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Found 11 papers, 5 papers with code
Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation
In this paper, we propose a simple yet effective approach for self-supervised video object segmentation (VOS).
Masked Autoencoders are Robust Data Augmentors
Specifically, MRA consistently enhances the performance on supervised, semi-supervised as well as few-shot classification.
Semantic-Aware Generation for Self-Supervised Visual Representation Learning
In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by requiring the target network to recover the original image based on the mid-level features.
Ranked #63 on
Semantic Segmentation
on Cityscapes test
Motion-aware Contrastive Video Representation Learning via Foreground-background Merging
To alleviate such bias, we propose \textbf{F}oreground-b\textbf{a}ckground \textbf{Me}rging (FAME) to deliberately compose the moving foreground region of the selected video onto the static background of others.
Bag of Instances Aggregation Boosts Self-supervised Distillation
Here bag of instances indicates a set of similar samples constructed by the teacher and are grouped within a bag, and the goal of distillation is to aggregate compact representations over the student with respect to instances in a bag.
Multi-dataset Pretraining: A Unified Model for Semantic Segmentation
This is achieved by first pretraining the network via the proposed pixel-to-prototype contrastive loss over multiple datasets regardless of their taxonomy labels, and followed by fine-tuning the pretrained model over specific dataset as usual.
Semi-supervised Contrastive Learning with Similarity Co-calibration
Semi-supervised learning acts as an effective way to leverage massive unlabeled data.
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning
In this paper, we propose a hierarchical semantic alignment strategy via expanding the views generated by a single image to \textbf{Cross-samples and Multi-level} representation, and models the invariance to semantically similar images in a hierarchical way.
K-Shot Contrastive Learning of Visual Features with Multiple Instance Augmentations
In this paper, we propose the $K$-Shot Contrastive Learning (KSCL) of visual features by applying multiple augmentations to investigate the sample variations within individual instances.
FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers
To this end, we present a novel regularization mechanism by learning the change of feature representations induced by a distribution of transformations without using the labels of data examples.
AETv2: AutoEncoding Transformations for Self-Supervised Representation Learning by Minimizing Geodesic Distances in Lie Groups
For this reason, we should use the geodesic to characterize how an image transform along the manifold of a transformation group, and adopt its length to measure the deviation between transformations.
