FindIt is the first comprehensive benchmark for evaluating generalist MLLMs on promptable object detection, referring expression detection, instance-level detection, and video detection with standardized parsable outputs.
Revisiting refer- ring expression comprehension evaluation in the era of large multimodal models
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Sa2VA unifies SAM-2 segmentation with MLLM reasoning into a single model for referring segmentation and conversation on images and videos, supported by a new 72k-expression Ref-SAV dataset.
LLaVA-OneVision-1.5 provides open datasets, code, and models that match or exceed closed competitors on 27 benchmarks at low cost through curated data and efficient training.
citing papers explorer
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FindIt: A Format-Informed Visual Detection Benchmark for Generalist Multimodal LLMs
FindIt is the first comprehensive benchmark for evaluating generalist MLLMs on promptable object detection, referring expression detection, instance-level detection, and video detection with standardized parsable outputs.
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Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos
Sa2VA unifies SAM-2 segmentation with MLLM reasoning into a single model for referring segmentation and conversation on images and videos, supported by a new 72k-expression Ref-SAV dataset.
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LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
LLaVA-OneVision-1.5 provides open datasets, code, and models that match or exceed closed competitors on 27 benchmarks at low cost through curated data and efficient training.