Are Vision-Language Models Truly Understanding Multi-vision Sensor?

• URL: http://arxiv.org/abs/2412.20750v1
• Date: Mon, 30 Dec 2024 06:44:25 GMT
• Title: Are Vision-Language Models Truly Understanding Multi-vision Sensor?
• Authors: Sangyun Chung, Youngjoon Yu, Youngchae Chee, Se Yeon Kim, Byung-Kwan Lee, Yong Man Ro,
• Abstract summary: Large-scale Vision-Language Models (VLMs) have advanced by aligning vision inputs with text.<n>For real-world applications, an understanding of diverse multi-vision sensor data, such as thermal, depth, and X-ray information, is essential.
• Score: 38.70868031001611
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale Vision-Language Models (VLMs) have advanced by aligning vision inputs with text, significantly improving performance in computer vision tasks. Moreover, for VLMs to be effectively utilized in real-world applications, an understanding of diverse multi-vision sensor data, such as thermal, depth, and X-ray information, is essential. However, we find that current VLMs process multi-vision sensor images without deep understanding of sensor information, disregarding each sensor's unique physical properties. This limitation restricts their capacity to interpret and respond to complex questions requiring multi-vision sensor reasoning. To address this, we propose a novel Multi-vision Sensor Perception and Reasoning (MS-PR) benchmark, assessing VLMs on their capacity for sensor-specific reasoning. Moreover, we introduce Diverse Negative Attributes (DNA) optimization to enable VLMs to perform deep reasoning on multi-vision sensor tasks, helping to bridge the core information gap between images and sensor data. Extensive experimental results validate that the proposed DNA method can significantly improve the multi-vision sensor reasoning for VLMs.

Related papers

• MSSIDD: A Benchmark for Multi-Sensor Denoising [55.41612200877861]
  We introduce a new benchmark, the Multi-Sensor SIDD dataset, which is the first raw-domain dataset designed to evaluate the sensor transferability of denoising models. We propose a sensor consistency training framework that enables denoising models to learn the sensor-invariant features.
  arXiv  Detail & Related papers  (2024-11-18T13:32:59Z)
• SensorBench: Benchmarking LLMs in Coding-Based Sensor Processing [6.8009140511761546]
  Large Language Models (LLMs) have promising capabilities in processing sensory data, suggesting their potential as copilots for developing sensing systems. We construct a comprehensive benchmark, SensorBench, to establish a quantifiable objective. The results show that while LLMs exhibit considerable proficiency in simpler tasks, they face inherent challenges in processing compositional tasks.
  arXiv  Detail & Related papers  (2024-10-14T17:21:39Z)
• SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition [9.072495000412943]
  We bridge the gap between wearable sensor technology and personalized AI assistants by enabling Large Language Models (LLMs) to understand time-series tasks like human activity recognition (HAR) We introduce SensorLLM, a two-stage framework to unlock LLMs' potential for sensor data tasks. We show that SensorLLM evolves into an effective sensor learner, reasoner, and learner, enabling it to generalize across diverse datasets for HAR tasks.
  arXiv  Detail & Related papers  (2024-10-14T15:30:41Z)
• SPARK: Multi-Vision Sensor Perception and Reasoning Benchmark for Large-scale Vision-Language Models [43.79587815909473]
  This paper aims to establish a multi-vision Sensor Perception And Reasoning benchmarK called SPARK. We generated 6,248 vision-language test samples to investigate multi-vision sensory perception and multi-vision sensory reasoning on physical sensor knowledge proficiency. Results showed that most models displayed deficiencies in multi-vision sensory reasoning to varying extents.
  arXiv  Detail & Related papers  (2024-08-22T03:59:48Z)
• By My Eyes: Grounding Multimodal Large Language Models with Sensor Data via Visual Prompting [24.39281384670957]
  We propose a visual prompting approach for sensor data using multimodal large language models (MLLMs) We design a visual prompt that directs MLLMs to utilize visualized sensor data alongside the target sensory task descriptions. We evaluate our approach on nine sensory tasks involving four sensing modalities, achieving an average of 10% higher accuracy than text-based prompts.
  arXiv  Detail & Related papers  (2024-07-15T01:33:54Z)
• MultiPLY: A Multisensory Object-Centric Embodied Large Language Model in 3D World [55.878173953175356]
  We propose MultiPLY, a multisensory embodied large language model. We first collect Multisensory Universe, a large-scale multisensory interaction dataset comprising 500k data. We demonstrate that MultiPLY outperforms baselines by a large margin through a diverse set of embodied tasks.
  arXiv  Detail & Related papers  (2024-01-16T18:59:45Z)
• Learning Online Multi-Sensor Depth Fusion [100.84519175539378]
  SenFuNet is a depth fusion approach that learns sensor-specific noise and outlier statistics. We conduct experiments with various sensor combinations on the real-world CoRBS and Scene3D datasets.
  arXiv  Detail & Related papers  (2022-04-07T10:45:32Z)
• Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition [131.6328804788164]
  We propose a framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) The SAKDN uses multiple wearable-sensors as teacher modalities and uses RGB videos as student modality.
  arXiv  Detail & Related papers  (2020-09-01T03:38:31Z)

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.