68,550 research outputs found
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
This paper addresses the task of zero-shot image classification. The key
contribution of the proposed approach is to control the semantic embedding of
images -- one of the main ingredients of zero-shot learning -- by formulating
it as a metric learning problem. The optimized empirical criterion associates
two types of sub-task constraints: metric discriminating capacity and accurate
attribute prediction. This results in a novel expression of zero-shot learning
not requiring the notion of class in the training phase: only pairs of
image/attributes, augmented with a consistency indicator, are given as ground
truth. At test time, the learned model can predict the consistency of a test
image with a given set of attributes , allowing flexible ways to produce
recognition inferences. Despite its simplicity, the proposed approach gives
state-of-the-art results on four challenging datasets used for zero-shot
recognition evaluation.Comment: in ECCV 2016, Oct 2016, amsterdam, Netherlands. 201
Why do These Match? Explaining the Behavior of Image Similarity Models
Explaining a deep learning model can help users understand its behavior and
allow researchers to discern its shortcomings. Recent work has primarily
focused on explaining models for tasks like image classification or visual
question answering. In this paper, we introduce Salient Attributes for Network
Explanation (SANE) to explain image similarity models, where a model's output
is a score measuring the similarity of two inputs rather than a classification
score. In this task, an explanation depends on both of the input images, so
standard methods do not apply. Our SANE explanations pairs a saliency map
identifying important image regions with an attribute that best explains the
match. We find that our explanations provide additional information not
typically captured by saliency maps alone, and can also improve performance on
the classic task of attribute recognition. Our approach's ability to generalize
is demonstrated on two datasets from diverse domains, Polyvore Outfits and
Animals with Attributes 2. Code available at:
https://github.com/VisionLearningGroup/SANEComment: Accepted at ECCV 202
- …