Cover Image

SmoGVLM: A Small, Graph-enhanced Vision-Language Model

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mondal, Debjyoti, Singh, Rituraj, Panda, Subhadarshi
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

KAM-CoT: Knowledge Augmented Multimodal Chain-of-Thoughts Reasoning
by: Mondal, Debjyoti, et al.
Published: (2024)

Can Safety Emerge from Weak Supervision? A Systematic Analysis of Small Language Models
by: Saha, Punyajoy, et al.
Published: (2026)

Vision Meets Language: A RAG-Augmented YOLOv8 Framework for Coffee Disease Diagnosis and Farmer Assistance
by: Mondal, Semanto
Published: (2025)

Seg-HGNN: Unsupervised and Light-Weight Image Segmentation with Hyperbolic Graph Neural Networks
by: Mondal, Debjyoti, et al.
Published: (2024)

Small Vision-Language Models: A Survey on Compact Architectures and Techniques
by: Patnaik, Nitesh, et al.
Published: (2025)

Open World Scene Graph Generation using Vision Language Models
by: Dutta, Amartya, et al.
Published: (2025)

iGVLM: Dynamic Instruction-Guided Vision Encoding for Question-Aware Multimodal Understanding
by: Liu, Hanpeng, et al.
Published: (2026)

HazardNet: A Small-Scale Vision Language Model for Real-Time Traffic Safety Detection at Edge Devices
by: Tami, Mohammad Abu, et al.
Published: (2025)

Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning
by: Zhu, Yingjie, et al.
Published: (2024)

How Does Vision-Language Adaptation Impact the Safety of Vision Language Models?
by: Lee, Seongyun, et al.
Published: (2024)

Dream-VL & Dream-VLA: Open Vision-Language and Vision-Language-Action Models with Diffusion Language Model Backbone
by: Ye, Jiacheng, et al.
Published: (2025)

Conflict Adaptation in Vision-Language Models
by: Hu, Xiaoyang
Published: (2025)

Vision Language Models are Confused Tourists
by: Irawan, Patrick Amadeus, et al.
Published: (2025)

Mipha: A Comprehensive Overhaul of Multimodal Assistant with Small Language Models
by: Zhu, Minjie, et al.
Published: (2024)

End-to-End Navigation with Vision Language Models: Transforming Spatial Reasoning into Question-Answering
by: Goetting, Dylan, et al.
Published: (2024)

FlightGPT: Towards Generalizable and Interpretable UAV Vision-and-Language Navigation with Vision-Language Models
by: Cai, Hengxing, et al.
Published: (2025)

Natural Language Inference Improves Compositionality in Vision-Language Models
by: Cascante-Bonilla, Paola, et al.
Published: (2024)

Do Vision-Language Models Really Understand Visual Language?
by: Hou, Yifan, et al.
Published: (2024)

PUMGPT: A Large Vision-Language Model for Product Understanding
by: Xue, Wei, et al.
Published: (2023)

Vision-Language Models Do Not Understand Negation
by: Alhamoud, Kumail, et al.
Published: (2025)

Text Prompt Injection of Vision Language Models
by: Zhu, Ruizhe
Published: (2025)

Evaluating Vision-Language Models for Emotion Recognition
by: Bhattacharyya, Sree, et al.
Published: (2025)

Intriguing Properties of Large Language and Vision Models
by: Lee, Young-Jun, et al.
Published: (2024)

Evaluation of Cultural Competence of Vision-Language Models
by: Yadav, Srishti, et al.
Published: (2025)

Vision Language Models Are Not (Yet) Spelling Correctors
by: Liang, Junhong, et al.
Published: (2025)

ViSpec: Accelerating Vision-Language Models with Vision-Aware Speculative Decoding
by: Kang, Jialiang, et al.
Published: (2025)

Do Vision-Language Models Truly Perform Vision Reasoning? A Rigorous Study of the Modality Gap
by: Xu, Yige, et al.
Published: (2026)

CLoVe: Encoding Compositional Language in Contrastive Vision-Language Models
by: Castro, Santiago, et al.
Published: (2024)

ViLBench: A Suite for Vision-Language Process Reward Modeling
by: Tu, Haoqin, et al.
Published: (2025)

Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR
by: Hennara, Khalil, et al.
Published: (2025)

Heron-Bench: A Benchmark for Evaluating Vision Language Models in Japanese
by: Inoue, Yuichi, et al.
Published: (2024)

A Unified Hallucination Mitigation Framework for Large Vision-Language Models
by: Chang, Yue, et al.
Published: (2024)

Diagnosing Vision Language Models' Perception by Leveraging Human Methods for Color Vision Deficiencies
by: Hayashi, Kazuki, et al.
Published: (2025)

Towards Zero-Shot Annotation of the Built Environment with Vision-Language Models (Vision Paper)
by: Han, Bin, et al.
Published: (2024)

Beyond the Vision Encoder: Identifying and Mitigating Spatial Bias in Large Vision-Language Models
by: Zhu, Yingjie, et al.
Published: (2025)

Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models
by: Wang, Weihang, et al.
Published: (2025)

PROGRESSLM: Towards Progress Reasoning in Vision-Language Models
by: Zhang, Jianshu, et al.
Published: (2026)

Can Vision-Language Models Solve the Shell Game?
by: Liu, Tiedong, et al.
Published: (2026)

Evaluating Vision-Language Models as Evaluators in Path Planning
by: Aghzal, Mohamed, et al.
Published: (2024)

Inference Compute-Optimal Video Vision Language Models
by: Wang, Peiqi, et al.
Published: (2025)