Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities
- URL: http://arxiv.org/abs/2403.04908v3
- Date: Tue, 01 Oct 2024 14:22:15 GMT
- Title: Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities
- Authors: Kaiwen Cai, Zhekai Duan, Gaowen Liu, Charles Fleming, Chris Xiaoxuan Lu,
- Abstract summary: Recent advancements in Vision-Language (VL) models have sparked interest in their deployment on edge devices.
We introduce EdgeVL, a novel framework that seamlessly integrates dual-modality knowledge distillation and quantization-aware contrastive learning.
Our work represents the first systematic effort to adapt large VL models for edge deployment, showcasing up to 15.4% accuracy improvements on multiple datasets and up to 93-fold reduction in model size.
- Score: 11.53488611812612
- License:
- Abstract: Recent advancements in Vision-Language (VL) models have sparked interest in their deployment on edge devices, yet challenges in handling diverse visual modalities, manual annotation, and computational constraints remain. We introduce EdgeVL, a novel framework that bridges this gap by seamlessly integrating dual-modality knowledge distillation and quantization-aware contrastive learning. This approach enables the adaptation of large VL models, like CLIP, for efficient use with both RGB and non-RGB images on resource-limited devices without the need for manual annotations. EdgeVL not only transfers visual language alignment capabilities to compact models but also maintains feature quality post-quantization, significantly enhancing open-vocabulary classification performance across various visual modalities. Our work represents the first systematic effort to adapt large VL models for edge deployment, showcasing up to 15.4% accuracy improvements on multiple datasets and up to 93-fold reduction in model size.
Related papers
- ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification [52.405499816861635]
Multiple instance learning (MIL)-based framework has become the mainstream for processing the whole slide image (WSI)
We propose a dual-scale vision-language multiple instance learning (ViLa-MIL) framework for whole slide image classification.
arXiv Detail & Related papers (2025-02-12T13:28:46Z) - VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models [63.27511432647797]
We propose VLsI: Verbalized Layers-to-Interactions, a new VLM family in 2B and 7B model sizes.
We validate VLsI across ten challenging vision-language benchmarks, achieving notable performance gains (11.0% for 2B and 17.4% for 7B) over GPT-4V.
arXiv Detail & Related papers (2024-12-02T18:58:25Z) - ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning [38.26304604660713]
ADEM-VL is an efficient vision-language method that tunes models based on pretrained large language models.
Our framework surpasses existing methods by an average accuracy of 0.77% on ScienceQA dataset.
arXiv Detail & Related papers (2024-10-23T11:31:06Z) - Fine-Grained Verifiers: Preference Modeling as Next-token Prediction in Vision-Language Alignment [57.0121616203175]
We propose FiSAO, a novel self-alignment method that utilizes the model's own visual encoder as a fine-grained verifier to improve vision-language alignment.
By leveraging token-level feedback from the vision encoder, FiSAO significantly improves vision-language alignment, even surpassing traditional preference tuning methods that require additional data.
arXiv Detail & Related papers (2024-10-18T03:34:32Z) - Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models [32.83187649097727]
We build a dataset of over four million multi-view image-text pairs across more than 100K objects.
We design a novel fine-tuning framework named Omniview-Tuning (OVT)
OVT introduces a Cross-Viewpoint Alignment objective through a minimax-like optimization strategy.
arXiv Detail & Related papers (2024-04-18T12:41:33Z) - Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters [65.15700861265432]
We present a parameter-efficient continual learning framework to alleviate long-term forgetting in incremental learning with vision-language models.
Our approach involves the dynamic expansion of a pre-trained CLIP model, through the integration of Mixture-of-Experts (MoE) adapters.
To preserve the zero-shot recognition capability of vision-language models, we introduce a Distribution Discriminative Auto-Selector.
arXiv Detail & Related papers (2024-03-18T08:00:23Z) - APoLLo: Unified Adapter and Prompt Learning for Vision Language Models [58.9772868980283]
We present APoLLo, a unified multi-modal approach that combines Adapter and Prompt learning for Vision-Language models.
APoLLo achieves a relative gain up to 6.03% over MaPLe (SOTA) on novel classes for 10 diverse image recognition datasets.
arXiv Detail & Related papers (2023-12-04T01:42:09Z) - Teaching Structured Vision&Language Concepts to Vision&Language Models [46.344585368641006]
We introduce the collective notion of Structured Vision&Language Concepts (SVLC)
SVLC includes object attributes, relations, and states which are present in the text and visible in the image.
We propose a more elegant data-driven approach for enhancing VL models' understanding of SVLCs.
arXiv Detail & Related papers (2022-11-21T18:54:10Z) - Enabling Multimodal Generation on CLIP via Vision-Language Knowledge
Distillation [79.72299298976525]
We propose to augment a vision-language pre-training model with a textual pre-trained language model (PLM) via vision-language knowledge distillation (VLKD)
Experiments show that the resulting model has strong zero-shot performance on multimodal generation tasks, such as open-ended visual question answering and image captioning.
The original textual language understanding and generation ability of the PLM is maintained after VLKD, which makes our model versatile for both multimodal and unimodal tasks.
arXiv Detail & Related papers (2022-03-12T09:33:37Z)
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.