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++
Latest papers with no code
EnTri: Ensemble Learning with Tri-level Representations for Explainable Scene Recognition
Through experiments on benchmark scene classification datasets, EnTri has demonstrated superiority in terms of recognition accuracy, achieving competitive performance compared to state-of-the-art approaches, with an accuracy of 87. 69%, 75. 56%, and 99. 17% on the MIT67, SUN397, and UIUC8 datasets, respectively.
Multi-query Vehicle Re-identification: Viewpoint-conditioned Network, Unified Dataset and New Metric
Existing vehicle re-identification methods mainly rely on the single query, which has limited information for vehicle representation and thus significantly hinders the performance of vehicle Re-ID in complicated surveillance networks.
Semantic-guided modeling of spatial relation and object co-occurrence for indoor scene recognition
Firstly, the Semantic Spatial Relation Module (SSRM) is constructed to model scene spatial features.
GeoNet: Benchmarking Unsupervised Adaptation across Geographies
In recent years, several efforts have been aimed at improving the robustness of vision models to domains and environments unseen during training.
Synergy between human and machine approaches to sound/scene recognition and processing: An overview of ICASSP special session
Machine Listening, as usually formalized, attempts to perform a task that is, from our perspective, fundamentally human-performable, and performed by humans.
Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification
Then, to build multi-scale hierarchical information of input features, we utilize an attention fusion mechanism to aggregate features from multiple layers of the backbone network.
Coarse-to-fine Task-driven Inpainting for Geoscience Images
To the best of our knowledge, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images by ignoring the geoscience related tasks.
Ambiguity-Aware Multi-Object Pose Optimization for Visually-Assisted Robot Manipulation
Tackling these limitations, we present an ambiguity-aware 6D object pose estimation network, PrimA6D++, as a generic uncertainty prediction method.
Fast and Efficient Scene Categorization for Autonomous Driving using VAEs
We train a Variational Autoencoder in an unsupervised manner and map images to a constrained multi-dimensional latent space and use the latent vectors as compact embeddings that serve as global descriptors for images.
Rice Leaf Disease Classification and Detection Using YOLOv5
We have annotate 1500 collected data sets and propose a rice leaf disease classification and detection method based on YOLOv5 deep learning.
