Skip to search formSkip to main contentSkip to account menu
Learning Deep Features for Discriminative Localization
In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network (CNN) to have remarkable localization ability…
View on IEEE
Places: A 10 Million Image Database for Scene Recognition
TLDR
The Places Database is described, a repository of 10 million scene photographs, labeled with scene semantic categories, comprising a large and diverse list of the types of environments encountered in the world, using the state-of-the-art Convolutional Neural Networks as baselines, that significantly outperform the previous approaches.
View on IEEE
Learning Deep Features for Scene Recognition using Places Database
TLDR
A new scene-centric database called Places with over 7 million labeled pictures of scenes is introduced with new methods to compare the density and diversity of image datasets and it is shown that Places is as dense as other scene datasets and has more diversity.
View Paper
Scene Parsing through ADE20K Dataset
TLDR
The ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts, is introduced and it is shown that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis.
View on IEEE
Network Dissection: Quantifying Interpretability of Deep Visual Representations
TLDR
This work uses the proposed Network Dissection method to test the hypothesis that interpretability is an axis-independent property of the representation space, then applies the method to compare the latent representations of various networks when trained to solve different classification problems.
View on IEEE
Semantic Understanding of Scenes Through the ADE20K Dataset
TLDR
This work presents a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts, and shows that the networks trained on this dataset are able to segment a wide variety of scenes and objects.
View on Springer
Temporal Relational Reasoning in Videos
TLDR
This paper introduces an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales.
View PDF on arXiv
Interpreting the Latent Space of GANs for Semantic Face Editing
TLDR
This work proposes a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs, and finds that the latent code of well-trained generative models actually learns a disentangled representation after linear transformations.
View on IEEE
Object Detectors Emerge in Deep Scene CNNs
TLDR
This work demonstrates that the same network can perform both scene recognition and object localization in a single forward-pass, without ever having been explicitly taught the notion of objects.
View PDF on arXiv
Unified Perceptual Parsing for Scene Understanding
TLDR
A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations and it is shown that it is able to effectively segment a wide range of concepts from images.
View PDF on arXiv
...
...
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy, Terms of Service, and Dataset License