Hugging Face Trending Papers

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.

Hugging Face Trending Papers

Jul 7

AVA-VLM: Adaptive Visual Attention-Vision Language Model for In-the-Wild Construction Site Monitoring

Vision-Language Models (VLMs) are promising for construction-site monitoring, and recent construction-tailored VLMs have primarily adapted pretrained VLMs through direct QA-style fine-tuning from a single global image. We argue that this direct paradigm remains limited for in-the-wild deployment in terms of operational range, reliability under reduced-resolution inputs, and inference efficiency.

llms fine-tuning multimodal

arXiv AI

Aug 7

HoloCount: A Holistic Visual Counting Benchmark for MLLMs

arXiv:2607. 06420v1 Announce Type: cross Abstract: Visual counting is a fundamental pillar of multimodal intelligence, requiring a seamless integration of fine-grained grounding and spatial reasoning.

By Jinhong Deng, Limeng Qiao, Guanglu Wan

llms multimodal benchmarks safety

Hugging Face Trending Papers

Jul 14

CoRe: A Comprehensive Framework for Cross-Image Comparative Reasoning in Vision-Language Models

Cross-image comparative reasoning remains challenging for vision-language models (VLMs), especially when correct prediction requires fine-grained attribute grounding and globally consistent reasoning. We present CoRe, a unified framework for this problem.

llms multimodal benchmarks safety

arXiv AI

Aug 7

Depth-Guided Video Object Counting in Crowded Scenes

arXiv:2608. 06236v1 Announce Type: cross Abstract: Our primary objective is to advance video object counting in crowded scenes, aiming to robustly count all instances of a target category based on given text or visual prompts.

By Yuanjing Xu, Xinyan Liu, Weidong Chen, Zixuan Zou, Linhao Zhang, Zhuangzhe Meng, Antoni B. Chan, Weigang Zhang

arXiv Machine Learning

Jul 13

The Count Is There, but Misaligned: Understanding and Correcting Counting Failures in VLMs

arXiv:2607. 09544v1 Announce Type: cross Abstract: Despite strong performance on many multimodal tasks, vision-language models (VLMs) still struggle with basic object counting.

By Ahmed Oumar El-Shangiti, Abzal Nurgazy, Hilal AlQuabeh, Nikolai Rozanov, Kentaro Inui

llms multimodal

arXiv AI

Jul 8

OpenGround: Planning-based Online Perception for Open-World 3D Visual Grounding

arXiv:2512. 23020v3 Announce Type: replace-cross Abstract: 3D visual grounding aims to locate objects based on natural language descriptions in 3D scenes.

By Wenyuan Huang, Zhenyu Zhang, Zhao Wang, Zhou Wei, Ting Huang, Fang Zhao, Jian Yang

llms benchmarks

arXiv Machine Learning

Jun 2

Self-Improving Small Object Grounding in LVLMs

arXiv:2606. 01612v1 Announce Type: cross Abstract: Can internal attention patterns in Large Vision Language Models (LVLMs) identify reliable small-object boxes without fine-tuning?

By Tianze Yang, Yucheng Shi, Ruitong Sun, Ninghao Liu, Jin Sun

llms fine-tuning safety

arXiv Machine Learning

Jul 9

Unveiling the Visual Counting Bottleneck in Vision-Language Models

arXiv:2605. 30170v2 Announce Type: replace-cross Abstract: While Large Vision-Language Models (VLMs) excel at interpolation, they suffer catastrophic failures in systematic generalization, most notably in visual counting.

By Xingzhou Pang, Yifan Hou, Junling Wang, Mrinmaya Sachan

llms multimodal benchmarks

arXiv AI

Jul 3

ProCal: Inference-Time Proposal Calibration for Open-Vocabulary Object Detection

arXiv:2607. 01759v1 Announce Type: cross Abstract: Open-vocabulary object detection aims to localize and classify objects beyond the fixed set of categories seen dur ing training.

By Jae-Ryung Hong, Ho-Joong Kim, Seong-Whan Lee

rag computer-vision multimodal

arXiv AI

Jul 14

ABot-N1: Toward a General Visual Language Navigation Foundation Model

arXiv:2607. 10383v1 Announce Type: cross Abstract: Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks.

By Ruiyan Gong, Yingnan Guo, Junjun Hu, Jintao Kong, Xiaoxu Leng, Tianlun Li, Weize Li, Fei Liu, Zhicheng Liu, Jia Lu, Minghua Luo, Chenlin Ming, Yanfen Shen, Jiyue Tao, Zhengbo Wang, Mingyang Yin, Minqi Gu, Zihao Guan, Wei Guo, Guoqing Liu, Huachong Pang, Menglin Yang, Zeqian Ye, Xiaoxiao Geng, Zhining Gu, Honglin Han, Di Jing, Hongyu Pan, Mingchao Sun, Kuan Yang, Jianfang Zhang, Yanghong Chen, Ye He, Wei Mei, Jiahao Shi, Xiangpo Yang, Yanqing Zhu, Zedong Chu, Xiaolong Wu, Mu Xu

robotics multimodal benchmarks safety

arXiv AI

Jun 30

Efficient Spatio-Temporal Grounding with Multimodal Large Models via Second-Level Tracking and RL Verification

arXiv:2606. 29023v1 Announce Type: cross Abstract: Spatio-temporal grounding in long videos requires precise temporal localization and robust object tracking conditioned on natural-language queries.

By Tianshu Zhang, Yan Wang, Ji Qi, Lijie Wen

llms reinforcement-learning multimodal

arXiv AI

Jun 2

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.

By Bingyu Li, Da Zhang, Tao Huo, Zhiyuan Zhao, Junyu Gao, Xuelong Li

llms agents computer-vision fine-tuning multimodal benchmarks

Related stories