arXiv Computer Vision
CountLoop: Training-Free High-Instance Image Generation via Iterative Agent Guidance
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arXiv Computer Vision
Sep 11
ABACUS: Adapting Unified Foundation Model for Bridging Image Count Understanding and Generation
ABACUS is a 3‑billion‑parameter vision‑language model that simultaneously tackles object counting, crowd counting, referring‑expression counting, and count‑faithful image generation. It introduces density‑aware adaptive zooming with an objectness map, a boundary‑aware count policy trained via GRPO to avoid over‑ or under‑counting at crop edges, and a cycle‑consistent GRPO strategy that scores generated images for count accuracy and aesthetic quality without external critics. The model sets new state‑of‑the‑art performance on seven benchmarks, outperforming both specialized and larger generalist models.
Hugging Face Trending Papers
Jun 22
ABACUS: Adapting Unified Foundation Model for Bridging Image Count Understanding and Generation
ABACUS is a unified vision-language model that handles object counting, crowd counting, referring-expression counting, and count-faithful image generation without any benchmark-specific training required. Our model is built on existing 3B-parameter unified foundation model and is adapted for object localization tasks using three key innovations: density-aware adaptive zooming with objectness maps for spatial grounding; a boundary-aware count policy via GRPO to eliminate crop-boundary errors; and a cycle-consistent GRPO strategy where the understanding branch self-critiques generated outputs, closing the understanding-generation gap without any external annotations.
arXiv Computer Vision
4d ago
Why MLLMs Struggle to Count: Overcoming Individuation and Aggregation Bottlenecks with ConvStack
arXiv:2609.37096v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) consistently struggle with fine-grained visual counting, yet the underlying causes remain poorly understood. I...
arXiv AI
Jul 21
Think, Plan, Paint: Layout-Aware Reasoning for Controllable Image Generation in Unified Models
arXiv:2607. 16409v1 Announce Type: cross Abstract: Unified Multimodal Large Language Models (MLLMs) offer a promising paradigm for unifying visual understanding and generation, yet they still struggle to follow complex spatial instructions and logical constraints in controllable image generation.
arXiv Computer Vision
Sep 1
Generalization over Memorization: Generalization-Aware Diffusion Adaptation for Single-Image Multi-View Synthesis
arXiv:2608.29233v1 Announce Type: new Abstract: We present the winning solution to the ACM Multimedia 2026 Grand Challenge on Single-Image Guided Multi-Angle Image Synthesis. It ranks first among 293...
arXiv Computer Vision
Aug 24
RECOUNT: Reference-guided Counting with Synthetic Visual Exemplars
arXiv:2608.20621v1 Announce Type: new Abstract: Text-guided zero-shot object counters excel at spatial localization but categorize poorly on novel or fine-grained classes: natural language is too coa...