Evaluation metrics are central to binary target segmentation because they determine how progress is measured, compared, and interpreted. In this paper, target denotes the task-defined positive region to be segmented rather than a generic foreground object.
arXiv:2609.10495v1 Announce Type: new Abstract: In real-time colonoscopy, ground-truth annotations are unavailable at inference, so polyp segmentation models can fail silently. We propose Referee-Bas...
VTOS (Vision Tools Orchestration Search) is a framework that adaptively orchestrates vision foundation tools—such as open‑vocabulary detectors, segmentation models, and post‑processing operators—by jointly searching for executable solution programs and observer programs that diagnose failures and provide feedback. The observer programs feed observations into a shared VisionThoughts knowledge base, guiding subsequent searches. In two case studies—dense object counting on LVIS‑Count and zero‑shot plant‑disease segmentation on PlantSeg‑OOD—VTOS outperforms static tool pipelines and agentic visual‑programming baselines, especially in complex scenarios like dense, occluded scenes and out‑of‑distribution segmentation.
The paper studies Graph-Guided Token Merging (G2TM), a module that reduces token count in Vision Transformers. It evaluates G2TM across multiple segmentation frameworks and decoder types, finding that its performance gains are tied to the encoder rather than the decoder. The authors report consistent reductions in GFLOPs (22‑47%) and throughput improvements (up to 74%) on ADE20K, with optimal hyperparameters depending mainly on backbone pre‑training and target dataset.
arXiv:2608.21460v1 Announce Type: cross Abstract: Vision Language Models have recently shown improvements in several objective and verifiable domains such as object detection but continue to underper...
arXiv:2608.29677v1 Announce Type: cross Abstract: Despite rapid advances in MIS, fair and reproducible comparisons of segmentation models remain challenging due to heterogeneous datasets, inconsisten...
SAM‑V is a geometry‑aware extension of the Segment Anything Model (SAM) that integrates 3D priors from a feed‑forward geometry model (VGGT) into 2D segmentation. It uses a prompt‑fusion mechanism to combine sparse SAM prompts with view‑specific camera tokens and local VGGT features, enabling a mask decoder that attends to both dense 2D and 3D cues. The resulting end‑to‑end system produces consistent multi‑view instance segmentation in a single forward pass, achieving significant gains on the IGGT 3D tracking benchmark without offline mask matching or explicit 3D reconstruction.
arXiv:2610.05911v2 Announce Type: replace Abstract: Multi-view panoptic segmentation assigns a semantic class and a scene-level instance ID to every pixel of an unordered set of images, and recent fe...
arXiv:2508.01661v2 Announce Type: replace Abstract: Amodal segmentation aims to recover complete object shapes, including occluded regions, serving as an essential technique for user-centric multimed...
arXiv:2609.38714v1 Announce Type: new Abstract: We describe our winning entry to the Waymo Open Dataset 2D Video Panoptic Segmentation Challenge. The task asks for a semantic class at every pixel of...
SegCol is a new dataset and benchmark for semantic segmentation of colon fold edges and surgical instruments in colonoscopy images, derived from the EndoMapper dataset. It offers manually annotated pixel‑level masks for three instrument classes and thin fold‑edge structures across temporally consistent image sequences, and serves as the basis for the SegCol Challenge within the EndoVis Challenge at MICCAI 2024. The study evaluates supervised segmentation and annotation‑efficient active learning, analyzes various segmentation metrics under structural perturbations, and highlights how metric behavior depends on target structure, underscoring the need for carefully selected evaluation protocols in endoscopic segmentation.
arXiv:2607. 00687v1 Announce Type: cross Abstract: Comparing transformer backbones for image segmentation is confounded: each is paired with a different decoder, recipe, and pretraining, so reported differences rarely reflect the backbone itself.
The paper introduces FaVOS, a benchmark for Video Object Segmentation (VOS) that focuses on scenarios where target objects appear only intermittently over long videos. It demonstrates that the standard J&F metric can be gamed by empty predictions, allowing trivial models to outperform strong ones like SAM 3. To address this, the authors propose Volumetric J&F, which treats mask sequences as spatio‑temporal volumes, reducing the influence of target‑absence rewards while maintaining sensitivity to segmentation quality and temporal structure.