In this paper, we consider the problem of simultaneously detecting objects and inferring their
visual attributes in an image, even for those with no manual annotations provided at the …

Recognizing and generating object-state compositions has been a challenging task,
especially when generalizing to unseen compositions. In this paper, we study the task of …

Abstract Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel
compositional concepts based on learned concepts such as attribute-object combinations …

We study the visual semantic embedding problem for image-text matching. Most existing
work utilizes a tailored cross-attention mechanism to perform local alignment across the two …

We study recognizing attributes for objects in visual scenes. We consider attributes to be any
phrases that describe an object's physical and semantic properties, and its relationships with …

Compositional zero-shot learning (CZSL) aims at learning visual concepts (ie, attributes and
objects) from seen compositions and combining concept knowledge into unseen …

We aim to learn to temporally localize object state changes and the corresponding state-
modifying actions by observing people interacting with objects in long uncurated web …

Compositional zero-shot learning (CZSL) aims to recognize unseen compositions with prior
knowledge of known primitives (attribute and object). Previous works for CZSL often suffer …

In photo editing, it is common practice to remove visual distractions to improve the overall
image quality and highlight the primary subject. However, manually selecting and removing …

We aim to learn to temporally localize object state changes and the corresponding state-
modifying actions by observing people interacting with objects in long uncurated web …