Scene Recognition
64 papers with code • 8 benchmarks • 15 datasets
Benchmarks
These leaderboards are used to track progress in Scene Recognition
ScanNet
ADE20K
Places205
SUN RGB-D
Places365
SUN397
AID
YUP++
Most implemented papers
Temporal Residual Networks for Dynamic Scene Recognition
Finally, our temporal ResNet boosts recognition performance and establishes a new state-of-the-art on dynamic scene recognition, as well as on the complementary task of action recognition.
AGA: Attribute-Guided Augmentation
rkwitt/GuidedAugmentation
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CVPR 2017
We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner.
Depth CNNs for RGB-D scene recognition: learning from scratch better than transferring from RGB-CNNs
However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features.
Empirical Analysis of Foundational Distinctions in Linked Open Data
For example, distinctions such as whether an entity is inherently a class or an individual, or whether it is a physical object or not, are hardly expressed in the data, although they have been largely studied and formalised by foundational ontologies (e. g. DOLCE, SUMO).
Fast and Accurate Point Cloud Registration using Trees of Gaussian Mixtures
Point cloud registration sits at the core of many important and challenging 3D perception problems including autonomous navigation, SLAM, object/scene recognition, and augmented reality.
From Volcano to Toyshop: Adaptive Discriminative Region Discovery for Scene Recognition
As deep learning approaches to scene recognition emerge, they have continued to leverage discriminative regions at multiple scales, building on practices established by conventional image classification research.
Self-Supervised Model Adaptation for Multimodal Semantic Segmentation
To address this limitation, we propose a mutimodal semantic segmentation framework that dynamically adapts the fusion of modality-specific features while being sensitive to the object category, spatial location and scene context in a self-supervised manner.
Plugin Networks for Inference under Partial Evidence
In this paper, we propose a novel method to incorporate partial evidence in the inference of deep convolutional neural networks.
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision
Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry.
Local Aggregation for Unsupervised Learning of Visual Embeddings
neuroailab/LocalAggregation-Pytorch
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ICCV 2019
Unsupervised approaches to learning in neural networks are of substantial interest for furthering artificial intelligence, both because they would enable the training of networks without the need for large numbers of expensive annotations, and because they would be better models of the kind of general-purpose learning deployed by humans.
