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Found 25 papers, 14 papers with code
Rˆ3Net:Relation-embedded Representation Reconstruction Network for Change Captioning
In this paper, we propose a Relation-embedded Representation Reconstruction Network (Rˆ3Net) to explicitly distinguish the real change from the large amount of clutter and irrelevant changes.
Rethinking Boundary Discontinuity Problem for Oriented Object Detection
Oriented object detection has been developed rapidly in the past few years, where rotation equivariant is crucial for detectors to predict rotated bounding boxes.
Gaussian Label Distribution Learning for Spherical Image Object Detection
Therefore, we propose GLDL-ATSS as a better training sample selection strategy for objects of the spherical image, which can alleviate the drawback of IoU threshold-based strategy of scale-sample imbalance.
ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting
In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input.
Learning Cross-view Geo-localization Embeddings via Dynamic Weighted Decorrelation Regularization
Cross-view geo-localization aims to spot images of the same location shot from two platforms, e. g., the drone platform and the satellite platform.
Gait Recognition in the Wild with Multi-hop Temporal Switch
Current methods that obtain state-of-the-art performance on in-the-lab benchmarks achieve much worse accuracy on the recently proposed in-the-wild datasets because these methods can hardly model the varied temporal dynamics of gait sequences in unconstrained scenes.
Multi-task Optimization Based Co-training for Electricity Consumption Prediction
The performance of MTO-CT is evaluated on solving each of these two sets of tasks in comparison to solving each task in the set independently without knowledge sharing under the same settings, which demonstrates the superiority of MTO-CT in terms of prediction accuracy.
Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference
Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions.
Multiple-environment Self-adaptive Network for Aerial-view Geo-localization
This task is mostly regarded as an image retrieval problem.
Gait Recognition in the Wild with Dense 3D Representations and A Benchmark
Based on Gait3D, we comprehensively compare our method with existing gait recognition approaches, which reflects the superior performance of our framework and the potential of 3D representations for gait recognition in the wild.
Ranked #1 on
Gait Recognition
on Gait3D
R$^3$Net:Relation-embedded Representation Reconstruction Network for Change Captioning
In this paper, we propose a Relation-embedded Representation Reconstruction Network (R$^3$Net) to explicitly distinguish the real change from the large amount of clutter and irrelevant changes.
Unbiased IoU for Spherical Image Object Detection
In this paper, we first identify that spherical rectangles are unbiased bounding boxes for objects in spherical images, and then propose an analytical method for IoU calculation without any approximations.
TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition
Despite significant improvement in gait recognition with deep learning, existing studies still neglect a more practical but challenging scenario -- unsupervised cross-domain gait recognition which aims to learn a model on a labeled dataset then adapts it to an unlabeled dataset.
Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans
The experimental results show that our automatically searched models (CovidNet3D) outperform the baseline human-designed models on the three datasets with tens of times smaller model size and higher accuracy.
Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas.
Ranked #1 on
Image-Based Localization
on cvact
Depth image denoising using nuclear norm and learning graph model
The depth images denoising are increasingly becoming the hot research topic nowadays because they reflect the three-dimensional (3D) scene and can be applied in various fields of computer vision.
Unsupervised Person Re-identification via Softened Similarity Learning
Person re-identification (re-ID) is an important topic in computer vision.
Deep Multi-View Enhancement Hashing for Image Retrieval
Therefore, we try to introduce the multi-view deep neural network into the hash learning field, and design an efficient and innovative retrieval model, which has achieved a significant improvement in retrieval performance.
Cascaded Revision Network for Novel Object Captioning
By this novel cascaded captioning-revising mechanism, CRN can accurately describe images with unseen objects.
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization
It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i. e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices.
Image Classification base on PCA of Multi-view Deep Representation
In the age of information explosion, image classification is the key technology of dealing with and organizing a large number of image data.
Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks
With asymptotic pruning, the information of the training set would be gradually concentrated in the remaining filters, so the subsequent training and pruning process would be stable.
Memory Matching Networks for One-Shot Image Recognition
In this paper, we introduce the new ideas of augmenting Convolutional Neural Networks (CNNs) with Memory and learning to learn the network parameters for the unlabelled images on the fly in one-shot learning.
Improving Person Re-identification by Attribute and Identity Learning
Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions.
Ranked #73 on
Person Re-Identification
on DukeMTMC-reID
