This paper addresses the vulnerability of DNNs used in robotic semantic segmentation to adversarial attacks by proposing specialized detection strategies to enhance safety in robotic perception system…

The paper introduces a novel privacy-preserving semantic segmentation method that enables model training and inference using independently encrypted images for each client and image.

The paper proposes Energy-Aware NECO, a single-pass hybrid detector that combines geometric ratio and logit-based energy scores to achieve superior pixel-wise out-of-distribution detection for semanti…

This paper systematically revisits and expands the threat model for backdoor attacks on semantic segmentation, proposing a unified framework (BADSEG) that demonstrates severe, previously overlooked vu…

LALE introduces a novel lightweight architecture that efficiently combines local convolutional features and global transformer context for land-cover segmentation, achieving superior efficiency and pe…

This paper proposes an uncertainty-guided synthetic context augmentation strategy for semantic segmentation models to improve performance on complex datasets with data sparsity and rare or visually di…

The paper proposes a unified framework to systematically redefine instance matching for Panoptic Quality evaluation, moving beyond the standard One-to-One matching to accommodate complex scenarios lik…

This paper proposes a STLSF module for echocardiography segmentation using a semantic correction component and texture enhancement, along with a frequency-aware denoising pre-training method.

CobSeg introduces a multi-branch architecture that enhances dialogue topic segmentation by explicitly modeling both semantic coherence and local lexical boundary transitions, achieving state-of-the-ar…

This paper proposes PairGS, a framework for open-vocabulary 3D Gaussian segmentation that models pairwise relations between Gaussians using rich signals from 3D Gaussian representations.

The paper presents SenseNova-Vision, a unified multimodal model for computer vision tasks using natural language instructions and optional visual prompts, trained primarily on a new corpus and requiri…

The paper proposes xModel-KD, a cross-modal knowledge distillation framework, to improve 3D point cloud segmentation by effectively transferring rich appearance cues from 2D images to sparse 3D geomet…

The paper introduces Multi-temporal Referring Segmentation (MTRS), a new task requiring models to segment language-described temporal changes, and proposes MTRefSeg-R1, a specialized framework that ac…

GeoSAM-3D proposes a novel framework for open-vocabulary 3D scene segmentation from simple monocular video by propagating object prompts using a geodesic distance kernel on a reconstructed Gaussian sc…

The paper introduces a novel two-stage framework to achieve robust, category-agnostic object localization in-context (ICL) by optimizing attention and minimizing localization error using reinforcement…

The paper shows that simple, non-architectural enhancements, such as adding semantic pseudo-labels and visibility information, can significantly boost Lidar Semantic Scene Completion performance.

This paper addresses robot localization in GPS-denied indoor environments using a semantic reasoning approach with a vision-language model, achieving high accuracy with a composite loss and curriculum…