Computer Vision - ECCV 2022
portes grátis
Computer Vision - ECCV 2022
17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XX
Avidan, Shai; Cisse, Moustapha; Brostow, Gabriel; Hassner, Tal; Farinella, Giovanni Maria
Springer International Publishing AG
10/2022
759
Mole
Inglês
9783031200434
15 a 20 dias
Descrição não disponível.
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-ShotLearning.- 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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
artificial intelligence;computer systems;computer vision;databases;education;image analysis;image coding;image processing;image segmentation;information retrieval;learning;machine learning;multimedia systems;object recognition;pattern recognition;signal processing
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-ShotLearning.- 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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
