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Found 18 papers, 5 papers with code
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
Finally, to decrease the intra-domain gap, we propose to employ a self-supervised adaptation technique from the easy to the hard split.
Ranked #2 on
Domain Adaptation
on Synscapes-to-Cityscapes
Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation
However, applying this approach in image translation is computationally intensive and error-prone due to the expensive time complexity and its non-trivial backpropagation.
MaXTron: Mask Transformer with Trajectory Attention for Video Panoptic Segmentation
To alleviate the issue, we propose to adapt the trajectory attention for both the dense pixel features and object queries, aiming to improve the short-term and long-term tracking results, respectively.
Ranked #1 on
Video Panoptic Segmentation
on VIPSeg
MATE: Masked Autoencoders are Online 3D Test-Time Learners
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data.
Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging
In this study, we present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes.
Learning Representations by Contrasting Clusters While Bootstrapping Instances
Learning visual representations using large-scale unlabelled images is a holy grail for most of computer vision tasks.
Two-phase Pseudo Label Densification for Self-training based Domain Adaptation
However, since only the confident predictions are taken as pseudo labels, existing self-training approaches inevitably produce sparse pseudo labels in practice.
Unsupervised Domain Adaptation for Video Semantic Segmentation
In this work, we present a new video extension of this task, namely Unsupervised Domain Adaptation for Video Semantic Segmentation.
LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation
In order to find the uncertain points, we generate an inconsistency mask using the proposed adaptive pixel selector and we label these segment-based regions to achieve near supervised performance with only a small fraction (about 2. 2%) ground truth points, which we call "Segment based Pixel-Labeling (SPL)".
Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation
The scheme first clusters compound target data based on style, discovering multiple latent domains (discover).
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation
Inspired by recent multi-modal UDA techniques, the proposed method exploits a teacher-student self-supervised learning scheme to train a pose estimation network without using target domain pose labels.
Ranked #5 on
6D Pose Estimation using RGBD
on REAL275
MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation
In this paper, we propose and explore a new multi-modal extension of test-time adaptation for 3D semantic segmentation.
Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management
In addition, to prevent overfitting of the TTA model, we devise novel regularization which modulates the adaptation rates using domain-similarity between the source and the current target domain.
Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation
To successfully train our framework, we collect the partial, confident target samples that are classified as known or unknown through on our proposed multi-criteria selection.
Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation
In specific, we achieve domain mixup in two-step: cut and paste.
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
Unlike previous unsupervised domain adaptation methods for category-level object pose estimation, our approach processes the test data in a sequential, online manner, and it does not require access to the source domain at runtime.
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation
The meta architecture of the proposed Video-kMaX consists of two components: within clip segmenter (for clip-level segmentation) and cross-clip associater (for association beyond clips).
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark
Multi-target multi-camera tracking is a crucial task that involves identifying and tracking individuals over time using video streams from multiple cameras.
