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Found 8 papers, 6 papers with code
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.
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.
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.
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
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.
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.
