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Found 16 papers, 9 papers with code
Boosting Long-tailed Object Detection via Step-wise Learning on Smooth-tail Data
Real-world data tends to follow a long-tailed distribution, where the class imbalance results in dominance of the head classes during training.
Open World DETR: Transformer based Open World Object Detection
Open world object detection aims at detecting objects that are absent in the object classes of the training data as unknown objects without explicit supervision.
Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning
Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes.
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.
Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection
In view of this limitation, we consider a more practical setting of complete absence of co-occurrence of the base and novel classes for the object detection task.
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.
SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network
In the MTGAN, the generator is a super-resolution network, which can up-sample small blurred images into fine-scale ones and recover detailed information for more accurate detection.
W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection
Finally, we use these pseudo ground-truths to train a fully-supervised detector.
Finding Tiny Faces in the Wild With Generative Adversarial Network
In this paper, we proposed an algorithm to directly generate a clear high-resolution face from a blurry small one by adopting a generative adversarial network (GAN).
