Computer Vision

First unsupervised object detection method with theoretical guarantees to recover true object positions, up to quantifiable small shifts!

Modality Adaptation with Diffusion Models (MADM) achieves state-of-the-art semantic segmentation by using pre-trained text-to-image diffusion models to enhance cross-modality capabilities and generate…

AltO: a novel unsupervised learning framework for accurately estimating homography from multimodal image pairs, achieving performance comparable to supervised methods.

This study introduces a novel unsupervised hierarchy-agnostic image segmentation method achieving detailed and unbiased parsing of semantic image structures across various datasets.

UnSeg: One universal unlearnable example generator protects images from image segmentation model training.

Self-guidance boosts masked generative models’ image synthesis, achieving superior quality and diversity with fewer steps!

DiffewS: a novel framework leverages diffusion models for few-shot semantic segmentation, significantly outperforming existing methods in multiple settings.

ProjDiff: A novel algorithm unleashes diffusion models’ denoising power for superior inverse problem solutions.

New MVUAV dataset and SemiMV semi-supervised learning model significantly improve multispectral video semantic segmentation!

New unlearnable framework secures 3D point cloud data by using class-wise transformations, enabling authorized training while preventing unauthorized access.

UniSDF: Unifying neural representations reconstructs complex scenes with reflections, achieving state-of-the-art performance by blending camera and reflected view radiance fields.

Unique3D: Single image to high-fidelity 3D mesh in 30 seconds!

UNION: Unsupervised 3D object detection method doubles average precision, leveraging LiDAR, camera, and temporal data for efficient training without manual labels.

UniFL: Unified Feedback Learning revolutionizes latent diffusion models by improving image quality, aesthetics, and inference speed through a unified feedback learning framework, surpassing existing m…

Unified Domain Generalization and Adaptation (UDGA) tackles 3D object detection’s domain adaptation challenges by leveraging multi-view overlap and label-efficient learning, achieving state-of-the-art…

UniDSeg uses Visual Foundation Models to create a unified framework for adaptable and generalizable cross-domain 3D semantic segmentation, achieving state-of-the-art results.

Deep learning models favor simple features, hindering generalization; this paper introduces a new feature complexity metric revealing a spectrum of simple-to-complex features, their learning dynamics,…

PartCLIPSeg, a novel framework, leverages generalized parts and object-level contexts to achieve significant improvements in open-vocabulary part segmentation, outperforming state-of-the-art methods.

Diffusion models generate unrealistic images by smoothly interpolating between data modes; this paper identifies this ‘mode interpolation’ failure and proposes a metric to detect and reduce it.

Researchers unveil a novel framework to dissect bias in large-scale visual datasets, identifying unique visual attributes and leveraging language models for detailed analysis, paving the way for creat…