Segment objects in videos using only 2 labeled frames with Two-shot-Video-Object-Segmentation Two-shot Video Object Segmentation arXiv paper abstract https://arxiv.org/abs/2303.12078 arXiv PDF paper https://arxiv.org/pdf/2303.12078.pdf Previous works on video object segmentation (VOS) are trained on densely annotated videos … acquiring annotations in pixel level is expensive and time-consuming. … demonstrate … two…

Detect all known and unknown objects using many sources and probability calibration with UniDetector Detecting Everything in the Open World: Towards Universal Object Detection arXiv paper abstract https://arxiv.org/abs/2303.11749 arXiv PDF paper https://arxiv.org/pdf/2303.11749.pdf … address universal object detection, which aims to detect every scene and predict every category … propose UniDetector…

Get 3D shape of novel object from one image using pre-trained diffusion models with Zero-1-to-3 Zero-1-to-3: Zero-shot One Image to 3D Object arXiv paper abstract https://arxiv.org/abs/2303.11328 arXiv PDF paper https://arxiv.org/pdf/2303.11328.pdf … introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image. To…

Vision transformer beats CNN on mobile devices for accuracy and speed with ElasticViT ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile Devices arXiv paper abstract https://arxiv.org/abs/2303.09730 arXiv PDF paper https://arxiv.org/pdf/2303.09730.pdf … designing lightweight and low-latency ViT models for diverse mobile devices remains a big challenge.

Get 3D surface from Neural Radiance Field using signed surface approximation with NeRFMeshing NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes arXiv paper abstract https://arxiv.org/abs/2303.09431 arXiv PDF paper https://arxiv.org/pdf/2303.09431.pdf … Neural Radiance Fields (NeRFs) … proposes that each 3D point can emit radiance, allowing to conduct view synthesis using…

Survey of visual recognition with deep learning on new domains using only a few examples Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey arXiv paper abstract https://arxiv.org/abs/2303.08557 arXiv PDF paper https://arxiv.org/pdf/2303.08557.pdf Deep learning has been highly successful in computer vision with large amounts of labeled data, but struggles with…

Get absolute depth in monocular self-supervised depth estimation by global scale factor with Dana One scalar is all you need — absolute depth estimation using monocular self-supervision arXiv paper abstract https://arxiv.org/abs/2303.07662 arXiv PDF paper (starts on page 3) https://arxiv.org/pdf/2303.07662.pdf Self-supervised monocular depth estimators can be trained or fine-tuned on new…

3D object detection boxes directly from image and point data using multi-modal features with CMT Cross Modal Transformer: Towards Fast and Robust 3D Object Detection arXiv paper abstract https://arxiv.org/abs/2301.01283 arXiv PDF paper https://arxiv.org/pdf/2301.01283.pdf GitHub https://github.com/junjie18/cmt … propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal…

Segmentation with only a few examples by using image captions instead of pixel labels with IMR-HSNet Iterative Few-shot Semantic Segmentation from Image Label Text arXiv paper abstract https://arxiv.org/abs/2303.05646 arXiv PDF paper https://arxiv.org/pdf/2303.05646.pdf GitHub https://github.com/whileherham/imr-hsnet Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only…