Search Results for author:
Found 14 papers, 11 papers with code
Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID.
TransReID: Transformer-based Object Re-Identification
Extracting robust feature representation is one of the key challenges in object re-identification (ReID).
Ranked #1 on
Person Re-Identification
on Market-1501-C
MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions
To investigate the feasibility of using motion expressions to ground and segment objects in videos, we propose a large-scale dataset called MeViS, which contains numerous motion expressions to indicate target objects in complex environments.
Ranked #2 on
Referring Video Object Segmentation
on MeViS
MOSE: A New Dataset for Video Object Segmentation in Complex Scenes
However, since the target objects in these existing datasets are usually relatively salient, dominant, and isolated, VOS under complex scenes has rarely been studied.
Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation
The inter-class relationships of semantic-related visual features are then required to be aligned with those in semantic space, thereby transferring semantic knowledge to visual feature learning.
GREC: Generalized Referring Expression Comprehension
This dataset encompasses a range of expressions: those referring to multiple targets, expressions with no specific target, and the single-target expressions.
Generalized Referring Expression Comprehension
Referring Expression
+1
An Empirical Study of Vehicle Re-Identification on the AI City Challenge
We mainly focus on four points, i. e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge.
1st Place Solution to VisDA-2020: Bias Elimination for Domain Adaptive Pedestrian Re-identification
Considering the large gap between the source domain and target domain, we focused on solving two biases that influenced the performance on domain adaptive pedestrian Re-ID and proposed a two-stage training procedure.
Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentation
In this work, we address the challenging task of few-shot and zero-shot 3D point cloud semantic segmentation.
Semantic-Promoted Debiasing and Background Disambiguation for Zero-Shot Instance Segmentation
It is desired to rescue novel objects from background and dominated seen categories.
Decoupling Static and Hierarchical Motion Perception for Referring Video Segmentation
In fact, static cues can sometimes interfere with temporal perception by overshadowing motion cues.
Ranked #1 on
Referring Video Object Segmentation
on MeViS
Region Generation and Assessment Network for Occluded Person Re-Identification
Then, to measure the importance of each generated region, we introduce a Region Assessment Module (RAM) that assigns confidence scores to different regions and reduces the negative impact of the occlusion regions by lower scores.
VGSG: Vision-Guided Semantic-Group Network for Text-based Person Search
With the help of relational knowledge transfer, VGKT is capable of aligning semantic-group textual features with corresponding visual features without external tools and complex pairwise interaction.
Ranked #6 on
Text based Person Retrieval
on CUHK-PEDES
(using extra training data)
Context-Aware Integration of Language and Visual References for Natural Language Tracking
Tracking by natural language specification (TNL) aims to consistently localize a target in a video sequence given a linguistic description in the initial frame.
