VSP: Assessing the dual challenges of perception and reasoning in spatial planning tasks for VLMs

• URL: http://arxiv.org/abs/2407.01863v1
• Date: Tue, 2 Jul 2024 00:24:01 GMT
• Title: VSP: Assessing the dual challenges of perception and reasoning in spatial planning tasks for VLMs
• Authors: Qiucheng Wu, Handong Zhao, Michael Saxon, Trung Bui, William Yang Wang, Yang Zhang, Shiyu Chang,
• Abstract summary: Vision language models (VLMs) are an exciting emerging class of language models (LMs) One understudied capability inVLMs is visual spatial planning. Our study introduces a benchmark that evaluates the spatial planning capability in these models in general.
• Score: 102.36953558562436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision language models (VLMs) are an exciting emerging class of language models (LMs) that have merged classic LM capabilities with those of image processing systems. However, the ways that these capabilities combine are not always intuitive and warrant direct investigation. One understudied capability in VLMs is visual spatial planning -- the ability to comprehend the spatial arrangements of objects and devise action plans to achieve desired outcomes in visual scenes. In our study, we introduce VSP, a benchmark that 1) evaluates the spatial planning capability in these models in general, and 2) breaks down the visual planning task into finer-grained sub-tasks, including perception and reasoning, and measure the LMs capabilities in these sub-tasks. Our evaluation shows that both open-source and private VLMs fail to generate effective plans for even simple spatial planning tasks. Evaluations on the fine-grained analytical tasks further reveal fundamental deficiencies in the models' visual perception and bottlenecks in reasoning abilities, explaining their worse performance in the general spatial planning tasks. Our work illuminates future directions for improving VLMs' abilities in spatial planning. Our benchmark is publicly available at https://github.com/UCSB-NLP-Chang/Visual-Spatial-Planning.

Related papers

• Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs [56.391404083287235]
  We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach. Our study uses LLMs and visual instruction tuning as an interface to evaluate various visual representations. We provide model weights, code, supporting tools, datasets, and detailed instruction-tuning and evaluation recipes.
  arXiv  Detail & Related papers  (2024-06-24T17:59:42Z)
• Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs [83.24033574914425]
  We present Prism, an innovative framework designed to disentangle the perception and reasoning processes involved in visual question solving. Prism comprises two distinct stages: a perception stage that utilizes a VLM to extract and articulate visual information in textual form, and a reasoning stage that formulates responses based on the extracted visual information. Our analytical framework provides several valuable insights, underscoring Prism's potential as a cost-effective solution for vision-language tasks.
  arXiv  Detail & Related papers  (2024-06-20T17:54:03Z)
• GSR-BENCH: A Benchmark for Grounded Spatial Reasoning Evaluation via Multimodal LLMs [3.2688425993442696]
  The ability to understand and reason about spatial relationships between objects in images is an important component of visual reasoning. We extend the previously released What'sUp dataset and propose a novel comprehensive evaluation for spatial relationship understanding.
  arXiv  Detail & Related papers  (2024-06-19T06:15:26Z)
• Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs [38.02017186215372]
  Integration of Large Language Models (LLMs) into visual domain tasks, resulting in visual-LLMs (V-LLMs), has enabled exceptional performance in vision-language tasks. However, existing V-LLMs demonstrate weak spatial reasoning and localization awareness. We explore how image-space coordinate based instruction fine-tuning objectives could inject spatial awareness into V-LLMs.
  arXiv  Detail & Related papers  (2024-04-11T03:09:34Z)
• SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors [42.85605789984155]
  Current state-of-the-art spatial reasoning-enhanced VLMs are trained to excel at spatial visual question answering (VQA) We present SpatialPIN, a framework designed to enhance the spatial reasoning capabilities of VLMs through prompting and interacting with priors from multiple 3D foundation models in a zero-shot, training-free manner. Our spatial reasoning-imbued VLM performs well on various forms of spatial VQA and can extend to help in various downstream robotics tasks such as pick and stack and trajectory planning.
  arXiv  Detail & Related papers  (2024-03-18T17:38:29Z)
• Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models [73.40350756742231]
  Visually-conditioned language models (VLMs) have seen growing adoption in applications such as visual dialogue, scene understanding, and robotic task planning. Despite the volume of new releases, key design decisions around image preprocessing, architecture, and optimization are under-explored.
  arXiv  Detail & Related papers  (2024-02-12T18:21:14Z)
• EgoPlan-Bench: Benchmarking Multimodal Large Language Models for Human-Level Planning [84.6451394629312]
  We introduce EgoPlan-Bench, a benchmark to evaluate the planning abilities of MLLMs in real-world scenarios. We show that EgoPlan-Bench poses significant challenges, highlighting a substantial scope for improvement in MLLMs to achieve human-level task planning. We also present EgoPlan-IT, a specialized instruction-tuning dataset that effectively enhances model performance on EgoPlan-Bench.
  arXiv  Detail & Related papers  (2023-12-11T03:35:58Z)
• Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning [32.045840007623276]
  We introduce Robotic Vision-Language Planning (ViLa), a novel approach for long-horizon robotic planning. ViLa directly integrates perceptual data into its reasoning and planning process. Our evaluation, conducted in both real-robot and simulated environments, demonstrates ViLa's superiority over existing LLM-based planners.
  arXiv  Detail & Related papers  (2023-11-29T17:46:25Z)
• LSM: Learning Subspace Minimization for Low-level Vision [78.27774638569218]
  We replace the regularization term with a learnable subspace constraint, and preserve the data term to exploit domain knowledge. This learning subspace minimization (LSM) framework unifies the network structures and the parameters for many low-level vision tasks. We demonstrate our LSM framework on four low-level tasks including interactive image segmentation, video segmentation, stereo matching, and optical flow, and validate the network on various datasets.
  arXiv  Detail & Related papers  (2020-04-20T10:49:38Z)

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.