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Found 17 papers, 3 papers with code
NTK-Guided Few-Shot Class Incremental Learning
While anti-amnesia FSCIL learners often excel in incremental sessions, they tend to prioritize mitigating knowledge attrition over harnessing the model's potential for knowledge acquisition.
SYNC-CLIP: Synthetic Data Make CLIP Generalize Better in Data-Limited Scenarios
Prompt learning is a powerful technique for transferring Vision-Language Models (VLMs) such as CLIP to downstream tasks.
Dense Affinity Matching for Few-Shot Segmentation
Few-Shot Segmentation (FSS) aims to segment the novel class images with a few annotated samples.
MetaGait: Learning to Learn an Omni Sample Adaptive Representation for Gait Recognition
Towards this goal, MetaGait injects meta-knowledge, which could guide the model to perceive sample-specific properties, into the calibration network of the attention mechanism to improve the adaptiveness from the omni-scale, omni-dimension, and omni-process perspectives.
GaitGCI: Generative Counterfactual Intervention for Gait Recognition
Gait is one of the most promising biometrics that aims to identify pedestrians from their walking patterns.
DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based Point-Level Consistency
In this paper, we propose a simple yet effective transformer framework for self-supervised learning called DenseDINO to learn dense visual representations.
Multi-Content Interaction Network for Few-Shot Segmentation
Few-Shot Segmentation (FSS) is challenging for limited support images and large intra-class appearance discrepancies.
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval
We introduce dynamic dictionaries for both modalities to enlarge the scale of image-text pairs, and diversity-sensitiveness is achieved by adaptive negative pair weighting.
Episode-based Prototype Generating Network for Zero-Shot Learning
We introduce a simple yet effective episode-based training framework for zero-shot learning (ZSL), where the learning system requires to recognize unseen classes given only the corresponding class semantics.
A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification
Towards alleviating the class imbalance issue in ZSC, we propose a sample-balanced training process to encourage all training classes to contribute equally to the learned model.
Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning
Zero-Shot Learning (ZSL) is generally achieved via aligning the semantic relationships between the visual features and the corresponding class semantic descriptions.
Bi-Adversarial Auto-Encoder for Zero-Shot Learning
Existing generative Zero-Shot Learning (ZSL) methods only consider the unidirectional alignment from the class semantics to the visual features while ignoring the alignment from the visual features to the class semantics, which fails to construct the visual-semantic interactions well.
Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning
To this end, we propose a novel stacked semantics-guided attention (S2GA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions.
Attribute-Guided Network for Cross-Modal Zero-Shot Hashing
To address the Cross-Modal Zero-Shot Hashing (CMZSH) retrieval task, we propose a novel Attribute-Guided Network (AgNet), which can perform not only IBIR, but also Text-Based Image Retrieval (TBIR).
Zero-Shot Learning via Latent Space Encoding
Instead of requiring a projection function to transfer information across different modalities like most previous work, LSE per- forms the interactions of different modalities via a feature aware latent space, which is learned in an implicit way.
Transductive Zero-Shot Learning with a Self-training dictionary approach
As an important and challenging problem in computer vision, zero-shot learning (ZSL) aims at automatically recognizing the instances from unseen object classes without training data.
Transductive Zero-Shot Learning with Adaptive Structural Embedding
Two fundamental challenges in it are visual-semantic embedding and domain adaptation in cross-modality learning and unseen class prediction steps, respectively.
