AnchorSeg uses ordered query banks of latent reasoning tokens plus a spatial anchor token and a Token-Mask Cycle Consistency loss to achieve 67.7% gIoU and 68.1% cIoU on the ReasonSeg benchmark.
Anwer, Erix Xing, Ming-Hsuan Yang, and Fahad S
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
fields
cs.CV 6polarities
background 2representative citing papers
PRISM benchmark perturbs Crello layouts into 110K samples isolating design principle violations, reveals limited sensitivity in several multimodal models, and proposes a multi-scale framework combining scorers, instruction-tuned VLMs, and prompt methods for interpretable design assessment.
OmniSch benchmarks large multimodal models on converting real PCB schematics into spatially weighted netlist graphs and finds large gaps in grounding, topology, and geometry.
MINGLE is a modular pipeline that combines off-the-shelf detection tools with VLM reasoning to localize socially connected groups in urban scenes and is supported by a new 100K-image dataset.
MoE-LLaVA applies mixture-of-experts sparsity to LVLMs via MoE-Tuning, delivering LLaVA-1.5-7B level visual understanding and better hallucination resistance with only ~3B active parameters.
This survey organizes the architectures, training strategies, data, evaluation methods, extensions, and challenges of Multimodal Large Language Models.
citing papers explorer
-
AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation
AnchorSeg uses ordered query banks of latent reasoning tokens plus a spatial anchor token and a Token-Mask Cycle Consistency loss to achieve 67.7% gIoU and 68.1% cIoU on the ReasonSeg benchmark.
-
Through the PRISM: Principle-Aware, Interpretable, and Multi-Scale Evaluation of Visual Designs
PRISM benchmark perturbs Crello layouts into 110K samples isolating design principle violations, reveals limited sensitivity in several multimodal models, and proposes a multi-scale framework combining scorers, instruction-tuned VLMs, and prompt methods for interpretable design assessment.
-
OmniSch: A Multimodal PCB Schematic Benchmark For Structured Diagram Visual Reasoning
OmniSch benchmarks large multimodal models on converting real PCB schematics into spatially weighted netlist graphs and finds large gaps in grounding, topology, and geometry.
-
MINGLE: VLMs for Semantically Complex Region Detection in Urban Scenes
MINGLE is a modular pipeline that combines off-the-shelf detection tools with VLM reasoning to localize socially connected groups in urban scenes and is supported by a new 100K-image dataset.
-
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
MoE-LLaVA applies mixture-of-experts sparsity to LVLMs via MoE-Tuning, delivering LLaVA-1.5-7B level visual understanding and better hallucination resistance with only ~3B active parameters.
-
A Survey on Multimodal Large Language Models
This survey organizes the architectures, training strategies, data, evaluation methods, extensions, and challenges of Multimodal Large Language Models.