Compositional Zero-Shot Learning
24 papers with code • 4 benchmarks • 6 datasets
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).
( Image credit: Heosuab )
Benchmarks
These leaderboards are used to track progress in Compositional Zero-Shot Learning
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Libraries
Use these libraries to find Compositional Zero-Shot Learning models and implementations
MIT-States
UT Zappos50K
C-GQA
AO-CLEVr
UT-Zappos50K
Most implemented papers
Open World Compositional Zero-Shot Learning
ExplainableML/czsl
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CVPR 2021
After estimating the feasibility score of each composition, we use these scores to either directly mask the output space or as a margin for the cosine similarity between visual features and compositional embeddings during training.
Learning Graph Embeddings for Open World Compositional Zero-Shot Learning
In this work, we overcome this assumption operating on the open world setting, where no limit is imposed on the compositional space at test time, and the search space contains a large number of unseen compositions.
Attributes as Operators: Factorizing Unseen Attribute-Object Compositions
Tushar-N/attributes-as-operators
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ECCV 2018
In addition, we show that not only can our model recognize unseen compositions robustly in an open-world setting, it can also generalize to compositions where objects themselves were unseen during training.
Symmetry and Group in Attribute-Object Compositions
DirtyHarryLYL/SymNet
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CVPR 2020
To model the compositional nature of these general concepts, it is a good choice to learn them through transformations, such as coupling and decoupling.
A causal view of compositional zero-shot recognition
nv-research-israel/causal_comp
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NeurIPS 2020
This leads to consistent misclassification of samples from a new distribution, like new combinations of known components.
Learning Graph Embeddings for Compositional Zero-shot Learning
ExplainableML/czsl
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CVPR 2021
In compositional zero-shot learning, the goal is to recognize unseen compositions (e. g. old dog) of observed visual primitives states (e. g. old, cute) and objects (e. g. car, dog) in the training set.
Independent Prototype Propagation for Zero-Shot Compositionality
FrankRuis/ProtoProp
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NeurIPS 2021
Next we propagate the independent prototypes through a compositional graph, to learn compositional prototypes of novel attribute-object combinations that reflect the dependencies of the target distribution.
Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition
The concept module generates semantically meaningful features for primitive concepts, whereas the visual module extracts visual features for attributes and objects from input images.
Learning Single/Multi-Attribute of Object with Symmetry and Group
To model the compositional nature of these concepts, it is a good choice to learn them as transformations, e. g., coupling and decoupling.
BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning
zhihou7/batchformer
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CVPR 2022
We perform extensive experiments on over ten datasets and the proposed method achieves significant improvements on different data scarcity applications without any bells and whistles, including the tasks of long-tailed recognition, compositional zero-shot learning, domain generalization, and contrastive learning.
