SynDORBench: Evaluating LVLM Perceptual Robustness Under Physically Constrained Visibility Conditions

arXiv:2609.31823 · cs.CV, cs.AI · Submitted 2026-09-25 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Benchmark — 42× in this paper · explained in 2 episodes
• Robustness — 42× in this paper · explained in 5 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Alignment — 7× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 6× in this paper
• Human-Presence Classification — 5× in this paper
• Benchmarking — 4× in this paper · explained in 3 episodes
• Multimodal Reasoning — 4× in this paper · explained in 5 episodes
• Aperture — 3× in this paper · explained in 1 episode
• Hallucination — 3× in this paper · explained in 3 episodes
• Semantic Understanding — 3× in this paper · explained in 2 episodes
• Ensemble Learning — 2× in this paper · explained in 2 episodes
• Physical Constraints — 2× in this paper · explained in 7 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Params Vision Encoder — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes
• Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs
• Qwen2.5-VL Technical Report
• SMPLitex: A Generative Model and Dataset for 3D Human Texture Estimation from Single Image
• PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding
• CLIPScore: A Reference-free Evaluation Metric for Image Captioning
• YOLOv11: An Overview of the Key Architectural Enhancements
• SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
• SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models
• Small Language Models: Survey, Measurements, and Insights
• SmolVLM: Redefining small and efficient multimodal models
• Contrast Limited Adaptive Histogram Equalization (CLAHE) Approach for Enhancement of the Microstructures of Friction Stir Welded Joints
• CinePile: A Long Video Question Answering Dataset and Benchmark
• Gemma 3 Technical Report
• Boundless: Generating Photorealistic Synthetic Data for Object Detection in Urban Streetscapes
• Text Embeddings by Weakly-Supervised Contrastive Pre-training
• Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
• LLaVAction: evaluating and training multi-modal large language models for action understanding
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

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