7,447 research outputs found
Hierarchical Sound Event Classification
Task 5 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge is "urban sound tagging''. Given a set of known sound categories and sub-categories, the goal is to build a multi-label audio classification model to predict whether each sound category is present or absent in an audio recording. We developed a model composed of a preprocessing layer that converts audio to a log-mel spectrogram, a VGG-inspired Convolutional Neural Network (CNN) that generates an embedding for the spectrogram, a pre-trained VGGish network that generates a separate audio embedding, and finally a series of fully-connected layers that converts these two embeddings (concatenated) into a multi-label classification. This model directly outputs both āfineā and ācoarseā labels; it treats the task as a 37-way multi-label classification problem. One version of this network did better at the coarse labels (CNN+VGGish1); another did better with fine labels on Micro AUPRC (CNN+VGGish2). A separate family of CNN models was also trained to take into account the hierarchical nature of the labels (Hierarchical1, Hierarchical2, and Hierarchical3). The hierarchical models perform better on Micro AUPRC with fine-level classification.24825
HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition
In image classification, visual separability between different object
categories is highly uneven, and some categories are more difficult to
distinguish than others. Such difficult categories demand more dedicated
classifiers. However, existing deep convolutional neural networks (CNN) are
trained as flat N-way classifiers, and few efforts have been made to leverage
the hierarchical structure of categories. In this paper, we introduce
hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category
hierarchy. An HD-CNN separates easy classes using a coarse category classifier
while distinguishing difficult classes using fine category classifiers. During
HD-CNN training, component-wise pretraining is followed by global finetuning
with a multinomial logistic loss regularized by a coarse category consistency
term. In addition, conditional executions of fine category classifiers and
layer parameter compression make HD-CNNs scalable for large-scale visual
recognition. We achieve state-of-the-art results on both CIFAR100 and
large-scale ImageNet 1000-class benchmark datasets. In our experiments, we
build up three different HD-CNNs and they lower the top-1 error of the standard
CNNs by 2.65%, 3.1% and 1.1%, respectively.Comment: Add new results on ImageNet using VGG-16-layer building block ne
Dual Skipping Networks
Inspired by the recent neuroscience studies on the left-right asymmetry of
the human brain in processing low and high spatial frequency information, this
paper introduces a dual skipping network which carries out coarse-to-fine
object categorization. Such a network has two branches to simultaneously deal
with both coarse and fine-grained classification tasks. Specifically, we
propose a layer-skipping mechanism that learns a gating network to predict
which layers to skip in the testing stage. This layer-skipping mechanism endows
the network with good flexibility and capability in practice. Evaluations are
conducted on several widely used coarse-to-fine object categorization
benchmarks, and promising results are achieved by our proposed network model.Comment: CVPR 2018 (poster); fix typ
Fine-grained Image Classification by Exploring Bipartite-Graph Labels
Given a food image, can a fine-grained object recognition engine tell "which
restaurant which dish" the food belongs to? Such ultra-fine grained image
recognition is the key for many applications like search by images, but it is
very challenging because it needs to discern subtle difference between classes
while dealing with the scarcity of training data. Fortunately, the ultra-fine
granularity naturally brings rich relationships among object classes. This
paper proposes a novel approach to exploit the rich relationships through
bipartite-graph labels (BGL). We show how to model BGL in an overall
convolutional neural networks and the resulting system can be optimized through
back-propagation. We also show that it is computationally efficient in
inference thanks to the bipartite structure. To facilitate the study, we
construct a new food benchmark dataset, which consists of 37,885 food images
collected from 6 restaurants and totally 975 menus. Experimental results on
this new food and three other datasets demonstrates BGL advances previous works
in fine-grained object recognition. An online demo is available at
http://www.f-zhou.com/fg_demo/
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Salient object detection increasingly receives attention as an important
component or step in several pattern recognition and image processing tasks.
Although a variety of powerful saliency models have been intensively proposed,
they usually involve heavy feature (or model) engineering based on priors (or
assumptions) about the properties of objects and backgrounds. Inspired by the
effectiveness of recently developed feature learning, we provide a novel Deep
Image Saliency Computing (DISC) framework for fine-grained image saliency
computing. In particular, we model the image saliency from both the coarse- and
fine-level observations, and utilize the deep convolutional neural network
(CNN) to learn the saliency representation in a progressive manner.
Specifically, our saliency model is built upon two stacked CNNs. The first CNN
generates a coarse-level saliency map by taking the overall image as the input,
roughly identifying saliency regions in the global context. Furthermore, we
integrate superpixel-based local context information in the first CNN to refine
the coarse-level saliency map. Guided by the coarse saliency map, the second
CNN focuses on the local context to produce fine-grained and accurate saliency
map while preserving object details. For a testing image, the two CNNs
collaboratively conduct the saliency computing in one shot. Our DISC framework
is capable of uniformly highlighting the objects-of-interest from complex
background while preserving well object details. Extensive experiments on
several standard benchmarks suggest that DISC outperforms other
state-of-the-art methods and it also generalizes well across datasets without
additional training. The executable version of DISC is available online:
http://vision.sysu.edu.cn/projects/DISC.Comment: This manuscript is the accepted version for IEEE Transactions on
Neural Networks and Learning Systems (T-NNLS), 201
- ā¦