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Found 28 papers, 6 papers with code
Multistage Fusion with Forget Gate for Multimodal Summarization in Open-Domain Videos
Multimodal summarization for open-domain videos is an emerging task, aiming to generate a summary from multisource information (video, audio, transcript).
Learning Invariant Visual Representations for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set.
Compositional Zero-Shot Learning
Out-of-Distribution Generalization
Span-level Bidirectional Cross-attention Framework for Aspect Sentiment Triplet Extraction
With differentiated span representations and bidirectional decoding structure, our model can extract sentiment triplets more precisely and efficiently.
Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval
Our model adapts to multi-scale feature inputs, favors multi-source retrieval methods, and can dynamically filter redundant features.
Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information
In this article, we first propose a novel RSCTIR framework based on global and local information (GaLR), and design a multi-level information dynamic fusion (MIDF) module to efficaciously integrate features of different levels.
Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders
Due to the unicity of receptive field, semantic segmentation of point clouds remains challenging for the expression of multi-receptive field features, which brings about the misclassification of instances with similar spatial structures.
Optical Flow Training under Limited Label Budget via Active Learning
Our experiments on both synthetic and real optical flow datasets show that our semi-supervised networks generally need around 50% of the labels to achieve close to full-label accuracy, and only around 20% with active learning on Sintel.
Learning by Active Forgetting for Neural Networks
This paper presents a learning model by active forgetting mechanism with artificial neural networks.
Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction
Implicit Event Argument Extraction seeks to identify arguments that play direct or implicit roles in a given event.
1213Li at SemEval-2021 Task 6: Detection of Propaganda with Multi-modal Attention and Pre-trained Models
This paper presents the solution proposed by the 1213Li team for subtask 3 in SemEval-2021 Task 6: identifying the multiple persuasion techniques used in the multi-modal content of the meme.
Double Similarity Distillation for Semantic Image Segmentation
In this paper, motivated by the residual learning and global aggregation, we propose a simple yet general and effective knowledge distillation framework called double similarity distillation (DSD) to improve the classification accuracy of all existing compact networks by capturing the similarity knowledge in pixel and category dimensions, respectively.
Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification
Then, attention mechanisms are used after feature fusion to extract spatial and channel information while linking the high-level semantic information and the low-level texture features, which can better locate the discriminative regions for the FGVC.
HYPER^2: Hyperbolic Poincare Embedding for Hyper-Relational Link Prediction
Link Prediction, addressing the issue of completing KGs with missing facts, has been broadly studied.
FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery
In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 15, 000 images for Fine-grAined object recognItion in high-Resolution remote sensing imagery which is named as FAIR1M.
dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference
dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates.
AF-EMS Detector: Improve the Multi-Scale Detection Per- formance of the Anchor-Free Detector
With the objects to be detected becoming more complex, the problem of multi-scale object detection has attracted more and more attention, especially in the field of remote sensing detection.
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.
A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms
Performance predictors are a type of regression models which can assist to accomplish the search, while without exerting much computational resource.
Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images
Semantic segmentation in very high resolution (VHR) aerial images is one of the most challenging tasks in remote sensing image understanding.
Oriented Objects as pairs of Middle Lines
It is noteworthy that the objects in COCO can be regard as a special form of oriented objects with an angle of 90 degrees.
Ship Instance Segmentation From Remote Sensing Images Using Sequence Local Context Module
The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neural network.
Comparison Network for One-Shot Conditional Object Detection
A two-stage detector for OSCD is introduced to compare the extracted query and target features with the learnable metric to approach the optimized non-linear conditional probability.
A Training-free, One-shot Detection Framework For Geospatial Objects In Remote Sensing Images
Deep learning based object detection has achieved great success.
A Remote Sensing Image Dataset for Cloud Removal
Removing clouds is an indispensable pre-processing step in remote sensing image analysis.
Wider Channel Attention Network for Remote Sensing Image Super-resolution
In this letter, we propose a novel single-image super-resolution (SISR) algorithm named Wider Channel Attention Network (WCAN) for remote sensing images.
Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network
The complexity of application scenarios, the redundancy of detection region, and the difficulty of dense ship detection are all the main obstacles that limit the successful operation of traditional methods in ship detection.
Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall.
MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification
With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs).
