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...
By Daniele Cardullo, Simone Teglia, Irene Amerini
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.
By Maya Varma, Jean-Benoit Delbrouck, Sophie Ostmeier, Akshay Chaudhari, Curtis Langlotz
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.
By Novanto Yudistira, Muhammad Dzaka Aufa Fadhillah, Irawati Nurmala Sari
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.
By Sara Pieri, Evangelos Kazakos, Shizhe Chen, Josef Sivic, Cordelia Schmid
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.
By Jin Li, Tao Chen, Kai Wen, Siqi Yin, Shuai Jiang, Weijie Wang, Jingwen Luo, Chenhui Wu
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...