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Found 16 papers, 3 papers with code
Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation
Since pseudo labels are noisy and unreliable, these methods suffer from catastrophic forgetting and error accumulation when dealing with dynamic data distributions.
Bayesian Evidential Learning for Few-Shot Classification
Few-Shot Classification(FSC) aims to generalize from base classes to novel classes given very limited labeled samples, which is an important step on the path toward human-like machine learning.
Trusted Multi-Scale Classification Framework for Whole Slide Image
Leveraging the Vision Transformer as the backbone for multi branches, our framework can jointly classification modeling, estimating the uncertainty of each magnification of a microscope and integrate the evidence from different magnification.
Memory-Based Label-Text Tuning for Few-Shot Class-Incremental Learning
The difficulties are that training on a sequence of limited data from new tasks leads to severe overfitting issues and causes the well-known catastrophic forgetting problem.
Switchable Representation Learning Framework with Self-compatibility
Existing research mainly focuses on the one-to-one compatible paradigm, which is limited in learning compatibility among multiple models.
Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation
Therefore, backward-compatible representation is proposed to enable "new" features to be compared with "old" features directly, which means that the database is active when there are both "new" and "old" features in it.
Dual-Tuning: Joint Prototype Transfer and Structure Regularization for Compatible Feature Learning
It is a heavy workload to re-extract features of the whole database every time. Feature compatibility enables the learned new visual features to be directly compared with the old features stored in the database.
Person30K: A Dual-Meta Generalization Network for Person Re-Identification
Recently, person re-identification (ReID) has vastly benefited from the surging waves of data-driven methods.
Large-Scale Unsupervised Person Re-Identification with Contrastive Learning
In particular, most existing unsupervised and domain adaptation ReID methods utilize only the public datasets in their experiments, with labels removed.
Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment
Federated Learning (FL) aims to establish a shared model across decentralized clients under the privacy-preserving constraint.
Dual-Refinement: Joint Label and Feature Refinement for Unsupervised Domain Adaptive Person Re-Identification
To this end, we propose a novel approach, called Dual-Refinement, that jointly refines pseudo labels at the off-line clustering phase and features at the on-line training phase, to alternatively boost the label purity and feature discriminability in the target domain for more reliable re-ID.
Prime-Aware Adaptive Distillation
With ten teacher-student combinations on six datasets, PAD promotes the performance of existing distillation methods and outperforms recent state-of-the-art methods.
Spherical Feature Transform for Deep Metric Learning
Consequently, the feature transform is performed by a rotation that respects the spherical data distributions.
Compact Descriptors for Video Analysis: the Emerging MPEG Standard
This paper provides an overview of the on-going compact descriptors for video analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG).
Incorporating Intra-Class Variance to Fine-Grained Visual Recognition
In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition.
Improving Object Detection with Region Similarity Learning
Object detection aims to identify instances of semantic objects of a certain class in images or videos.
