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Found 13 papers, 11 papers with code
ConSept: Continual Semantic Segmentation via Adapter-based Vision Transformer
Empirical investigations on the adaptation of existing frameworks to vanilla ViT reveal that incorporating visual adapters into ViTs or fine-tuning ViTs with distillation terms is advantageous for enhancing the segmentation capability of novel classes.
FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models
Few-shot class-incremental learning (FSCIL) aims to mitigate the catastrophic forgetting issue when a model is incrementally trained on limited data.
Dimensionality Reduction
Few-Shot Class-Incremental Learning
+2
DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection
Most existing data augmentation methods tend to find a compromise in augmenting the data, \textit{i. e.}, increasing the amplitude of augmentation carefully to avoid degrading some data too much and doing harm to the model performance.
UniM$^2$AE: Multi-modal Masked Autoencoders with Unified 3D Representation for 3D Perception in Autonomous Driving
The extension makes it possible to back-project the informative features, obtained by fusing features from both modalities, into their native modalities to reconstruct the multiple masked inputs.
Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation
This problem has been widely investigated in the research community and several Incremental Learning (IL) approaches have been proposed in the past years.
Continual Attentive Fusion for Incremental Learning in Semantic Segmentation
To fill this gap, in this paper we introduce a novel attentive feature distillation approach to mitigate catastrophic forgetting while accounting for semantic spatial- and channel-level dependencies.
Global and Local Alignment Networks for Unpaired Image-to-Image Translation
The global alignment network aims to transfer the input image from the source domain to the target domain.
Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation
Specifically, in the image translation stage, Bi-Mix leverages the knowledge of day-night image pairs to improve the quality of nighttime image relighting.
Transformer-Based Source-Free Domain Adaptation
In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation.
Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction
While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range dependencies due to the intrinsic locality of the convolution operation.
Ranked #8 on
Depth Estimation
on NYU-Depth V2
Variational Structured Attention Networks for Deep Visual Representation Learning
Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework, leading to Variational STructured Attention networks (VISTA-Net).
Variational Structured Attention Networks for Dense Pixel-Wise Prediction
State-of-the-art performances in dense pixel-wise prediction tasks are obtained with specifically designed convolutional networks.
Bi-Directional Generation for Unsupervised Domain Adaptation
To balance the mitigation of domain gap and the preservation of the inherent structure, we propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains.
