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Found 17 papers, 10 papers with code
Classifying Long-tailed and Label-noise Data via Disentangling and Unlearning
T2H noise severely degrades model performance by polluting the head classes and forcing the model to learn the tail samples as head.
Iterative Prompt Relocation for Distribution-Adaptive Visual Prompt Tuning
Specifically, we develop a prompt relocation strategy for ADO derived from this formulation, comprising two optimization steps: identifying and pruning idle prompts, followed by determining the optimal blocks for their relocation.
Asynchronous Federated Clustering with Unknown Number of Clusters
However, some tricky but common FC problems are still relatively unexplored, including the heterogeneity in terms of clients' communication capacity and the unknown number of proper clusters $k^*$.
Attributed Graph Clustering via Generalized Quaternion Representation Learning
Clustering complex data in the form of attributed graphs has attracted increasing attention, where appropriate graph representation is a critical prerequisite for accurate cluster analysis.
Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition
To address this issue, we propose a novel method called Random SAM prompt tuning (RSAM-PT) to improve the model generalization, requiring only one-step gradient computation at each step.
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning
Additionally, due to the diverse environments in which each client operates, data heterogeneity is also a classic challenge in federated learning.
Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D Visual Transformers
Accordingly, we propose the Adaptive PointFormer (APF), which fine-tunes pre-trained 2D models with only a modest number of parameters to directly process point clouds, obviating the need for mapping to images.
Transitive Vision-Language Prompt Learning for Domain Generalization
The vision-language pre-training has enabled deep models to make a huge step forward in generalizing across unseen domains.
Dynamically Anchored Prompting for Task-Imbalanced Continual Learning
We find that imbalanced tasks significantly challenge the capability of models to control the trade-off between stability and plasticity from the perspective of recent prompt-based continual learning methods.
Improve Knowledge Distillation via Label Revision and Data Selection
In addition to the supervision of ground truth, the vanilla KD method regards the predictions of the teacher as soft labels to supervise the training of the student model.
Feature Fusion from Head to Tail for Long-Tailed Visual Recognition
To rectify this issue, we propose to augment tail classes by grafting the diverse semantic information from head classes, referred to as head-to-tail fusion (H2T).
Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems
To achieve joint channel estimation and feedback, this paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix.
Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment
It is unfavorable for training on balanced data, but can be utilized to adjust the validity of the samples in long-tailed data, thereby solving the distorted embedding space of long-tailed problems.
Ranked #15 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Feature-Balanced Loss for Long-Tailed Visual Recognition
Deep neural networks frequently suffer from performance degradation when the training data is long-tailed because several majority classes dominate the training, resulting in a biased model.
Ranked #16 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition
That is, the embedding space of head classes severely compresses that of tail classes, which is not conducive to subsequent classifier learning.
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation
To address this problem, state-of-the-art methods usually adopt a mixture of experts (MoE) to focus on different parts of the long-tailed distribution.
FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark
Researchers have explored advanced methods from computer vision and natural language processing to incorporate medical domain knowledge for the generation of readable medical reports.
