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A Survey on Efficient Vision‐Language Models

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01216-8_35openalex-snapshot

Textual Explanations for Self-Driving Vehicles

inferredcites →https://doi.org/10.1007/978-3-030-01364-6_20openalex-snapshot

Radiology Objects in COntext (ROCO): A Multimodal Image Dataset

inferredcites →https://doi.org/10.1007/978-3-030-58577-8_8openalex-snapshot

Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

inferredcites →https://doi.org/10.1007/978-3-030-63076-8_2openalex-snapshot

Deep Leakage from Gradients

inferredcites →https://doi.org/10.1007/978-3-031-19775-8_12openalex-snapshot

PTQ4ViT: Post-training Quantization for Vision Transformers with Twin Uniform Quantization

inferredcites →https://doi.org/10.1007/978-3-031-43904-9_70openalex-snapshot

Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models

inferredcites →https://doi.org/10.1007/978-3-031-43993-3_51openalex-snapshot

PMC-CLIP: Contrastive Language-Image Pre-training Using Biomedical Documents

inferredcites →https://doi.org/10.1007/978-3-031-72658-3_13openalex-snapshot

MMBench: Is Your Multi-modal Model an All-Around Player?

inferredcites →https://doi.org/10.1007/978-3-031-73390-1_18openalex-snapshot

Self-adapting Large Visual-Language Models to Edge Devices Across Visual Modalities

inferredcites →https://doi.org/10.1007/978-3-031-73404-5_14openalex-snapshot

Isomorphic Pruning for Vision Models

inferredcites →https://doi.org/10.1007/978-3-031-82240-7_11openalex-snapshot

Lightweight Unsupervised Federated Learning with Pretrained Vision Language Model

inferredcites →https://doi.org/10.1007/978-3-031-89471-8_43openalex-snapshot

Addressing Failures in Robotics Using Vision-Based Language Models (VLMs) and Behavior Trees (BT)

inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot

Microsoft COCO: Common Objects in Context

inferredcites →https://doi.org/10.1007/978-3-319-46493-0_15openalex-snapshot

A Diagram is Worth a Dozen Images

inferredcites →https://doi.org/10.1007/s11263-018-1116-0openalex-snapshot

Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering

inferredcites →https://doi.org/10.1007/s11263-026-03019-3openalex-snapshot

Efficient Test-Time Prompt Tuning for Vision-Language Models

inferredcites →https://doi.org/10.1007/s11432-024-4235-6openalex-snapshot

OCRBench: on the hidden mystery of OCR in large multimodal models

inferredcites →https://doi.org/10.1007/s44267-025-00099-6openalex-snapshot

Efficient multimodal large language models: a survey

inferredcites →https://doi.org/10.1016/j.cag.2024.01.012openalex-snapshot

A survey of efficient fine-tuning methods for Vision-Language Models — Prompt and Adapter

inferredcites →https://doi.org/10.1016/j.chbah.2025.100200openalex-snapshot

Collaborative human-AI trust (CHAI-T): A process framework for active management of trust in human-AI collaboration

inferredcites →https://doi.org/10.1016/j.jksuci.2019.08.002openalex-snapshot

An efficient technique for image compression and quality retrieval using matrix completion

inferredcites →https://doi.org/10.1016/j.trc.2025.105457openalex-snapshot

V2X-VLM: End-to-End V2X cooperative autonomous driving through large vision-Language models

inferredcites →https://doi.org/10.1109/access.2025.3532853openalex-snapshot

Agentic AI: Autonomous Intelligence for Complex Goals—A Comprehensive Survey

inferredcites →https://doi.org/10.1109/cac53003.2021.9728246openalex-snapshot

Fixed-point Quantization for Vision Transformer

inferredcites →https://doi.org/10.1109/cluster52292.2023.00015openalex-snapshot

Prophet: Fine-grained Load Balancing for Parallel Training of Large-scale MoE Models

inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot

ImageNet: A large-scale hierarchical image database

inferredcites →https://doi.org/10.1109/cvpr.2016.542openalex-snapshot

Yin and Yang: Balancing and Answering Binary Visual Questions

inferredcites →https://doi.org/10.1109/cvpr.2016.571openalex-snapshot

MSR-VTT: A Large Video Description Dataset for Bridging Video and Language

inferredcites →https://doi.org/10.1109/cvpr.2017.345openalex-snapshot

Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning

inferredcites →https://doi.org/10.1109/cvpr.2019.00851openalex-snapshot

Towards VQA Models That Can Read

inferredcites →https://doi.org/10.1109/cvpr.2019.01084openalex-snapshot

Grounding Human-To-Vehicle Advice for Self-Driving Vehicles

inferredcites →https://doi.org/10.1109/cvpr.2019.01282openalex-snapshot

TOUCHDOWN: Natural Language Navigation and Spatial Reasoning in Visual Street Environments

inferredcites →https://doi.org/10.1109/cvpr52688.2022.00516openalex-snapshot

VL-ADAPTER: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks

inferredcites →https://doi.org/10.1109/cvpr52688.2022.01519openalex-snapshot

FLAVA: A Foundational Language And Vision Alignment Model

inferredcites →https://doi.org/10.1109/cvpr52729.2023.00653openalex-snapshot

Visual-Language Prompt Tuning with Knowledge-Guided Context Optimization

inferredcites →https://doi.org/10.1109/cvpr52729.2023.01946openalex-snapshot

NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers

inferredcites →https://doi.org/10.1109/cvpr52729.2023.02170openalex-snapshot

Boost Vision Transformer with GPU-Friendly Sparsity and Quantization

inferredcites →https://doi.org/10.1109/cvpr52733.2024.00529openalex-snapshot

Time-, Memory- and Parameter-Efficient Visual Adaptation

inferredcites →https://doi.org/10.1109/cvpr52733.2024.00913openalex-snapshot

MMMU: A Massive Multi-Discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI

inferredcites →https://doi.org/10.1109/cvpr52733.2024.01306openalex-snapshot

Low-Rank Approximation for Sparse Attention in Multi-Modal LLMs

inferredcites →https://doi.org/10.1109/cvpr52733.2024.01343openalex-snapshot

Efficient Test-Time Adaptation of Vision-Language Models

inferredcites →https://doi.org/10.1109/cvpr52733.2024.01348openalex-snapshot

Building Vision-Language Models on Solid Foundations with Masked Distillation

inferredcites →https://doi.org/10.1109/cvpr52733.2024.01356openalex-snapshot

Multi-Modal Hallucination Control by Visual Information Grounding

inferredcites →https://doi.org/10.1109/cvpr52733.2024.01486openalex-snapshot

PELA: Learning Parameter-Efficient Models with Low-Rank Approximation

inferredcites →https://doi.org/10.1109/cvpr52733.2024.01509openalex-snapshot

PTQ4SAM: Post-Training Quantization for Segment Anything

inferredcites →https://doi.org/10.1109/cvpr52733.2024.01532openalex-snapshot

MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning

inferredcites →https://doi.org/10.1109/cvpr52733.2024.01533openalex-snapshot

MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning

inferredcites →https://doi.org/10.1109/cvpr52733.2024.02191openalex-snapshot

Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters

inferredcites →https://doi.org/10.1109/cvpr52733.2024.02245openalex-snapshot

On the Test-Time Zero-Shot Generalization of Vision-Language Models: Do we Really need Prompt Learning?

inferredcites →https://doi.org/10.1109/cvpr52733.2024.02249openalex-snapshot

MMA: Multi-Modal Adapter for Vision-Language Models

inferredcites →https://doi.org/10.1109/cvpr52733.2024.02513openalex-snapshot

PromptKD: Unsupervised Prompt Distillation for Vision-Language Models

inferredcites →https://doi.org/10.1109/cvpr52733.2024.02537openalex-snapshot

Sonic VisionLM: Playing Sound with Vision Language Models

inferredcites →https://doi.org/10.1109/cvpr52733.2024.02584openalex-snapshot

MoPE-CLIP: Structured Pruning for Efficient Vision-Language Models with Module-Wise Pruning Error Metric

inferredcites →https://doi.org/10.1109/cvpr52734.2025.00394openalex-snapshot

MBQ: Modality-Balanced Quantization for Large Vision-Language Models

inferredcites →https://doi.org/10.1109/cvpr52734.2025.01841openalex-snapshot

FastVLM: Efficient Vision Encoding for Vision Language Models

inferredcites →https://doi.org/10.1109/cvpr52734.2025.02347openalex-snapshot

Minimal Interaction Separated Tuning: A New Paradigm for Visual Adaptation

inferredcites →https://doi.org/10.1109/cvprw63382.2024.00166openalex-snapshot

Low-Rank Few-Shot Adaptation of Vision-Language Models

inferredcites →https://doi.org/10.1109/cvprw67362.2025.00018openalex-snapshot

Online Gaussian Test-Time Adaptation of Vision-Language Models

inferredcites →https://doi.org/10.1109/flics70075.2026openalex-snapshot

2026 2nd International Conference on Federated Learning and Intelligent Computing Systems (FLICS)

inferredcites →https://doi.org/10.1109/gcwkshps52748.2021.9682052openalex-snapshot

Federated Learning in Heterogeneous Networks with Unreliable Communication

inferredcites →https://doi.org/10.1109/icassp55912.2026.11461843openalex-snapshot

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification

inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-snapshot

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

inferredcites →https://doi.org/10.1109/iccv48922.2021.00146openalex-snapshot

Compressing Visual-linguistic Model via Knowledge Distillation

inferredcites →https://doi.org/10.1109/iccv51070.2023.01100openalex-snapshot

Sigmoid Loss for Language Image Pre-Training

inferredcites →https://doi.org/10.1109/iccv51070.2023.02011openalex-snapshot

Distribution-Aware Prompt Tuning for Vision-Language Models

inferredcites →https://doi.org/10.1109/icmew.2019.00050openalex-snapshot

Real-World Image Denoising via Weighted Low Rank Approximation

inferredcites →https://doi.org/10.1109/icra48891.2023.10160614openalex-snapshot

Ground then Navigate: Language-guided Navigation in Dynamic Scenes

inferredcites →https://doi.org/10.1109/icra57147.2024.10610090openalex-snapshot

Physically Grounded Vision-Language Models for Robotic Manipulation

inferredcites →https://doi.org/10.1109/icra57147.2024.10611485openalex-snapshot

Talk2BEV: Language-enhanced Bird’s-eye View Maps for Autonomous Driving

inferredcites →https://doi.org/10.1109/ijcnn64981.2025.11228676openalex-snapshot

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers

inferredcites →https://doi.org/10.1109/infocomwkshps61880.2024.10620835openalex-snapshot

Efficient Adapting for Vision-language Foundation Model in Edge Computing Based on Personalized and Multi-Granularity Federated Learning

inferredcites →https://doi.org/10.1109/iros58592.2024.10801993openalex-snapshot

ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models

inferredcites →https://doi.org/10.1109/jbhi.2022.3207502openalex-snapshot

Multi-Modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training

inferredcites →https://doi.org/10.1109/jiot.2025.3579032openalex-snapshot

Vision-Language Models for Edge Networks: A Comprehensive Survey

inferredcites →https://doi.org/10.1109/lra.2024.3511409openalex-snapshot

VLM-Social-Nav: Socially Aware Robot Navigation Through Scoring Using Vision-Language Models

inferredcites →https://doi.org/10.1109/tac.1980.1102314openalex-snapshot

The singular value decomposition: Its computation and some applications

inferredcites →https://doi.org/10.1109/tcsvt.2025.3551766openalex-snapshot

M2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension

inferredcites →https://doi.org/10.1109/tencon61640.2024.10902892openalex-snapshot

Large Language Models for Video Surveillance Applications

inferredcites →https://doi.org/10.1109/tip.2018.2886712openalex-snapshot

Matrix Completion Based on Non-Convex Low-Rank Approximation

inferredcites →https://doi.org/10.1109/tiv.2024.3402136openalex-snapshot

Vision Language Models in Autonomous Driving: A Survey and Outlook

inferredcites →https://doi.org/10.1109/tmm.2023.3291588openalex-snapshot

Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model

inferredcites →https://doi.org/10.1109/tmm.2024.3453061openalex-snapshot

VLDadaptor: Domain Adaptive Object Detection With Vision-Language Model Distillation

inferredcites →https://doi.org/10.1109/tpami.2024.3369699openalex-snapshot

Vision-Language Models for Vision Tasks: A Survey

inferredcites →https://doi.org/10.1109/tvcg.2025.3549147openalex-snapshot

ViDDAR: Vision Language Model-Based Task-Detrimental Content Detection for Augmented Reality

inferredcites →https://doi.org/10.1109/vrw66409.2025.00039openalex-snapshot

Advancing the Understanding and Evaluation of AR-Generated Scenes: When Vision-Language Models Shine and Stumble

inferredcites →https://doi.org/10.1109/wacv48630.2021.00225openalex-snapshot

DocVQA: A Dataset for VQA on Document Images

inferredcites →https://doi.org/10.1109/wacv56688.2023.00110openalex-snapshot

DRAMA: Joint Risk Localization and Captioning in Driving

inferredcites →https://doi.org/10.1109/wacvw60836.2024.00040openalex-snapshot

TextAug: Test Time Text Augmentation for Multimodal Person Re-Identification

inferredcites →https://doi.org/10.1109/wacvw60836.2024.00107openalex-snapshot

VLAAD: Vision and Language Assistant for Autonomous Driving

inferredcites →https://doi.org/10.1145/3589335.3651939openalex-snapshot

Federated Learning in Large Model Era: Vision-Language Model for Smart City Safety Operation Management

inferredcites →https://doi.org/10.1145/3664647.3680838openalex-snapshot

Advancing Multimodal Large Language Models with Quantization-Aware Scale Learning for Efficient Adaptation

inferredcites →https://doi.org/10.1145/3687060openalex-snapshot

Understanding the Evolvement of Trust Over Time within Human-AI Teams

inferredcites →https://doi.org/10.1145/3731715.3733324openalex-snapshot

Enhancing Adversarial Robustness of Vision-Language Models through Low-Rank Adaptation

inferredcites →https://doi.org/10.1371/journal.pone.0229132openalex-snapshot

Adaptive trust calibration for human-AI collaboration

inferredcites →https://doi.org/10.15607/rss.2021.xvii.044openalex-snapshot

MQA: Answering the Question via Robotic Manipulation

inferredcites →https://doi.org/10.1609/aaai.v38i19.30158openalex-snapshot

Bidirectional Contrastive Split Learning for Visual Question Answering

inferredcites →https://doi.org/10.1609/aaai.v39i22.34512openalex-snapshot

Mitigating Hallucinations in Large Vision-Language Models by Adaptively Constraining Information Flow

inferredcites →https://doi.org/10.18653/v1/2020.findings-emnlp.191openalex-snapshot

MedICaT: A Dataset of Medical Images, Captions, and Textual References

inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.243openalex-snapshot

The Power of Scale for Parameter-Efficient Prompt Tuning

inferredcites →https://doi.org/10.18653/v1/2021.naacl-mainopenalex-snapshot

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

inferredcites →https://doi.org/10.18653/v1/2022.findings-acl.177openalex-snapshot

ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning

inferredcites →https://doi.org/10.18653/v1/2022.findings-naacl.119openalex-snapshot

KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation

inferredcites →https://doi.org/10.18653/v1/2023.acl-long.721openalex-snapshot

PuMer: Pruning and Merging Tokens for Efficient Vision Language Models

inferredcites →https://doi.org/10.18653/v1/2023.findings-eacl.88openalex-snapshot

PubMedCLIP: How Much Does CLIP Benefit Visual Question Answering in the Medical Domain?

inferredcites →https://doi.org/10.18653/v1/2023.findings-emnlp.758openalex-snapshot

Scaling Vision-Language Models with Sparse Mixture of Experts

inferredcites →https://doi.org/10.18653/v1/2023.mrl-1.2openalex-snapshot

Meta-learning For Vision-and-language Cross-lingual Transfer

inferredcites →https://doi.org/10.18653/v1/2024.acl-long.122openalex-snapshot

LRQuant: Learnable and Robust Post-Training Quantization for Large Language Models

inferredcites →https://doi.org/10.18653/v1/2024.acl-long.679openalex-snapshot

Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models

inferredcites →https://doi.org/10.18653/v1/2024.clinicalnlp-1.24openalex-snapshot

SERPENT-VLM : Self-Refining Radiology Report Generation Using Vision Language Models

inferredcites →https://doi.org/10.18653/v1/2024.findings-acl.595openalex-snapshot

Fair Federated Learning with Biased Vision-Language Models

inferredcites →https://doi.org/10.18653/v1/2024.findings-emnlp.221openalex-snapshot

Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models

inferredcites →https://doi.org/10.18653/v1/2024.findings-emnlp.921openalex-snapshot

Advancing Vision-Language Models with Adapter Ensemble Strategies

inferredcites →https://doi.org/10.18653/v1/2024.inlg-main.32openalex-snapshot

Context-aware Visual Storytelling with Visual Prefix Tuning and Contrastive Learning

inferredcites →https://doi.org/10.18653/v1/d19-1215openalex-snapshot

Talk2Car: Taking Control of Your Self-Driving Car

inferredcites →https://doi.org/10.18653/v1/p18-1238openalex-snapshot

Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning

inferredcites →https://doi.org/10.2139/ssrn.4783140openalex-snapshot

Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions

inferredcites →https://doi.org/10.24963/ijcai.2022/762openalex-snapshot

A Survey of Vision-Language Pre-Trained Models

inferredcites →https://doi.org/10.3389/frai.2024.1430984openalex-snapshot

Vision-language models for medical report generation and visual question answering: a review

inferredcites →https://doi.org/10.3390/math12020286openalex-snapshot

VL-Meta: Vision-Language Models for Multimodal Meta-Learning

inferredcites →https://doi.org/10.4230/oasics.icpec.2025.4openalex-snapshot

Can Open Large Language Models Catch Vulnerabilities?

inferredcites →https://doi.org/10.48550/arxiv.1506.02142openalex-snapshot

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

inferredcites →https://doi.org/10.48550/arxiv.1602.05629openalex-snapshot

Communication-Efficient Learning of Deep Networks from Decentralized Data

inferredcites →https://doi.org/10.48550/arxiv.1703.04977openalex-snapshot

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

inferredcites →https://doi.org/10.48550/arxiv.1706.04599openalex-snapshot

On Calibration of Modern Neural Networks

inferredcites →https://doi.org/10.48550/arxiv.1808.07945openalex-snapshot

Maximal Jacobian-based Saliency Map Attack

inferredcites →https://doi.org/10.48550/arxiv.1812.01193openalex-snapshot

e-SNLI: Natural Language Inference with Natural Language Explanations

inferredcites →https://doi.org/10.48550/arxiv.1901.07042openalex-snapshot

MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs

inferredcites →https://doi.org/10.48550/arxiv.1902.00751openalex-snapshot

Parameter-Efficient Transfer Learning for NLP

inferredcites →https://doi.org/10.48550/arxiv.1904.07451openalex-snapshot

Counterfactual Visual Explanations

inferredcites →https://doi.org/10.48550/arxiv.1908.03557openalex-snapshot

VisualBERT: A Simple and Performant Baseline for Vision and Language

inferredcites →https://doi.org/10.48550/arxiv.2012.06946openalex-snapshot

MiniVLM: A Smaller and Faster Vision-Language Model

inferredcites →https://doi.org/10.48550/arxiv.2103.00020openalex-snapshot

Learning Transferable Visual Models From Natural Language Supervision

inferredcites →https://doi.org/10.48550/arxiv.2106.08254openalex-snapshot

BEiT: BERT Pre-Training of Image Transformers

inferredcites →https://doi.org/10.48550/arxiv.2106.13884openalex-snapshot

Multimodal Few-Shot Learning with Frozen Language Models

inferredcites →https://doi.org/10.48550/arxiv.2106.14156openalex-snapshot

Post-Training Quantization for Vision Transformer

inferredcites →https://doi.org/10.48550/arxiv.2201.07703openalex-snapshot

Q-ViT: Fully Differentiable Quantization for Vision Transformer

inferredcites →https://doi.org/10.48550/arxiv.2209.07511openalex-snapshot

Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models

inferredcites →https://doi.org/10.48550/arxiv.2211.10438openalex-snapshot

SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

inferredcites →https://doi.org/10.48550/arxiv.2302.14794openalex-snapshot

Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning

inferredcites →https://doi.org/10.48550/arxiv.2304.10592openalex-snapshot

MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

inferredcites →https://doi.org/10.48550/arxiv.2305.17888openalex-snapshot

LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

inferredcites →https://doi.org/10.48550/arxiv.2307.08691openalex-snapshot

FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

inferredcites →https://doi.org/10.48550/arxiv.2307.16125openalex-snapshot

SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

inferredcites →https://doi.org/10.48550/arxiv.2309.02591openalex-snapshot

Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning

inferredcites →https://doi.org/10.48550/arxiv.2309.14717openalex-snapshot

QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

inferredcites →https://doi.org/10.48550/arxiv.2312.00674openalex-snapshot

LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models

inferredcites →https://doi.org/10.48550/arxiv.2312.16886openalex-snapshot

MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices

inferredcites →https://doi.org/10.48550/arxiv.2401.14159openalex-snapshot

Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

inferredcites →https://doi.org/10.48550/arxiv.2402.04291openalex-snapshot

BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

inferredcites →https://doi.org/10.48550/arxiv.2402.12289openalex-snapshot

DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models

inferredcites →https://doi.org/10.48550/arxiv.2402.14289openalex-snapshot

TinyLLaVA: A Framework of Small-scale Large Multimodal Models

inferredcites →https://doi.org/10.48550/arxiv.2403.12983openalex-snapshot

OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization

inferredcites →https://doi.org/10.48550/arxiv.2403.14119openalex-snapshot

C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion

inferredcites →https://doi.org/10.48550/arxiv.2403.19838openalex-snapshot

Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving

inferredcites →https://doi.org/10.48550/arxiv.2405.09215openalex-snapshot

Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model

inferredcites →https://doi.org/10.48550/arxiv.2405.09818openalex-snapshot

Chameleon: Mixed-Modal Early-Fusion Foundation Models

inferredcites →https://doi.org/10.48550/arxiv.2406.06385openalex-snapshot

Low-Rank Quantization-Aware Training for LLMs

inferredcites →https://doi.org/10.48550/arxiv.2407.00952openalex-snapshot

SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models

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Advances in Neural Information Processing Systems 19

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