attention Network for Action Detection
Previous action detection methods fail in selecting the key temporal information in long videos.
Ranked #1 on
Action Detection
on TSU
Selective Spatio-Temporal Aggregation Based Pose Refinement System: Towards Understanding Human Activities in Real-World Videos
Taking advantage of human pose data for understanding human activities has attracted much attention these days.
Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection
Therefore, we propose a new baseline method for activity detection to tackle the novel challenges provided by our dataset.
VPN: Learning Video-Pose Embedding for Activities of Daily Living
The 2 key components of this VPN are a spatial embedding and an attention network.
Ranked #6 on
Action Classification
on Toyota Smarthome dataset
(using extra training data)
Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data
To address this issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network (H-STGCN), which is able to "deduce" future travel time by exploiting the data of upcoming traffic volume.
Convolutional Gated Recurrent Units for Medical Relation Classification
Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for relation classification.
Classifying medical relations in clinical text via convolutional neural networks
Deep learning research on relation classification has achieved solid performance in the general domain.
