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Found 18 papers, 6 papers with code
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene Classification
Besides, pioneer ZSL models use convolutional neural networks pre-trained on ImageNet, which focus on the main objects appearing in each image, neglecting the background context that also matters in RS scene classification.
Jointly Optimized Global-Local Visual Localization of UAVs
Our GLVL network is a two-stage visual localization approach, combining a large-scale retrieval module that finds similar regions with the UAV flight scene, and a fine-grained matching module that localizes the precise UAV coordinate, enabling real-time and precise localization.
ARAI-MVSNet: A multi-view stereo depth estimation network with adaptive depth range and depth interval
Moreover, our method also achieves the lowest $e_{1}$ and $e_{3}$ on the BlendedMVS dataset and the highest Acc and $F_{1}$-score on the ETH 3D dataset, surpassing all listed methods. Project website: https://github. com/zs670980918/ARAI-MVSNet
Inferring High-level Geographical Concepts via Knowledge Graph and Multi-scale Data Integration: A Case Study of C-shaped Building Pattern Recognition
To improve efficiency and effectiveness, we integrate multi-scale data using a knowledge graph, focusing on the recognition of C-shaped building patterns.
Distinctive Image Captioning via CLIP Guided Group Optimization
Image captioning models are usually trained according to human annotated ground-truth captions, which could generate accurate but generic captions.
Learning Prototype via Placeholder for Zero-shot Recognition
Specifically, we combine seen classes to hallucinate new classes which play as placeholders of the unseen classes in the visual and semantic space.
Multi-dimension Geospatial feature learning for urban region function recognition
In this paper, we propose a Multi-dimension Feature Learning Model~(MDFL) using high-dimensional GBD data in conjunction with RS images for urban region function recognition.
On Distinctive Image Captioning via Comparing and Reweighting
First, we propose a distinctiveness metric -- between-set CIDEr (CIDErBtw) to evaluate the distinctiveness of a caption with respect to those of similar images.
Attribute Prototype Network for Any-Shot Learning
While a visual-semantic embedding layer learns global features, local features are learned through an attribute prototype network that simultaneously regresses and decorrelates attributes from intermediate features.
Ranked #5 on
GZSL Video Classification
on ActivityNet-GZSL(main)
VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning
Our model visually divides a set of images from seen classes into clusters of local image regions according to their visual similarity, and further imposes their class discrimination and semantic relatedness.
Human Attention in Fine-grained Classification
The way humans attend to, process and classify a given image has the potential to vastly benefit the performance of deep learning models.
Ranked #41 on
Fine-Grained Image Classification
on CUB-200-2011
Group-based Distinctive Image Captioning with Memory Attention
In particular, we propose a group-based memory attention (GMA) module, which stores object features that are unique among the image group (i. e., with low similarity to objects in other images).
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network
Single image super-resolution is an effective way to enhance the spatial resolution of remote sensing image, which is crucial for many applications such as target detection and image classification.
Where is the Model Looking At?--Concentrate and Explain the Network Attention
We generate attribute-based textual explanations for the network and ground the attributes on the image to show visual explanations.
ASTRAL: Adversarial Trained LSTM-CNN for Named Entity Recognition
Under such circumstances, how to make full use of the information extracted by word embedding requires more in-depth research.
SRQA: Synthetic Reader for Factoid Question Answering
This model enhances the question answering system in the multi-document scenario from three aspects: model structure, optimization goal, and training method, corresponding to Multilayer Attention (MA), Cross Evidence (CE), and Adversarial Training (AT) respectively.
Attribute Prototype Network for Zero-Shot Learning
As an additional benefit, our model points to the visual evidence of the attributes in an image, e. g. for the CUB dataset, confirming the improved attribute localization ability of our image representation.
Compare and Reweight: Distinctive Image Captioning Using Similar Images Sets
A wide range of image captioning models has been developed, achieving significant improvement based on popular metrics, such as BLEU, CIDEr, and SPICE.
