InstanceBench: Diagnosing Referential Reasoning and Target Identity in Referring Expression Segmentation

arXiv:2610.09478 · cs.CV · Submitted 2026-10-07 · Read on arXiv

cs.CV

Submitted: 2026-10-07

Updated: 2026-10-07

Code: https://github.com/aierwaixi/InstanceBench

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Grounding — 66× in this paper · explained in 3 episodes
• Benchmark — 25× in this paper · explained in 2 episodes
• Core — 16× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Consistency — 7× in this paper · explained in 3 episodes
• Referring Expression Segmentation — 7× in this paper
• Conference on Computer Vision and Pattern Recognition — 6× in this paper
• Mask Generation — 5× in this paper · explained in 3 episodes
• Robustness — 4× in this paper · explained in 6 episodes
• Alignment — 3× in this paper
• CVF International Conference on Computer Vision — 2× in this paper
• Hallucination — 2× in this paper · explained in 4 episodes
• Instruction Tuning — 2× in this paper · explained in 3 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Annotator Agreement — 1× in this paper
• Domain Shift — 1× in this paper · explained in 7 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
• LLaVA-OneVision: Easy Visual Task Transfer
• CogVLM: Visual Expert for Pretrained Language Models
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

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