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tSF: Transformer-Based Semantic Filter for Few-Shot Learning
Adversarial Feature Augmentation for Cross-Domain Few-Shot Classification
Constructing Balance from Imbalance for Long-Tailed Image Recognition
On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond
Few-Shot Video Object Detection
Worst Case Matters for Few-Shot Recognition
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image Classification
Doubly Deformable Aggregation of Covariance Matrices for Few-Shot Segmentation
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation
Rethinking Clustering-Based Pseudo Labeling for Unsupervised Meta-Learning
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition
Few-Shot Class-Incremental Learning for 3D Point Cloud Objects
Meta-Learning with Less Forgetting on Large-Scale Non-stationary Task Distributions
DNA: Improving Few-Shot Transfer Learning with Low-Rank Decomposition and Alignment
Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning
Open-World Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding
Few-Shot Classification with Contrastive Learning
Time-rEversed diffusioN tEnsor Transformer: A New TENET of Few-Shot Object Detection
Self-Promoted Supervision for Few-Shot Transformer
Few-Shot Object Counting and Detection
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations
Mutually Reinforcing Structure with Proposal Contrastive Consistency for Few-Shot Object Detection
Dual Contrastive Learning with Anatomical Auxiliary Supervision for Few-Shot Medical Image Segmentation
Improving Few-Shot Learning through Multi-task Representation Learning Theory
Tree Structure-Aware Few Shot Image Classification via Hierarchical Aggregation
Inductive and Transductive Few Shot Video Classification via Appearance and Temporal Alignments
Temporal and Cross-Modal Attention for Audio-Visual Zero-Shot Learning
HM: Hybrid Masking for Few-Shot Segmentation
TransVLAD: Focusing on Locally Aggregated Descriptors for Few-Shot Learning
Kernel Relative-Prototype Spectral Filtering for Few-Shot Learning
"This Is My Unicorn, Fluffy" : Personalizing Frozen Vision-Language Representations
CLOSE: Curriculum Learning on the Sharing Extent towards Better One-Shot NAS
Streamable Neural Fields
Gradient-Based Uncertainty for Monocular Depth Estimation
Online Continual Learning with Contrastive Vision Transformer
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution
EAutoDet: Efficient Architecture Search for Object Detection
A Max-Flow Based Approach for Neural Architecture Search
OccamNets: Mitigating Dataset Bias by Favoring Simpler Hypotheses
ERA: Enhanced Rational Activations
Convolutional Embedding Makes Hierarchical Vision Transformer Stronger.
Adversarial Feature Augmentation for Cross-Domain Few-Shot Classification
Constructing Balance from Imbalance for Long-Tailed Image Recognition
On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond
Few-Shot Video Object Detection
Worst Case Matters for Few-Shot Recognition
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image Classification
Doubly Deformable Aggregation of Covariance Matrices for Few-Shot Segmentation
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation
Rethinking Clustering-Based Pseudo Labeling for Unsupervised Meta-Learning
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition
Few-Shot Class-Incremental Learning for 3D Point Cloud Objects
Meta-Learning with Less Forgetting on Large-Scale Non-stationary Task Distributions
DNA: Improving Few-Shot Transfer Learning with Low-Rank Decomposition and Alignment
Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning
Open-World Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding
Few-Shot Classification with Contrastive Learning
Time-rEversed diffusioN tEnsor Transformer: A New TENET of Few-Shot Object Detection
Self-Promoted Supervision for Few-Shot Transformer
Few-Shot Object Counting and Detection
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations
Mutually Reinforcing Structure with Proposal Contrastive Consistency for Few-Shot Object Detection
Dual Contrastive Learning with Anatomical Auxiliary Supervision for Few-Shot Medical Image Segmentation
Improving Few-Shot Learning through Multi-task Representation Learning Theory
Tree Structure-Aware Few Shot Image Classification via Hierarchical Aggregation
Inductive and Transductive Few Shot Video Classification via Appearance and Temporal Alignments
Temporal and Cross-Modal Attention for Audio-Visual Zero-Shot Learning
HM: Hybrid Masking for Few-Shot Segmentation
TransVLAD: Focusing on Locally Aggregated Descriptors for Few-Shot Learning
Kernel Relative-Prototype Spectral Filtering for Few-Shot Learning
"This Is My Unicorn, Fluffy" : Personalizing Frozen Vision-Language Representations
CLOSE: Curriculum Learning on the Sharing Extent towards Better One-Shot NAS
Streamable Neural Fields
Gradient-Based Uncertainty for Monocular Depth Estimation
Online Continual Learning with Contrastive Vision Transformer
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution
EAutoDet: Efficient Architecture Search for Object Detection
A Max-Flow Based Approach for Neural Architecture Search
OccamNets: Mitigating Dataset Bias by Favoring Simpler Hypotheses
ERA: Enhanced Rational Activations
Convolutional Embedding Makes Hierarchical Vision Transformer Stronger.