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Found 24 papers, 11 papers with code
Towards Unsupervised Eye-Region Segmentation for Eye Tracking
To achieve accurate parsing of the eye-region, we first leverage the pretrained foundation model Segment Anything (SAM) in an automatic way to refine the eye indications.
CondSeg: Ellipse Estimation of Pupil and Iris via Conditioned Segmentation
Parsing of eye components (i. e. pupil, iris and sclera) is fundamental for eye tracking and gaze estimation for AR/VR products.
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
For data scaling, we introduce a Warmup-Stable-Decay (WSD) learning rate scheduler (LRS), conducive to continuous training and domain adaptation.
MTS-DVGAN: Anomaly Detection in Cyber-Physical Systems using a Dual Variational Generative Adversarial Network
Then, by exploiting the distribution property and modeling the normal patterns of multivariate time series, a variational autoencoder is introduced to force the generative adversarial network (GAN) to generate diverse samples.
IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System
FE-Block module performs fine-grained and early feature interactions to capture the interactive signals between user and item towers explicitly and CIR module leverages a contrastive interaction regularization to further enhance the interactions implicitly.
U-HRNet: Delving into Improving Semantic Representation of High Resolution Network for Dense Prediction
Therefore, we designed a U-shaped High-Resolution Network (U-HRNet), which adds more stages after the feature map with strongest semantic representation and relaxes the constraint in HRNet that all resolutions need to be calculated parallel for a newly added stage.
Neural-FacTOR: Neural Representation Learning for Website Fingerprinting Attack over TOR Anonymity
TOR (The Onion Router) network is a widely used open source anonymous communication tool, the abuse of TOR makes it difficult to monitor the proliferation of online crimes such as to access criminal websites.
Low Resource Style Transfer via Domain Adaptive Meta Learning
Text style transfer (TST) without parallel data has achieved some practical success.
Dressing in the Wild by Watching Dance Videos
While significant progress has been made in garment transfer, one of the most applicable directions of human-centric image generation, existing works overlook the in-the-wild imagery, presenting severe garment-person misalignment as well as noticeable degradation in fine texture details.
PP-YOLOv2: A Practical Object Detector
To meet these two concerns, we comprehensively evaluate a collection of existing refinements to improve the performance of PP-YOLO while almost keep the infer time unchanged.
Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English
In this paper, we describe our system for the AAAI 2021 shared task of COVID-19 Fake News Detection in English, where we achieved the 3rd position with the weighted F1 score of 0. 9859 on the test set.
Ranked #1 on
Fake News Detection
on Grover-Mega
RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning
We study unsupervised video representation learning that seeks to learn both motion and appearance features from unlabeled video only, which can be reused for downstream tasks such as action recognition.
Ranked #11 on
Self-Supervised Action Recognition
on UCF101
PP-YOLO: An Effective and Efficient Implementation of Object Detector
We mainly try to combine various existing tricks that almost not increase the number of model parameters and FLOPs, to achieve the goal of improving the accuracy of detector as much as possible while ensuring that the speed is almost unchanged.
Graph-PCNN: Two Stage Human Pose Estimation with Graph Pose Refinement
In the first stage, heatmap regression network is applied to obtain a rough localization result, and a set of proposal keypoints, called guided points, are sampled.
FenceMask: A Data Augmentation Approach for Pre-extracted Image Features
It is based on the 'simulation of object occlusion' strategy, which aim to achieve the balance between object occlusion and information retention of the input data.
Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification
In order to overcome these challenges, we propose to use cross-modality attention with semantic graph embedding for multi label classification.
Ranked #8 on
Multi-Label Classification
on NUS-WIDE
Multi-Label Classification with Label Graph Superimposing
In this paper, we propose a label graph superimposing framework to improve the conventional GCN+CNN framework developed for multi-label recognition in the following two aspects.
Ranked #30 on
Multi-Label Classification
on MS-COCO
Deep Concept-wise Temporal Convolutional Networks for Action Localization
In this paper, we empirically find that stacking more conventional temporal convolution layers actually deteriorates action classification performance, possibly ascribing to that all channels of 1D feature map, which generally are highly abstract and can be regarded as latent concepts, are excessively recombined in temporal convolution.
Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition
In this challenge, we propose spatial-temporal network (StNet) for better joint spatial-temporal modelling and comprehensively video understanding.
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
In this paper, however, we show that temporal information, especially longer-term patterns, may not be necessary to achieve competitive results on common video classification datasets.
Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification
Experiment results on the challenging Kinetics dataset demonstrate that our proposed temporal modeling approaches can significantly improve existing approaches in the large-scale video recognition tasks.
Ranked #167 on
Action Classification
on Kinetics-400
Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding
This paper describes our solution for the video recognition task of the Google Cloud and YouTube-8M Video Understanding Challenge that ranked the 3rd place.
Semi-Federated Scheduling of Parallel Real-Time Tasks on Multiprocessors
In this paper we propose the semi-federate scheduling approach, which only grants $x$ dedicated processors to a heavy task with processing capacity requirement $x + \epsilon$, and schedules the remaining $\epsilon$ part together with light tasks on shared processors.
Distributed, Parallel, and Cluster Computing
Video Captioning with Multi-Faceted Attention
Recently, video captioning has been attracting an increasing amount of interest, due to its potential for improving accessibility and information retrieval.
