Search Results for author:
Found 25 papers, 14 papers with code
Online Camera-to-ground Calibration for Autonomous Driving
Online camera-to-ground calibration is to generate a non-rigid body transformation between the camera and the road surface in a real-time manner.
OvarNet: Towards Open-vocabulary Object Attribute Recognition
In this paper, we consider the problem of simultaneously detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at the training stage, resembling an open-vocabulary scenario.
Ranked #1 on
Open Vocabulary Attribute Detection
on OVAD benchmark
(using extra training data)
Pose correction scheme for camera-scanning Fourier ptychography based on camera calibration and homography transform
Fourier ptychography (FP), as a computational imaging method, is a powerful tool to improve imaging resolution.
Parallel Fourier Ptychography reconstruction
Fourier ptychography has attracted a wide range of focus for its ability of large space-bandwidth-produce, and quantative phase measurement.
Decoupled IoU Regression for Object Detection
Prior works propose to predict Intersection-over-Union (IoU) between bounding boxes and corresponding ground-truths to improve NMS, while accurately predicting IoU is still a challenging problem.
Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge
To promote the development of occlusion understanding, we collect a large-scale dataset called OVIS for video instance segmentation in the occluded scenario.
Unsupervised Segmentation for Terracotta Warrior with Seed-Region-Growing CNN(SRG-Net)
Then we present a supervised segmentation and unsupervised reconstruction networks to learn the characteristics of 3D point clouds.
Boosting Entity-aware Image Captioning with Multi-modal Knowledge Graph
Entity-aware image captioning aims to describe named entities and events related to the image by utilizing the background knowledge in the associated article.
End-to-end Temporal Action Detection with Transformer
Temporal action detection (TAD) aims to determine the semantic label and the temporal interval of every action instance in an untrimmed video.
Ranked #3 on
Temporal Action Localization
on HACS
Salient Object Ranking with Position-Preserved Attention
In this paper, we study the Salient Object Ranking (SOR) task, which manages to assign a ranking order of each detected object according to its visual saliency.
PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
Classical recommender system methods typically face the filter bubble problem when users only receive recommendations of their familiar items, making them bored and dissatisfied.
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction
While several cross domain sequential recommendation models have been proposed to leverage information from a source domain to improve CTR predictions in a target domain, they did not take into account bidirectional latent relations of user preferences across source-target domain pairs.
SwiftNet: Real-time Video Object Segmentation
In this work we present SwiftNet for real-time semisupervised video object segmentation (one-shot VOS), which reports 77. 8% J &F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance.
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
Occluded Video Instance Segmentation: A Benchmark
On the OVIS dataset, the highest AP achieved by state-of-the-art algorithms is only 16. 3, which reveals that we are still at a nascent stage for understanding objects, instances, and videos in a real-world scenario.
Ranked #16 on
Video Instance Segmentation
on OVIS validation
Horizontal-to-Vertical Video Conversion
Alongside the prevalence of mobile videos, the general public leans towards consuming vertical videos on hand-held devices.
Multi-shot Temporal Event Localization: a Benchmark
Current developments in temporal event or action localization usually target actions captured by a single camera.
Ranked #2 on
Temporal Action Localization
on MUSES
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework
Meanwhile, such applications usually require modeling the intrinsic clusters in high-dimensional data, which usually displays heterogeneous statistical patterns as the patterns of different clusters may appear in different dimensions.
Unsupervised Segmentation for Terracotta Warrior Point Cloud (SRG-Net)
Then we present a supervised segmentation and unsupervised reconstruction networks to learn the characteristics of 3D point clouds.
Reducing the Teacher-Student Gap via Spherical Knowledge Disitllation
We investigate this problem by study the gap of confidence between teacher and student.
Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge
On the other hand, there is a large semantic gap between seen and unseen classes in the existing multi-label classification datasets.
Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution
Click-through rate (CTR) prediction is an essential task in industrial applications such as video recommendation.
Attribute-aware Pedestrian Detection in a Crowd
To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion.
Correlation Maximized Structural Similarity Loss for Semantic Segmentation
Inspired by the widely-used structural similarity (SSIM) index in image quality assessment, we use the linear correlation between two images to quantify their structural similarity.
Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning
An increasing number of well-trained deep networks have been released online by researchers and developers, enabling the community to reuse them in a plug-and-play way without accessing the training annotations.
Multi-Objective Generalized Linear Bandits
In this paper, we study the multi-objective bandits (MOB) problem, where a learner repeatedly selects one arm to play and then receives a reward vector consisting of multiple objectives.
