We seek to learn models that we can interact with using high-level concepts: if the model did
not think there was a bone spur in the x-ray, would it still predict severe arthritis? State-of-the …

One of the most challenging question types in VQA is when answering the question requires
outside knowledge not present in the image. In this work we study open-domain knowledge …

Person re-identification (re-ID) and attribute recognition share a common target at learning
pedestrian descriptions. Their difference consists in the granularity. Most existing re-ID …

Abstract We propose'Hide-and-Seek', a weakly-supervised framework that aims to improve
object localization in images and action localization in videos. Most existing weakly …

State-of-the-art methods for zero-shot visual recognition formulate learning as a joint
embedding problem of images and side information. In these formulations the current best …

We present a novel latent embedding model for learning a compatibility function between
image and class embeddings, in the context of zero-shot classification. The proposed …

Attributes act as intermediate representations that enable parameter sharing between
classes, a must when training data is scarce. We propose to view attribute-based image …

Semantic part localization can facilitate fine-grained categorization by explicitly isolating
subtle appearance differences associated with specific object parts. Methods for pose …

Image classification has advanced significantly in recent years with the availability of large-
scale image sets. However, fine-grained classification remains a major challenge due to the …

This work targets human action recognition in video. While recent methods typically
represent actions by statistics of local video features, here we argue for the importance of a …