An LLM-Empowered Low-Resolution Vision System for On-Device Human Behavior Understanding

• URL: http://arxiv.org/abs/2505.01743v1
• Date: Sat, 03 May 2025 08:46:04 GMT
• Title: An LLM-Empowered Low-Resolution Vision System for On-Device Human Behavior Understanding
• Authors: Siyang Jiang, Bufang Yang, Lilin Xu, Mu Yuan, Yeerzhati Abudunuer, Kaiwei Liu, Liekang Zeng, Hongkai Chen, Zhenyu Yan, Xiaofan Jiang, Guoliang Xing,
• Abstract summary: We propose a novel, labor-saving system, Llambda, designed to support low-resolution HBU.<n>The core idea is to leverage limited labeled data and a large amount of unlabeled data to guide LLMs in generating informative captions.<n>Llambda outperforms several state-of-the-art LVLM systems up to $40.03%$ on average Bert-Score.
• Score: 7.588486998437453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancements in Large Vision Language Models (LVLMs) offer the potential to surpass conventional labeling by generating richer, more detailed descriptions of on-device human behavior understanding (HBU) in low-resolution vision systems, such as depth, thermal, and infrared. However, existing large vision language model (LVLM) approaches are unable to understand low-resolution data well as they are primarily designed for high-resolution data, such as RGB images. A quick fixing approach is to caption a large amount of low-resolution data, but it requires a significant amount of labor-intensive annotation efforts. In this paper, we propose a novel, labor-saving system, Llambda, designed to support low-resolution HBU. The core idea is to leverage limited labeled data and a large amount of unlabeled data to guide LLMs in generating informative captions, which can be combined with raw data to effectively fine-tune LVLM models for understanding low-resolution videos in HBU. First, we propose a Contrastive-Oriented Data Labeler, which can capture behavior-relevant information from long, low-resolution videos and generate high-quality pseudo labels for unlabeled data via contrastive learning. Second, we propose a Physical-Knowledge Guided Captioner, which utilizes spatial and temporal consistency checks to mitigate errors in pseudo labels. Therefore, it can improve LLMs' understanding of sequential data and then generate high-quality video captions. Finally, to ensure on-device deployability, we employ LoRA-based efficient fine-tuning to adapt LVLMs for low-resolution data. We evaluate Llambda using a region-scale real-world testbed and three distinct low-resolution datasets, and the experiments show that Llambda outperforms several state-of-the-art LVLM systems up to $40.03\%$ on average Bert-Score.

Related papers

• LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data Generation [41.97593224447291]
  This paper presents a systematic method to enhance visual grounding by utilizing decoder layers of the Large Language Models (LLMs)<n>We demonstrate that intermediate hidden states from early LLM layers retain strong spatial-semantic correlations that are beneficial to grounding tasks.<n> Experiments show that our adaptation strategy significantly enhances the performance on complex free-form text queries.
  arXiv  Detail & Related papers  (2025-03-18T00:50:40Z)
• OLA-VLM: Elevating Visual Perception in Multimodal LLMs with Auxiliary Embedding Distillation [95.78870389271832]
  The standard practice for developing contemporary MLLMs is to feed features from vision encoder(s) into the LLM and train with natural language supervision.<n>We propose OLA-VLM, the first approach distilling knowledge into the LLM's hidden representations from a set of target visual representations.<n>We show that OLA-VLM boosts performance by an average margin of up to 2.5% on various benchmarks, with a notable improvement of 8.7% on the Depth task in CV-Bench.
  arXiv  Detail & Related papers  (2024-12-12T18:55:18Z)
• AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning [19.68349294206012]
  We propose a training-free adaptive inference method for multi-modal LLMs.<n>With a minimalist design, our method can be applied to both video and image LLMs.<n>Under a similar computational cost, our method outperforms the state-of-the-art methods in long video understanding.
  arXiv  Detail & Related papers  (2024-12-04T11:47:57Z)
• Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data [54.934578742209716]
  In real-world NLP applications, Large Language Models (LLMs) offer promising solutions due to their extensive training on vast datasets.<n>LLKD is an adaptive sample selection method that incorporates signals from both the teacher and student.<n>Our comprehensive experiments show that LLKD achieves superior performance across various datasets with higher data efficiency.
  arXiv  Detail & Related papers  (2024-11-12T18:57:59Z)
• GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval [80.96706764868898]
  We present a new Low-light Image Enhancement (LLIE) network via Generative LAtent feature based codebook REtrieval (GLARE) We develop a generative Invertible Latent Normalizing Flow (I-LNF) module to align the LL feature distribution to NL latent representations, guaranteeing the correct code retrieval in the codebook. Experiments confirm the superior performance of GLARE on various benchmark datasets and real-world data.
  arXiv  Detail & Related papers  (2024-07-17T09:40:15Z)
• One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
  Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs)<n>We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
  arXiv  Detail & Related papers  (2024-05-30T03:44:54Z)
• Large Language Model with Graph Convolution for Recommendation [21.145230388035277]
  Text information can sometimes be of low quality, hindering its effectiveness for real-world applications. With knowledge and reasoning capabilities capsuled in Large Language Models, utilizing LLMs emerges as a promising way for description improvement. We propose a Graph-aware Convolutional LLM method to elicit LLMs to capture high-order relations in the user-item graph.
  arXiv  Detail & Related papers  (2024-02-14T00:04:33Z)
• Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance [56.04768229686853]
  Large Vision-Language Models (LVLMs) tend to hallucinate non-existing objects in the images. We introduce a framework called Mitigating hallucinAtion via classifieR-Free guIdaNcE (MARINE) MARINE is both training-free and API-free, and can effectively and efficiently reduce object hallucinations during the generation process.
  arXiv  Detail & Related papers  (2024-02-13T18:59:05Z)
• Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes [57.62036621319563]
  We introduce CLLM, which leverages the prior knowledge of Large Language Models (LLMs) for data augmentation in the low-data regime. We demonstrate the superior performance of CLLM in the low-data regime compared to conventional generators.
  arXiv  Detail & Related papers  (2023-12-19T12:34:46Z)
• HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving [44.06475712570428]
  HiLM-D is a resource-efficient framework that enhances visual information processing in MLLMs for ROLISP.<n>Our method is motivated by the fact that the primary variations in autonomous driving scenarios are the motion trajectories.<n>Experiments show HiLM-D's significant improvements over current MLLMs, with a 3.7% in BLEU-4 for captioning and 8.7% in mIoU for detection.
  arXiv  Detail & Related papers  (2023-09-11T01:24:13Z)
• VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment [52.489874804051304]
  VoLTA is a new vision-language pre-training paradigm that only utilizes image-caption data but fine-grained region-level image understanding. VoLTA pushes multi-modal fusion deep into the uni-modal backbones during pre-training. Experiments on a wide range of vision- and vision-language downstream tasks demonstrate the effectiveness of VoLTA.
  arXiv  Detail & Related papers  (2022-10-09T01:49:58Z)

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.