CrowdCue: Specialist-Cue Conditioning for Vision-Language Crowd Counting

arXiv:2609.23012 · cs.CV · Submitted 2026-09-19 · Read on arXiv

cs.CV

Submitted: 2026-09-19

Updated: 2026-09-19

Terminology

• Accuracy — 24× in this paper · explained in 1 episode
• Benchmark — 12× in this paper · explained in 2 episodes
• Abstraction — 11× in this paper · explained in 1 episode
• Transformer — 8× in this paper · explained in 2 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Ablation — 6× in this paper · explained in 1 episode
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• GRPO — 6× in this paper · explained in 6 episodes
• Trace — 5× in this paper · explained in 2 episodes
• Decode — 4× in this paper · explained in 2 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 3× in this paper
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Positional Bias — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• alpha — 3× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Distribution Shift — 2× in this paper · explained in 6 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Structured Reasoning — 2× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Mean Absolute Error — 1× in this paper
• Pruning — 1× in this paper · explained in 4 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Root Mean Squared Error — 1× in this paper
• Scaffolding — 1× in this paper · explained in 3 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• Qwen2.5-VL Technical Report
• RCCFormer: A Robust Crowd Counting Network Based on Transformer
• LoRA: Low-Rank Adaptation of Large Language Models
• Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models
• CLIP-Count: Towards Text-Guided Zero-Shot Object Counting
• ProgRoCC: A Progressive Approach to Rough Crowd Counting
• TCFormer: A 5M-Parameter Transformer with Density-Guided Aggregation for Weakly-Supervised Crowd Counting
• Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models
• CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
• ZIP: Scalable Crowd Counting via Zero-Inflated Poisson Modeling
• Multi-modal Crowd Counting via a Broker Modality
• Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework
• MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding
• CrowdVLM-R1: Expanding R1 Ability to Vision Language Model for Crowd Counting using Fuzzy Group Relative Policy Reward
• DSGC-Net: A Dual-Stream Graph Convolutional Network for Crowd Counting via Feature Correlation Mining
• Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V

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