Search Results for author:
Found 17 papers, 5 papers with code
RefineVIS: Video Instance Segmentation with Temporal Attention Refinement
We introduce a novel framework called RefineVIS for Video Instance Segmentation (VIS) that achieves good object association between frames and accurate segmentation masks by iteratively refining the representations using sequence context.
Ranked #1 on
Video Instance Segmentation
on YouTube-VIS 2021
(using extra training data)
Consistent Video Instance Segmentation with Inter-Frame Recurrent Attention
We propose a consistent end-to-end video instance segmentation framework with Inter-Frame Recurrent Attention to model both the temporal instance consistency for adjacent frames and the global temporal context.
Deep Frequency Filtering for Domain Generalization
Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge.
Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation
In this paper, to address more practical scenarios, we propose a new task, Lifelong Unsupervised Domain Adaptive (LUDA) person ReID.
Domain Adaptive Person Re-Identification
Knowledge Distillation
+4
MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark
This dataset provides a more reliable benchmark of multi-camera, multi-object tracking systems in cluttered and crowded environments.
Ranked #2 on
Object Tracking
on MMPTRACK
TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking
TransMOT effectively models the interactions of a large number of objects by arranging the trajectories of the tracked objects as a set of sparse weighted graphs, and constructing a spatial graph transformer encoder layer, a temporal transformer encoder layer, and a spatial graph transformer decoder layer based on the graphs.
Ranked #2 on
Multi-Object Tracking
on 2DMOT15
(using extra training data)
GMOT-40: A Benchmark for Generic Multiple Object Tracking
Multiple Object Tracking (MOT) has witnessed remarkable advances in recent years.
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
The average video length of LaSOT is around 2, 500 frames, where each video contains various challenge factors that exist in real world video footage, such as the targets disappearing and re-appearing.
Feature Space Augmentation for Long-Tailed Data
Real-world data often follow a long-tailed distribution as the frequency of each class is typically different.
Ranked #24 on
Long-tail Learning
on Places-LT
Map3D: Registration Based Multi-Object Tracking on 3D Serial Whole Slide Images
However, the 3D identification and association of large-scale glomeruli on renal pathology is challenging due to large tissue deformation, missing tissues, and artifacts from WSI.
Graph Neural Network for Hamiltonian-Based Material Property Prediction
Development of next-generation electronic devices for applications call for the discovery of quantum materials hosting novel electronic, magnetic, and topological properties.
TracKlinic: Diagnosis of Challenge Factors in Visual Tracking
For the analysis component, given the tracking results on all sequences, it investigates the behavior of the tracker under each individual factor and generates the report automatically.
Clustered Object Detection in Aerial Images
The key components in ClusDet include a cluster proposal sub-network (CPNet), a scale estimation sub-network (ScaleNet), and a dedicated detection network (DetecNet).
FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking
Data association-based multiple object tracking (MOT) involves multiple separated modules processed or optimized differently, which results in complex method design and requires non-trivial tuning of parameters.
Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment
To address this issue, in this paper we propose an instance-aware tracker to integrate SOT techniques for MOT by encoding awareness both within and between target models.
Scene Parsing via Dense Recurrent Neural Networks with Attentional Selection
Recurrent neural networks (RNNs) have shown the ability to improve scene parsing through capturing long-range dependencies among image units.
LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking.
