Survey of methods for handling distribution shifts for robust computer vision Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts arXiv paper abstract https://arxiv.org/abs/2312.01540 arXiv PDF paper https://arxiv.org/pdf/2312.01540.pdf … There is … gap between … computer vision models and … the real world. …

Get 3D object from monocular RGB-D video using diffusion prior with MorpheuS MorpheuS: Neural Dynamic 360° Surface Reconstruction from Monocular RGB-D Video arXiv paper abstract https://arxiv.org/abs/2312.00778 arXiv PDF paper https://arxiv.org/pdf/2312.00778.pdf … neural representations … can accurately capture the motion and achieve high-fidelity reconstruction of the target object. Despite this, real-world…

Segment areas, objects, and parts in a scene at the same time with JPPF JPPF: Multi-task Fusion for Consistent Panoptic-Part Segmentation arXiv paper abstract https://arxiv.org/abs/2311.18618 arXiv PDF paper https://arxiv.org/pdf/2311.18618.pdf Part-aware panoptic segmentation is a problem of computer vision that aims to provide a semantic understanding of the scene at multiple…

Segment scene using information from vision-language models without neural training with PnP-OVSS Plug-and-Play, Dense-Label-Free Extraction of Open-Vocabulary Semantic Segmentation from Vision-Language Models arXiv paper abstract https://arxiv.org/abs/2311.17095 arXiv PDF paper https://arxiv.org/pdf/2311.17095.pdf From an enormous amount of image-text pairs, large-scale vision-language models (VLMs) learn to implicitly associate image regions with words

Segment scene using vision-language models for diverse semantic knowledge with SemiVL SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance arXiv paper abstract https://arxiv.org/abs/2311.16241 arXiv PDF paper https://arxiv.org/pdf/2311.16241.pdf GitHub https://github.com/google-research/semivl In semi-supervised semantic segmentation, a model is trained with a limited number of labeled images along with a large corpus of unlabeled…

Segment image into open set of categories using cost from a hierarchical encoder with SED SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation arXiv paper abstract https://arxiv.org/abs/2311.15537 arXiv PDF paper https://arxiv.org/pdf/2311.15537.pdf Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic groups from an open set of categories. … propose…

Segment image with one example using model correspondence between example and target with SEGIC SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation arXiv paper abstract https://arxiv.org/abs/2311.14671 arXiv PDF paper https://arxiv.org/pdf/2311.14671.pdf In-context segmentation aims at segmenting novel images using a few labeled example images, termed as “in-context examples”, exploring content similarities…

Real-time object pose from one RGB-D image using hierarchical binary surface encoding with HiPose HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation arXiv paper abstract https://arxiv.org/abs/2311.12588 arXiv PDF paper https://arxiv.org/pdf/2311.12588.pdf … present a novel dense-correspondence method for 6DoF object pose estimation from a single…

Detect unknown and known objects using CLIP, SAM, and GDINO with cooperative-foundational-models Enhancing Novel Object Detection via Cooperative Foundational Models arXiv paper abstract https://arxiv.org/abs/2311.12068 arXiv PDF paper https://arxiv.org/pdf/2311.12068.pdf … address the challenging and emergent problem of novel object detection (NOD), focusing on the accurate detection of both known and novel…