Jul 2026· Journal of Artificial Intelligence and Information· 0 citations· 46 references
TL;DR
An overview of Vision LLM architectures, their applications and the challenges they face and case study of how building of AI Models through visionLLM may help IndoAI AI camera system are provided.
Abstract
Vision LLMs are trained on vast datasets containing paired image-text samples, allowing them to perform tasks such as image captioning, visual question answering (VQA) and multimodal reasoning. These Models (Vision LLMs) mark a transformative leap in artificial intelligence by merging visual and linguistic understanding, enabling seamless human-machine communication, power groundbreaking applications-from automated diagnostic reporting in healthcare to real-time scene analysis in autonomous systems. Yet, key challenges remain, including computational inefficiency, embedded biases in training data and limited interpretability which currently restrict broader deployment. Cutting-edge research is tackling these obstacles through optimized model architectures, fairness-aware dataset curation and advanced explainable AI methods. As these advancements progress, Vision LLMs are poised to revolutionize AI- driven solutions across industries such as healthcare, robotics, autonomous vehicles. Their continued evolution is redefining the landscape of interdisciplinary AI, fostering more intuitive, ethical and scalable intelligent systems. This article provides an overview of Vision LLM architectures, their applications and the challenges they face and case study of how building of AI Models through visionLLM may help IndoAI AI camera system.
Detection and localization of AI-tampered images are critical for trustworthy AI, yet modern generative models have made such manipulations increasingly difficult to identify. While traditional binary classifiers can detect image tampering, they lack interpretability and generalization. Vision-Language Models (VLMs) offer a promising alternative due to their strong visual understanding and reasoning capabilities; however, existing approaches typically rely on supervised finetuning with curated explanations rather than exploiting their inherent reasoning capabilities. In this work, we investigate whether VLMs can be trained to reason about AI-generated image edits using reinforcement learning (RL) rather than explicit reasoning supervision. Motivated by the success in Group Relative Policy Optimization (GRPO), an RL technique that incentivizes the model to reason by generating thinking traces prior to giving the final answer, we propose a GRPO-based training framework that utilizes simple accuracy and format rewards. Given an input image, the model produces a structured reasoning trace and predicts whether the image has been tampered with. A lightweight segmentation model is then guided by the reasoning output to generate pixel-level localization masks. Experiments across multiple image manipulation datasets demonstrate that our approach achieves competitive detection and localization performance compared to state-of-the-art image forgery detectors, despite requiring substantially weaker supervision. We introduce effective intersection over union (eff-IoU), a unified metric to jointly evaluate detection and localization. These results suggest that reinforcement learning provides an effective and scalable mechanism for teaching VLMs to reason about AI-generated content.
Darsha Udayanga, Pin-Yu Chen, Payel Das et al.· 0 citations
AutoDavis is introduced, a first-of-its-kind automatic and dynamic evaluation protocol that enables on-demand benchmarking of LVLMs across specific capability dimensions and shows effectiveness and reliability, offering a new paradigm for dynamic benchmarking of multimodal intelligence.
Han Bao, Yue Huang, Yanbo Wang et al.· Proceedings of the 32nd ACM...· 0 citations
UniRS-Instruct is presented, a high-quality, diversified, and unified multimodal instruction-following dataset for RSI understanding that unifies diverse tasks, including image captioning, visual question answering, visual grounding, and region-level captioning, into a consistent format.
Linrui Xu, Yuhan Wang, Ling Zhao et al.· IEEE Journal of Selected Top...· 0 citations
This paper presents a multimodal pipeline that combines vision-language captioning models and openvocabulary object detectors to investigate the impact of automatically generated textual prompts on semantic image understanding. The study evaluates several captioning models, including BLIP, BLIP-2, InstructBLIP, and LLaVA, in combination with two open-vocabulary detectors, OWLv2 and Grounding DINO. Experiments conducted on a representative subset of the COCO dataset show that prompt quality significantly influences detection performance and that post-processing operations, including label normalization and filtering, substantially improve semantic detection metrics. The results reveal complementary behaviors between Grounding DINO and OWLv2, highlighting the importance of prompt engineering and output refinement in multimodal vision-language pipelines. Rather than introducing a new detection architecture, this work provides a comparative analysis of the interactions between caption generation, prompt extraction, and open-vocabulary detection, offering insights for the design of future interactive vision-language systems.
Xin Gao, Madjid Maidi, B. Daachi· NLP & Big Data· 0 citations
The Robust and Fine-grained training framework for CLIP-based vision-language models (RoFLIP) is proposed, enhancing both the robustness and granularity of vision-language alignment and underscore RoFLIP’s compositional reasoning and generalization abilities.
Yiwei Sun, Chuanbin Liu, Shancheng Fang et al.· International Journal of Com...· 0 citations
Recent advancements in Multimodal Large Language Models (MLLMs) have evolved from static perception to interleaved visual-language reasoning, often referred to as ``thinking with images''. A basic operation in this reasoning process is to zoom in on regions of interest (often represented with bounding boxes) to acquire finer visual details. In this paper, we propose \textbf{Seg}mentation before \textbf{Answer}ing (SegAnswer), which shifts the unit of zoom-in from the popular bounding box to pixel-level segmentation mask. By employing fine-grained masks to isolate the target area from cluttered environments, segmented visual input yields a more precise region of interest, effectively filtering out redundant background and interfering objects. Furthermore, the discrete patches of segmented visual input align more seamlessly with how MLLMs structure visual tokens via positional embeddings. In experiments, we evaluate SegAnswer across diverse benchmarks, including high-resolution perception, general perception, and hallucination. It achieves consistent improvements and also exhibits considerable performance on segmentation tasks, validating its capability for reliable pixel grounding.
Yake Wei, Yuan Wang, Fengyun Rao et al.· 0 citations
