Event-Driven Refresh and Recurrence Memory to Reduce Stale Grounding in Referring Video Object Segmentation

arXiv:2609.38758 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Accuracy — 22× in this paper · explained in 1 episode
• Grounding — 22× in this paper · explained in 3 episodes
• Benchmark — 21× in this paper · explained in 2 episodes
• Ablation — 13× in this paper · explained in 1 episode
• Core — 8× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Referring Video Object Segmentation — 7× in this paper
• Pruning — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Graceful Degradation — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos
• SAM 2: Segment Anything in Images and Videos
• Segment Anything
• Learning Transferable Visual Models From Natural Language Supervision
• Visual Instruction Tuning
• RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation
• End-to-End Referring Video Object Segmentation with Multimodal Transformers
• OnlineRefer: A Simple Online Baseline for Referring Video Object Segmentation
• SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation
• Actor and Action Video Segmentation from a Sentence
• The 2017 DAVIS Challenge on Video Object Segmentation
• Marginalising over Stationary Kernels with Bayesian Quadrature
• XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
• Associating Objects with Transformers for Video Object Segmentation
• Matching Anything by Segmenting Anything
• Towards Robust Referring Video Object Segmentation with Cyclic Relational Consensus

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models