MiD-VLM is a multimodal model that detects and grounds global semantic misinformation in image-caption pairs. It combines image-text representations from SigLIP with visual scene features from DINOv2, and adapts Qwen2‑VL‑7B using LoRA to jointly predict authenticity, identify manipulation type, and localize misleading caption tokens. On the DGM4+ validation split, it achieves an F1 score of 0.9679 for binary authenticity classification.
arXiv:2512.20257v2 Announce Type: replace Abstract: With the rise of easily accessible generative tools for creating and manipulating multimedia content, the threat of realistic synthetic alterations...
arXiv:2607. 15216v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) often introduce errors when generating image captions, resulting in misaligned image-text pairs.
The paper presents a batik-captioning system that uses class labels as compact supervision when expert text annotations are scarce. It combines a LoRA‑adapted DINOv2 classifier with a Qwen2.5‑VL‑7B captioner, both loaded in 4‑bit NF4, and evaluates three captioner conditions across 150 images, achieving a 0.9067 ground‑truth class‑label mention rate. The classifier attains 96.10% pooled accuracy, with higher performance on public‑source images (98.33%) than on local images (75.76%), and the study highlights how class conditioning preserves label information while also propagating classification errors into fluent captions.
PANORAMA introduces a new panoptic grounded captioning framework that jointly generates detailed image captions and associates each phrase with precise pixel-level masks. The authors create PanoCaps, a human‑annotated benchmark with dense captions and near‑complete pixel coverage, and propose a phrase‑mask matching protocol with a generalized Panoptic Quality metric. PANORAMA conditions a pretrained segmenter on contextualized phrase representations, learns to select appropriate masks, and achieves state‑of‑the‑art grounding performance on PanoCaps and other pixel‑level tasks.
arXiv:2601. 22725v4 Announce Type: replace-cross Abstract: Recent advances in diffusion models have significantly elevated the visual fidelity of Virtual Try-On (VTON) systems, yet reliable evaluation remains a persistent bottleneck.
Intelligent systems that act in the world require image understanding that is both comprehensive and spatially grounded. Current vision-language models (VLMs) can generate fluent and detailed image ca...
arXiv:2608. 01856v2 Announce Type: replace Abstract: Bi-temporal remote-sensing disaster change captioning often needs to identify sparse and spatially localized changes across large pre- and post-event scenes and then translate them into coherent, factual descriptions.
arXiv:2608. 09122v1 Announce Type: cross Abstract: The localized depiction of perceptual quality has long been a crucial, yet underexplored, challenge in image quality assessment (IQA).
arXiv:2610.41118v1 Announce Type: new Abstract: Vision-language compositionality is the ability to identify which properties, relations, quantities, and states bind to which entities in both an image...
arXiv:2609.37576v1 Announce Type: new Abstract: With the rapid advancement of text-to-image (T2I) generation, robust evaluation becomes critical yet challenging, as traditional metrics fail to captur...
The paper introduces LHSDet, a detector for AI‑generated images that treats detection as a visual question answering task. It employs a triple‑branch architecture: a low‑level visual branch for local texture cues, a high‑level visual branch using SigLIP2 for global perception, and a semantic‑level textual branch that generates captions with BLIP‑2. Experiments show that LHSDet achieves strong accuracy and robustness across various generative models.
arXiv:2501.04001v4 Announce Type: replace Abstract: This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-...