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.

通过引入 PixelLM，一种高效且有效的大规模多模态模型，该论文解决了对涉及多个开放世界目标的图像推理任务生成像素级掩码的挑战，并在多个基准测试中表现出色，证实了各个组成部分的有效性。