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Found 20 papers, 5 papers with code
Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification
In particular, our method minimizes the cross-modal gap by identifying and aligning shared prototypes that capture key discriminative features across modalities, then uses multiple bridging steps based on this information to enhance the feature representation.
SeTformer is What You Need for Vision and Language
Kernel methods are employed to simplify computations by approximating softmax but often lead to performance drops compared to softmax attention.
Efficient Object Detection in Optical Remote Sensing Imagery via Attention-based Feature Distillation
To address the aforementioned challenges, we propose Attention-based Feature Distillation (AFD), a new KD approach that distills both local and global information from the teacher detector.
Distance Weighted Trans Network for Image Completion
The challenge of image generation has been effectively modeled as a problem of structure priors or transformation.
ClusVPR: Efficient Visual Place Recognition with Clustering-based Weighted Transformer
VPR is particularly difficult due to the presence of duplicate regions and the lack of attention to small objects in complex scenes, resulting in recognition deviations.
Entropy Transformer Networks: A Learning Approach via Tangent Bundle Data Manifold
This paper focuses on an accurate and fast interpolation approach for image transformation employed in the design of CNN architectures.
Adaptive Generation of Privileged Intermediate Information for Visible-Infrared Person Re-Identification
In this paper, the Adaptive Generation of Privileged Intermediate Information training approach is introduced to adapt and generate a virtual domain that bridges discriminant information between the V and I modalities.
Image Completion via Dual-path Cooperative Filtering
Given the recent advances with image-generating algorithms, deep image completion methods have made significant progress.
VTAE: Variational Transformer Autoencoder with Manifolds Learning
This weak projection, however, can be addressed by a Riemannian metric, and we show that geodesics computation and accurate interpolations between data samples on the Riemannian manifold can substantially improve the performance of deep generative models.
Hybrid Gromov-Wasserstein Embedding for Capsule Learning
Capsule networks (CapsNets) aim to parse images into a hierarchy of objects, parts, and their relations using a two-step process involving part-whole transformation and hierarchical component routing.
Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network
Skin lesion detection in dermoscopic images is essential in the accurate and early diagnosis of skin cancer by a computerized apparatus.
Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images
In IPSSD, single-shot detector is adopted combined with an image pyramid network to extract semantically strong features for generating candidate regions.
Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images
Second, most of the standard methods used hand-crafted features, and do not work well on the detection of objects parts of which are missing.
Rotation Equivariant Feature Image Pyramid Network for Object Detection in Optical Remote Sensing Imagery
The proposed model adopts single-shot detector in parallel with a lightweight image pyramid module to extract representative features and generate regions of interest in an optimization approach.
Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies
However, there is a lack of comprehensive review in this field, especially lack of a collection of GANs loss-variant, evaluation metrics, remedies for diverse image generation, and stable training.
Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks
We also propose a feature pyramid network that improves the performance of the proposed model by extracting effective features from all the layers of the network for describing different scales objects.
Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis
In particular, we restore the balance in the imbalanced dataset by generating faulty samples from the proposed mixture of data distribution.
Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks
It improves learning from imbalanced data by incorporating the majority distribution structure in the generation of new minority samples.
AMIL: Adversarial Multi Instance Learning for Human Pose Estimation
The proposed adversarial residual multi-instance neural network that is based on pooling has been validated on two datasets for the human pose estimation task and successfully outperforms the other state-of-arts models.
A novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images
This paper presents a novel deep neural network structure for pixel-wise sea-land segmentation, a Residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images.
