F-LMM: Grounding Frozen Large Multimodal Models

• URL: http://arxiv.org/abs/2406.05821v1
• Date: Sun, 9 Jun 2024 15:14:26 GMT
• Title: F-LMM: Grounding Frozen Large Multimodal Models
• Authors: Size Wu, Sheng Jin, Wenwei Zhang, Lumin Xu, Wentao Liu, Wei Li, Chen Change Loy,
• Abstract summary: We present F-LMM -- grounding frozen off-the-shelf LMMs in human-AI conversations. Using only a few trainable CNN layers, we can translate word-pixel attention weights to mask logits. Our F-LMM neither learns special segmentation tokens nor utilises high-quality grounded instruction-tuning data.
• Score: 53.8059045627934
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Endowing Large Multimodal Models (LMMs) with visual grounding capability can significantly enhance AIs' understanding of the visual world and their interaction with humans. However, existing methods typically fine-tune the parameters of LMMs to learn additional segmentation tokens and overfit grounding and segmentation datasets. Such a design would inevitably cause a catastrophic diminution in the indispensable conversational capability of general AI assistants. In this paper, we comprehensively evaluate state-of-the-art grounding LMMs across a suite of multimodal question-answering benchmarks, observing pronounced performance drops that indicate vanishing general knowledge comprehension and weakened instruction following ability. To address this issue, we present F-LMM -- grounding frozen off-the-shelf LMMs in human-AI conversations -- a straightforward yet effective design based on the fact that word-pixel correspondences conducive to visual grounding inherently exist in the attention weights of well-trained LMMs. Using only a few trainable CNN layers, we can translate word-pixel attention weights to mask logits, which a SAM-based mask refiner can further optimise. Our F-LMM neither learns special segmentation tokens nor utilises high-quality grounded instruction-tuning data, but achieves competitive performance on referring expression segmentation and panoptic narrative grounding benchmarks while completely preserving LMMs' original conversational ability. Additionally, with instruction-following ability preserved and grounding ability obtained, our F-LMM can perform visual chain-of-thought reasoning and better resist object hallucinations.

Related papers

• LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models [55.903148392998965]
  We introduce LOKI, a novel benchmark designed to evaluate the ability of LMMs to detect synthetic data across multiple modalities. The benchmark includes coarse-grained judgment and multiple-choice questions, as well as fine-grained anomaly selection and explanation tasks. We evaluate 22 open-source LMMs and 6 closed-source models on LOKI, highlighting their potential as synthetic data detectors and also revealing some limitations in the development of LMM capabilities.
  arXiv  Detail & Related papers  (2024-10-13T05:26:36Z)
• Emerging Pixel Grounding in Large Multimodal Models Without Grounding Supervision [29.004844323516412]
  Current large multimodal models (LMMs) face challenges in grounding, which requires the model to relate language components to visual entities. We find that the grounding ability can in fact emerge in LMMs trained without explicit grounding supervision. We propose DIFFLMM, an LMM utilizing a diffusion-based visual encoder, as opposed to the standard CLIP visual encoder, and trained with the same weak supervision.
  arXiv  Detail & Related papers  (2024-10-10T17:59:55Z)
• VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents [50.12414817737912]
  Large Multimodal Models (LMMs) have ushered in a new era in artificial intelligence, merging capabilities in both language and vision to form highly capable Visual Foundation Agents. Existing benchmarks fail to sufficiently challenge or showcase the full potential of LMMs in complex, real-world environments. VisualAgentBench (VAB) is a pioneering benchmark specifically designed to train and evaluate LMMs as visual foundation agents.
  arXiv  Detail & Related papers  (2024-08-12T17:44:17Z)
• Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models [12.687494201105066]
  This paper proposes Traj-LLM, the first to investigate the potential of using Large Language Models (LLMs) to generate future motion from agents' past/observed trajectories and scene semantics. LLMs' powerful comprehension abilities capture a spectrum of high-level scene knowledge and interactive information. Emulating the human-like lane focus cognitive function, we introduce lane-aware probabilistic learning powered by the pioneering Mamba module.
  arXiv  Detail & Related papers  (2024-05-08T09:28:04Z)
• PSALM: Pixelwise SegmentAtion with Large Multi-Modal Model [49.80313655590392]
  PSALM is a powerful extension of the Large Multi-modal Model (LMM) to address the segmentation task challenges. It incorporates a mask decoder and a well-designed input schema to handle a variety of segmentation tasks. The flexible design of PSALM supports joint training across multiple datasets and tasks, leading to improved performance and task generalization.
  arXiv  Detail & Related papers  (2024-03-21T17:50:47Z)
• Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models [87.47400128150032]
  We propose a novel LMM architecture named Lumen, a Large multimodal model with versatile vision-centric capability enhancement. Lumen first promotes fine-grained vision-language concept alignment. Then the task-specific decoding is carried out by flexibly routing the shared representation to lightweight task decoders.
  arXiv  Detail & Related papers  (2024-03-12T04:13:45Z)
• Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels [95.44077384918725]
  We propose to teach large multi-modality models (LMMs) with text-defined rating levels instead of scores. The proposed Q-Align achieves state-of-the-art performance on image quality assessment (IQA), image aesthetic assessment (IAA) and video quality assessment (VQA) tasks.
  arXiv  Detail & Related papers  (2023-12-28T16:10:25Z)
• Compositional Chain-of-Thought Prompting for Large Multimodal Models [46.721769077885966]
  Compositional Chain-of-Thought (CCoT) is a novel zero-shot Chain-of-Thought prompting method. We first generate an SG using the Large Language Model (LLM) and then use that SG in the prompt to produce a response. We find that the proposed CCoT approach not only improves LMM performance but also improves the performance of several popular LMMs on general multimodal benchmarks.
  arXiv  Detail & Related papers  (2023-11-27T22:23:27Z)
• Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and Ethics [32.123919380959485]
  Multi-modal large language models (MLLMs) are trained based on large language models (LLM) While they excel in multi-modal tasks, the pure NLP abilities of MLLMs are often underestimated and left untested. We show that visual instruction tuning, a prevailing strategy for transitioning LLMs into MLLMs, unexpectedly and interestingly helps models attain both improved truthfulness and ethical alignment.
  arXiv  Detail & Related papers  (2023-09-13T17:57:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.