2 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
3DCity-LLM: Empowering Multi-modality Large Language Models for 3D City-scale Perception and Understanding
Yiping Chen, Jinpeng Li, Wenyu Ke +6
While multi-modality large language models excel in object-centric or indoor scenarios, scaling them to 3D city-scale environments remains a formidable challenge. To bridge this ga…
PinPoint: Evaluation of Composed Image Retrieval with Explicit Negatives, Multi-Image Queries, and Paraphrase Testing
Rohan Mahadev, Joyce Yuan, Patrick Poirson +3
Composed Image Retrieval (CIR) has made significant progress, yet current benchmarks are limited to single ground-truth answers and lack the annotations needed to evaluate false po…
WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models
Runjie Zhou, Youbo Shao, Haoyu Lu +16
We introduce WorldVQA, a benchmark designed to evaluate the atomic visual world knowledge of Multimodal Large Language Models (MLLMs). Unlike current evaluations, which often confl…
Towards Pixel-Level VLM Perception via Simple Points Prediction
Tianhui Song, Haoyu Lu, Hao Yang +8
We present SimpleSeg, a strikingly simple yet highly effective approach to endow Multimodal Large Language Models (MLLMs) with native pixel-level perception. Our method reframes se…