arXiv AI
Jun 11

LASA: A Weak Supervision Method for Open-Vocabulary Scene Sketch Semantic Segmentation

arXiv:2606. 11837v1 Announce Type: cross Abstract: Open-vocabulary scene sketch semantic segmentation aims to assign dense semantic labels to sparse line drawings based on flexible category vocabularies specified at inference time, without relying on pixel-level annotations during training.

By Liwen Yi, Xianlin Zhang, Yue Zhang, Yue Ming, Xueming Li
arXiv Computer Vision
Sep 3

Detecting Object Hallucinations in Large Vision-Language Models via Cross-Modal Attention Drifts and Mask-Based Verification

The paper introduces CADMP, a lightweight framework for detecting object hallucinations in large vision‑language models. CADMP measures cross‑modal attention drift between adjacent layers and verifies predictions by masking visually relevant regions, combining these signals to identify hallucinated outputs. Experiments on multiple benchmarks show that CADMP achieves competitive detection performance, and ablation studies confirm the complementary roles of attention drift and mask‑based verification.

By Xuanbing Wen, Boxu Chen, Le Yang, Jiakai Wang, Zhengyu Zhao, Chenhao Lin, Chao Shen
arXiv Computer Vision
Oct 9

From What to Which: Decoding Modifier Grounding in Frozen MLLMs

The paper investigates whether frozen Multimodal Large Language Models (MLLMs) encode grounding information about specific visual instances within the tokens they generate, including modifiers such as attributes, spatial expressions, and relational terms. The authors introduce OTTER, a lightweight supervised probe that applies Optimal Transport to align generated tokens with visual regions, producing compact grounding maps. Experiments demonstrate that modifier tokens, especially spatial terms, carry instance‑discriminative visual information, that object nouns also encode referential cues, and that the recovered grounding remains robust to context changes and generalizes to free generation and cross‑dataset transfer.

By Barbara Toniella Corradini (AI for Good), Caterina Gallegati (University of Siena, Italy), Ludovica Genovese (AI for Good), Vittorio Murino (AI for Good)