Perception Tokens Enhance Visual Reasoning in Multimodal Language Models

Multimodal language models (MLMs) continue to struggle with fundamental visual perception tasks that specialist models excel at tasks that require reasoning over 3D benefit from depth estimation; similarly, reasoning 2D over object instances benefits from object detection. Yet, MLMs can not produce intermediate depth or boxes to reason over.Finetuning MLMs on relevant data doesn't generalize well and outsourcing computation to specialized vision tools is too compute-intensive and memory-inefficient.To address this, we introduce Perception Tokens, intrinsic image representations designed to assist reasoning tasks where language is insufficient. Perception tokens act as auxiliary reasoning tokens, akin to chain-of-thought prompts in language models. For example, in a depth-related task, an MLM augmented with perception tokens can reason by generating a depth map as tokens, enabling it to solve the problem effectively.We propose Aurora, a training method that augments MLMs with perception tokens for improved reasoning over visual inputs. Aurora employs a VQVAE to transform intermediate image representations, such as depth maps or bounding boxes, into a tokenized format, which is then used in a multi-task training framework.Aurora achieves notable improvements across counting benchmarks: $+10.8\%$ on BLINK, $+11.3\%$ on CVBench, and $+8.3\%$ on SEED-Bench, outperforming finetuning approaches in generalization across datasets. It also improves on relative depth: over $+6\%$ on BLINK.With perception tokens, Aurora expands the scope of MLMs beyond language-based reasoning, paving the way for more effective visual reasoning capabilities.