Text2Model: Text-based Model Induction for Zero-shot Image Classification
The author introduces the Text2Model approach, which generates task-specific classifiers using only text descriptions, addressing limitations of existing zero-shot learning methods and demonstrating strong improvements in classification tasks.
PerceptionCLIP: Enhancing Zero-Shot Image Classification with Contextual Attributes
Enhancing zero-shot image classification by leveraging contextual attributes for improved performance.
PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts
人間の視覚知覚を模倣するPerceptionCLIPは、ゼロショット画像分類において改善された汎化性能、スパリアス特徴への依存の軽減、およびグループの堅牢性を達成します。
CLAMP: Contrastive Language Model for Zero-Shot Image Classification
Adapting Large Language Models for zero-shot image classification through contrastive learning.
Text2Model: Text-based Model Induction for Zero-shot Image Classification
Text2Model introduces a novel approach to zero-shot image classification by generating task-specific classifiers from text descriptions, outperforming existing methods.
Enhancing Zero-shot Image Classification through Confidence-weighted Fusion of Multi-model Alignment
A novel framework that leverages the strengths of multiple models, including CLIP and DINO, along with an adaptive weighting mechanism based on confidence levels, to significantly improve zero-shot image classification performance.
Enhancing Zero-Shot Image Classification Accuracy Using Multimodal Large Language Models
Multimodal large language models (LLMs) can significantly improve the accuracy of zero-shot image classification by generating rich textual representations of images, which complement visual features and enhance classification accuracy.
멀티모달 대규모 언어 모델을 사용한 제로샷 이미지 분류 성능 향상
멀티모달 대규모 언어 모델(LLM)을 활용하여 이미지 설명과 초기 예측을 생성하고, 이를 기존 이미지 특징과 결합하여 제로샷 이미지 분류 정확도를 향상시킬 수 있다.
카메라 트랩 이미지의 제로샷 분류를 향하여
본 논문에서는 MegaDetector와 두 개의 분류기를 결합한 방법과 DINOv2 기반 이미지 검색 방법을 통해 카메라 트랩 이미지의 제로샷 분류 가능성을 제시합니다.
