PixelLM: Pixel Reasoning with Large Multimodal Model

Zhongwei Ren; Zhicheng Huang; Yunchao Wei; Yao Zhao; Dongmei Fu; Jiashi Feng; Xiaojie Jin

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arXiv · 2312.02228

PixelLM: Pixel Reasoning with Large Multimodal Model

Zhongwei Ren·Zhicheng Huang·Yunchao Wei·Yao Zhao·Dongmei Fu·Jiashi Feng·Xiaojie Jin

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Abstract

While large multimodal models (LMMs) have achieved remarkable progress, generating pixel-level masks for image reasoning tasks involving multiple open-world targets remains a challenge. To bridge this gap, we introduce PixelLM, an effective and efficient LMM for pixel-level reasoning and understanding. Central to PixelLM is a novel, lightweight pixel decoder and a comprehensive segmentation codebook. The decoder efficiently produces masks from the hidden embeddings of the codebook tokens, which encode detailed target-relevant information. With this design, PixelLM harmonizes with the structure of popular LMMs and avoids the need for additional costly segmentation models. Furthermore, we propose a target refinement loss to enhance the model's ability to differentiate between multiple targets, leading to substantially improved mask quality. To advance research in this area, we construct MUSE, a high-quality multi-target reasoning segmentation benchmark. PixelLM excels across various pixel-level image reasoning and understanding tasks, outperforming well-established methods in multiple benchmarks, including MUSE, single- and multi-referring segmentation. Comprehensive ablations confirm the efficacy of each proposed component. All code, models, and datasets will be publicly available.

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BibTeX RIS

Zhongwei Ren, Zhicheng Huang, Yunchao Wei, Yao Zhao, Dongmei Fu, Jiashi Feng, Xiaojie Jin. 2023-12-04. PixelLM: Pixel Reasoning with Large Multimodal Model. https://arxiv.org/abs/2312.02228

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