The paper introduces the Semantic Drift Protocol (SDP), a Telephone Game-inspired method to evaluate how well unified models preserve meaning when alternating between image-to-text (I2T) and text-to-image (T2I) generation over multiple generations. It defines Mean Cumulative Drift (MCD) and Multi-Generation GenEval (MGG) as metrics for semantic retention, and presents a new benchmark of 400 image‑text pairs from NoCaps and DOCCI to stress-test models beyond COCO. Applying SDP to seven models shows that models with strong single‑pass scores can still suffer severe semantic drift, revealing catastrophic failure modes that isolated benchmarks miss.
By Sabbir Mollah, Rohit Gupta, Sirnam Swetha, Qingyang Liu, Ahnaf Munir, Mubarak Shah
arXiv:2606.20032v2 Announce Type: replace
Abstract: Unlike traditional remote sensing change detection that relies on predefined categories, Open-Vocabulary Change Detection (OVCD) identifies land co...
By Hongming Zhu, Huaji Chen, Bowen Du, Sicong Liu, Qin Liu
The paper proposes a weakly supervised remote sensing change detection method that uses change captions as the sole supervision signal, eliminating the need for pixel‑level change masks. It introduces a caption‑driven generation pipeline to create bi‑temporal image pairs with controlled changes and a Semantic‑Appearance Agreement Framework (SAAF) that fuses caption‑grounded semantic responses with RGB differences for accurate change localization. Experiments on the Flair‑RSGen and WHU‑CDC datasets demonstrate that SAAF outperforms existing limited‑supervision baselines in macro‑averaged IoU and F1 metrics.
By Yuan Qian, Jie Ma
MinCU is a new benchmark for grounded minimal‑change understanding that presents pairs of near‑identical images differing by a single atomic variation in object category, attribute, count, or spatial position. Models are evaluated on their ability to describe the change, localize the changed region, and identify the changed entity. The authors also introduce SG‑ISA, a structured autoregressive method that decomposes the task into a Think‑Locate‑Describe sequence, showing that fine‑tuning with SG‑ISA improves both grounding accuracy and description quality while reducing reasoning‑token overhead.
By Chaoqian Mu, Wenhao Wu, Zichen Liang, Jiaxu Li, Lijun Wang, Yifan Wang, Huchuan Lu
arXiv:2511.14086v2 Announce Type: replace-cross
Abstract: Despite recent progress in 3D-LLMs, they remain limited in accurately grounding language to visual and spatial elements in 3D environments. T...
By Yue Zhang, Zun Wang, Han Lin, Jialu Li, Jianing Yang, Yonatan Bitton, Idan Szpektor, Mohit Bansal
Visual foundation models are commonly adapted under the assumption that the appearance of incoming data may change while the semantic meaning of the prediction task remains fixed. In long-lived visual...