Papers with Code 아카이브
Compositional Zero-Shot Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 4
- 보존된 평가 기록
- 6
Compositional Zero-Shot Learning (CZSL) is a computer vision task in which the goal is to recognize unseen compositions fromed from seen state and object during training. The key challenge in CZSL is the inherent entanglement between the state and object within the context of an image. Some example benchmarks for this task are MIT-states, UT-Zappos, and C-GQA. Models are usually evaluated with the Accuracy for both seen and unseen compositions, as well as their Harmonic Mean(HM).
<span style="color:grey; opacity: 0.6">( Image credit: Heosuab )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Feasibility with Language Models for Open-World Compositional Zero-Shot Learning
MSCI: Addressing CLIP's Inherent Limitations for Compositional Zero-Shot Learning
Visual Adaptive Prompting for Compositional Zero-Shot Learning
Learning Clustering-based Prototypes for Compositional Zero-shot Learning
Learning Primitive Relations for Compositional Zero-Shot Learning
Dual-Modal Prototype Joint Learning for Compositional Zero-Shot Learning
Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning
Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning
Duplex: Dual Prototype Learning for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training
Unified Framework for Open-World Compositional Zero-shot Learning
Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot Learning
Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning
Cross-composition Feature Disentanglement for Compositional Zero-shot Learning
TsCA: On the Semantic Consistency Alignment via Conditional Transport for Compositional Zero-Shot Learning
Attention Based Simple Primitives for Open World Compositional Zero-Shot Learning
Anticipating Future Object Compositions without Forgetting
Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot Learning
MAC: A Benchmark for Multiple Attributes Compositional Zero-Shot Learning
CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning
Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning
Revealing the Proximate Long-Tail Distribution in Compositional Zero-Shot Learning
Compositional Zero-Shot Learning for Attribute-Based Object Reference in Human-Robot Interaction
Prompt Tuning for Zero-shot Compositional Learning
Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding
HOMOE: A Memory-Based and Composition-Aware Framework for Zero-Shot Learning with Hopfield Network and Soft Mixture of Experts
Compositional Zero-shot Learning via Progressive Language-based Observations
GIPCOL: Graph-Injected Soft Prompting for Compositional Zero-Shot Learning
Hierarchical Visual Primitive Experts for Compositional Zero-Shot Learning
Learning Conditional Attributes for Compositional Zero-Shot Learning
CAILA: Concept-Aware Intra-Layer Adapters for Compositional Zero-Shot Learning
Prompting Language-Informed Distribution for Compositional Zero-Shot Learning
DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning
Learning Attention as Disentangler for Compositional Zero-shot Learning
Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.