What Sketches Tell Us about LVLMs: Conventions, Grounding, and Localisation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
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.
arXiv:2608.28707v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Visual Question Answering (VQA), yet they continue to struggle with quest...
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.
arXiv:2502.04192v5 Announce Type: replace Abstract: Multiple works have emerged to push the boundaries of multi-modal large language models (MLLMs) towards pixel-level understanding. The current tren...
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.
Despite recent advances in large vision-language models (LVLMs), object hallucination remains a major barrier to their reliable deployment. Existing detection methods often characterize visual groundi...