WSRVOS enables referring video object segmentation with text-only supervision by combining MLLM-based expression augmentation, multimodal feature interaction, pseudo-mask fusion, and temporal ranking constraints.
Villa: Video reasoning segmentation with large language model
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
years
2026 7roles
background 3polarities
background 3representative citing papers
The paper defines UAV reasoning segmentation, releases DRSeg—10,000 aerial images with reasoning QA and masks—and shows its PixDLM baseline beats prior reasoning-segmentation models under fine-tuning.
The survey formalizes MLLM perception as a unified vision-language capability and traces its evolution via a new five-stage taxonomy while outlining future challenges.
RCoT-Seg uses GRPO-reinforced keyframe selection from a CoT-start corpus followed by SAM2 mask propagation to improve video object segmentation under implicit temporal instructions over prior MLLM sampling methods.
A staged pipeline using ASR transcription, visual existence verification, Sa2VA coarse segmentation, and agent-guided SAM3 refinement won first place in the PVUW MeViS-Audio track by decomposing audio-conditioned Ref-VOS into sequential verification and refinement steps.
An agent-augmented Sa2VA pipeline for referring video object segmentation placed third in the MeViS-Text track of the 5th PVUW Challenge by adding verification, search, and refinement stages.
This review organizes literature on large multimodal models and object-centric vision into four themes—understanding, referring segmentation, editing, and generation—while summarizing paradigms, strategies, and challenges like instance permanence and consistent interaction.
citing papers explorer
-
Weakly-Supervised Referring Video Object Segmentation through Text Supervision
WSRVOS enables referring video object segmentation with text-only supervision by combining MLLM-based expression augmentation, multimodal feature interaction, pseudo-mask fusion, and temporal ranking constraints.
-
PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation
The paper defines UAV reasoning segmentation, releases DRSeg—10,000 aerial images with reasoning QA and masks—and shows its PixDLM baseline beats prior reasoning-segmentation models under fine-tuning.
-
From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models
The survey formalizes MLLM perception as a unified vision-language capability and traces its evolution via a new five-stage taxonomy while outlining future challenges.
-
RCoT-Seg: Reinforced Chain-of-Thought for Video Reasoning and Segmentation
RCoT-Seg uses GRPO-reinforced keyframe selection from a CoT-start corpus followed by SAM2 mask propagation to improve video object segmentation under implicit temporal instructions over prior MLLM sampling methods.
-
APRVOS: 1st Place Winner of 5th PVUW MeViS-Audio Track
A staged pipeline using ASR transcription, visual existence verification, Sa2VA coarse segmentation, and agent-guided SAM3 refinement won first place in the PVUW MeViS-Audio track by decomposing audio-conditioned Ref-VOS into sequential verification and refinement steps.
-
AgentRVOS for MeViS-Text Track of 5th PVUW Challenge: 3rd Method
An agent-augmented Sa2VA pipeline for referring video object segmentation placed third in the MeViS-Text track of the 5th PVUW Challenge by adding verification, search, and refinement stages.
-
LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation
This review organizes literature on large multimodal models and object-centric vision into four themes—understanding, referring segmentation, editing, and generation—while summarizing paradigms, strategies, and challenges like instance permanence and consistent interaction.