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Found 14 papers, 7 papers with code
Relating CNN-Transformer Fusion Network for Change Detection
While deep learning, particularly convolutional neural networks (CNNs), has revolutionized remote sensing (RS) change detection (CD), existing approaches often miss crucial features due to neglecting global context and incomplete change learning.
Ranked #2 on
Change Detection
on SYSU-CD
Foster Adaptivity and Balance in Learning with Noisy Labels
Moreover, existing methods tend to neglect the class balance in selecting samples, leading to biased model performance.
Knowledge Transfer with Simulated Inter-Image Erasing for Weakly Supervised Semantic Segmentation
Considering the adopted bidirectional alignment will also weaken the anchor image activation if appropriate constraints are missing, we propose a self-supervised regularization module to maintain the reliable activation in discriminative regions and improve the inter-class object boundary recognition for complex images with multiple categories of objects.
Poly Kernel Inception Network for Remote Sensing Detection
Object detection in remote sensing images (RSIs) often suffers from several increasing challenges, including the large variation in object scales and the diverse-ranging context.
VideoMAC: Video Masked Autoencoders Meet ConvNets
In this paper, we propose a new approach termed as \textbf{VideoMAC}, which combines video masked autoencoders with resource-friendly ConvNets.
Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection
Specifically, we propose Class-Balance-based sample Selection (CBS) to prevent the tail class samples from being neglected during training.
Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning
There has been significant attention devoted to the effectiveness of various domains, such as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the performance of methods for noisy label learning (NLL) tasks.
Attention Map Guided Transformer Pruning for Edge Device
Then we combine the similarity and first-order gradients of key tokens along the query dimension for token importance estimation and remove redundant key and value tokens to further reduce the inference complexity.
PNP: Robust Learning From Noisy Labels by Probabilistic Noise Prediction
Label noise has been a practical challenge in deep learning due to the strong capability of deep neural networks in fitting all training data.
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets.
Jo-SRC: A Contrastive Approach for Combating Noisy Labels
Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance.
Salvage Reusable Samples from Noisy Data for Robust Learning
To this end, we propose a certainty-based reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images.
Dynamically Visual Disambiguation of Keyword-based Image Search
To address this issue, we present an adaptive multi-model framework that resolves polysemy by visual disambiguation.
Road Segmentation with Image-LiDAR Data Fusion
Robust road segmentation is a key challenge in self-driving research.
