Inferring Analogous Attributes

Chao-Yeh Chen and Kristen Grauman
The University of Texas at Austin

The appearance of an attribute can vary considerably from class to class (e.g., a “fluffy” dog vs. a “fluffy” towel), making standard class-independent attribute models break down. Yet, training object-specific models for each attribute can be impractical, and defeats the purpose of using attributes to bridge category boundaries. We propose a novel form of transfer learning that addresses this dilemma. We develop a tensor factorization approach which, given a sparse set of class-specific attribute classifiers, can infer new ones for object-attribute pairs unobserved during training. For example, even though the system has no labeled images of striped dogs, it can use its knowledge of other attributes and objects to tailor “stripedness” to the dog category. With two large-scale datasets, we demonstrate both the need for category-sensitive attributes as well as our method’s successful transfer. Our inferred attribute classifiers perform similarly well to those trained with the luxury of labeled class-specific instances, and much better than those restricted to traditional modes of transfer.

Problem: want to learn category sensitive attributes

- Attributes: visual properties that describe objects and transcend object and scene category boundaries.
- Status quo approach: learn a single attribute classifier with data from all possible object/scene categories.

But are attributes truly category-independent? Below we show an example that the appearance of an attribute can vary considerably from class to class (e.g., a “fluffy” dog vs. a “fluffy” towel).


An intuitive but impractical solution…

- Category-sensitive attributes: learn a separate attribute classifier for each object category.

Learn category-sensitive fluffy attribute for dog by positive and negative examples.

- Ignore semantic sharing that some attributes do possess.
- Make unrealistic assumptions about training data availability.

The data availability for training category-sensitive attributes in ImageNet and SUN datasets.




-Infer the pose in missing view with tensor completion.

(1)        Learning category-sensitive attributes


- Importance-weighted support vector machine (SVM) to train a category-sensitive attribute.

- Attributes’ visual cues are shared among some objects, but the sharing is not universal.


(2)        Object-attribute classifier tensor

- Construct a tensor comprised of the sparse set of explicitly trained category-sensitive attribute classifiers

- Each W(n,m,:) is a category-sensitive SVM weight vector trained for the n-th object and m-th attribute.

- Due to label availability, the tensor is sparse (75% missing values).

- For non-linear classifiers, we use explicit kernel maps.


(3)        Inferring analogous attributes


- Apply Bayesian probabilistic tensor factorization [Xiong et al. 2010].

- Use recovered latent factors to impute unobserved classifier parameters.

(4)        Discussion


- Analogous attributes transfer information from multiple objects and attributes. 

- Novel transfer idea: tensor completion to infer classifiers “untrainable” from data.

- Assume some common structure among the explicitly trained category-sensitive models.


Accuracy (mAP) of attribute prediction

- We infer classifiers for 26K attributes. Category-sensitive baseline impossible for 70% of them!
- Our gains over Universal average 0.13 in AP for 79% of cases.

Accuracy (mAP) of attribute prediction. Total 664 categories x 84 attributes.

Example predictions

Test images that our method (top row) and the universal method (bottom row) predicted most confidently as having the named attribute. (X = positive for the attribute,  check = negative, according to ground truth.)

Discovered analogous attributes

- Which categories are found to be analogous for an attribute? Each example shows nearby category in latent space and the most similar attributes.

Analogous attribute examples for ImageNet (top) and SUN (bottom). Words above each neighbor indicate the 3 most similar attributes (learned or inferred) between leftmost query category and its neighboring categories in latent space. Query category: neighbor category= 1.Bottle: filter, syrup, bullshot, gerenuk. 2.Platypus: giraffe, ungulate, rorqual, patas. 3.Airplane cabin: aquarium, boat deck, conference center, art studio. 4.Courtroom: cardroom, florist shop, performance arena, beach house.

Focusing on semantically close data

- Could side information about relatedness further help ease transfer? Yes.  If we restrict tensor to closely related objects and closely related attributes, obtain even better results.

Attribute label prediction mAP when restricting the tensor to semantically close classes. The explicitly trained category-sensitive classifiers serve as an upper bound.


Inferring nonlinear classifiers

- We use the homogeneous kernel map of order 3 to approximate chi-square kernel nonlinear SVM.

Results using kernel maps to infer non-linear SVMs.


- Attributes are not strictly category-independent àNeed category-sensitive attributes.
- Infer analogous classifiers from observed classifiers organized by inter-related label spaces.
- Enables category-sensitive training, even when category-specific labeled data not possible.

- Paper, Supp
- Poster
- Bibtex