1,742 research outputs found
Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition
A key challenge in fine-grained recognition is how to find and represent
discriminative local regions. Recent attention models are capable of learning
discriminative region localizers only from category labels with reinforcement
learning. However, not utilizing any explicit part information, they are not
able to accurately find multiple distinctive regions. In this work, we
introduce an attribute-guided attention localization scheme where the local
region localizers are learned under the guidance of part attribute
descriptions. By designing a novel reward strategy, we are able to learn to
locate regions that are spatially and semantically distinctive with
reinforcement learning algorithm. The attribute labeling requirement of the
scheme is more amenable than the accurate part location annotation required by
traditional part-based fine-grained recognition methods. Experimental results
on the CUB-200-2011 dataset demonstrate the superiority of the proposed scheme
on both fine-grained recognition and attribute recognition
The iNaturalist Species Classification and Detection Dataset
Existing image classification datasets used in computer vision tend to have a
uniform distribution of images across object categories. In contrast, the
natural world is heavily imbalanced, as some species are more abundant and
easier to photograph than others. To encourage further progress in challenging
real world conditions we present the iNaturalist species classification and
detection dataset, consisting of 859,000 images from over 5,000 different
species of plants and animals. It features visually similar species, captured
in a wide variety of situations, from all over the world. Images were collected
with different camera types, have varying image quality, feature a large class
imbalance, and have been verified by multiple citizen scientists. We discuss
the collection of the dataset and present extensive baseline experiments using
state-of-the-art computer vision classification and detection models. Results
show that current non-ensemble based methods achieve only 67% top one
classification accuracy, illustrating the difficulty of the dataset.
Specifically, we observe poor results for classes with small numbers of
training examples suggesting more attention is needed in low-shot learning.Comment: CVPR 201
The Devil is in the Tails: Fine-grained Classification in the Wild
The world is long-tailed. What does this mean for computer vision and visual
recognition? The main two implications are (1) the number of categories we need
to consider in applications can be very large, and (2) the number of training
examples for most categories can be very small. Current visual recognition
algorithms have achieved excellent classification accuracy. However, they
require many training examples to reach peak performance, which suggests that
long-tailed distributions will not be dealt with well. We analyze this question
in the context of eBird, a large fine-grained classification dataset, and a
state-of-the-art deep network classification algorithm. We find that (a) peak
classification performance on well-represented categories is excellent, (b)
given enough data, classification performance suffers only minimally from an
increase in the number of classes, (c) classification performance decays
precipitously as the number of training examples decreases, (d) surprisingly,
transfer learning is virtually absent in current methods. Our findings suggest
that our community should come to grips with the question of long tails
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
Feedback-prop: Convolutional Neural Network Inference under Partial Evidence
We propose an inference procedure for deep convolutional neural networks
(CNNs) when partial evidence is available. Our method consists of a general
feedback-based propagation approach (feedback-prop) that boosts the prediction
accuracy for an arbitrary set of unknown target labels when the values for a
non-overlapping arbitrary set of target labels are known. We show that existing
models trained in a multi-label or multi-task setting can readily take
advantage of feedback-prop without any retraining or fine-tuning. Our
feedback-prop inference procedure is general, simple, reliable, and works on
different challenging visual recognition tasks. We present two variants of
feedback-prop based on layer-wise and residual iterative updates. We experiment
using several multi-task models and show that feedback-prop is effective in all
of them. Our results unveil a previously unreported but interesting dynamic
property of deep CNNs. We also present an associated technical approach that
takes advantage of this property for inference under partial evidence in
general visual recognition tasks.Comment: Accepted to CVPR 201
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