Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective | Paper Citation Record

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This paper cites A survey of vision-language pre-trained models.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective A survey of vision-language pre-trained models

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This paper cites A survey on multimodal large language models.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective A survey on multimodal large language models

Reference 2

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This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

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This paper cites BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation

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This paper cites CoCa: Contrastive captioners are image-text foundation models.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective CoCa: Contrastive captioners are image-text foundation models

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This paper cites Multimodal few-shot learning with frozen language models.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Multimodal few-shot learning with frozen language models

Reference 6

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This paper cites BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

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This paper cites Visual instruction tuning.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Visual instruction tuning

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This paper cites LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

Reference 9

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This paper cites Enhance Reasoning Ability of Visual-Language Models via Large Language Models.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Enhance Reasoning Ability of Visual-Language Models via Large Language Models

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This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Learn to explain: Multimodal reasoning via thought chains for science question answering

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This paper cites MMBench: Is Your Multi-modal Model an All-around Player?.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective MMBench: Is Your Multi-modal Model an All-around Player?

Reference 12

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This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 13

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This paper cites Efficient multimodal large language models: A survey.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Efficient multimodal large language models: A survey

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This paper cites Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

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This paper cites Flamingo: A visual language model for few-shot learning.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Flamingo: A visual language model for few-shot learning

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This paper cites ClipCap: CLIP Prefix for Image Captioning.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective ClipCap: CLIP Prefix for Image Captioning

Reference 17

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This paper cites MAPL: Parameter-efficient adaptation of unimodal pre-trained models for vision-language few-shot prompting.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective MAPL: Parameter-efficient adaptation of unimodal pre-trained models for vision-language few-shot prompting

Reference 18

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This paper cites Meta learning to bridge vision and language models for multimodal few-shot learning.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Meta learning to bridge vision and language models for multimodal few-shot learning

Reference 19

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This paper cites Grounding language models to images for multimodal inputs and outputs.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Grounding language models to images for multimodal inputs and outputs

Reference 20

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This paper cites MiniGPT-4: Enhancing vision- language understanding with advanced large language models.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective MiniGPT-4: Enhancing vision- language understanding with advanced large language models

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective CogVLM: Visual Expert for Pretrained Language Models

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Zero-shot video question answering via frozen bidirectional language models

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Video-LLaMA: An instruction-tuned audio-visual language model for video understanding

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Improved baselines with visual instruction tuning

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Qwen Technical Report

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective mPLUG-Owl2: Revolutionizing multi-modal large language model with modality collaboration

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective LoRA: Low-rank adaptation of large language models

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective MobileVLM V2: Faster and Stronger Baseline for Vision Language Model

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective VL-Mamba: Exploring State Space Models for Multimodal Learning

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective VL-Adapter: Parameter-efficient transfer learning for vision-and- language tasks

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective eP-ALM: Efficient perceptual augmentation of language models

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Modular and parameter-efficient multimodal fusion with prompting

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Memory-space visual prompting for efficient vision-language fine-tuning

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective LLaMA- Adapter: Efficient fine-tuning of large language models with zero-initialized attention

Reference 37

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective LST: Ladder side-tuning for parameter and memory efficient transfer learning

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Querying as Prompt: Parameter-efficient learning for multimodal language model

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective VL-PET: Vision-and-language parameter-efficient tuning via granularity control

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Cheap and Quick: Efficient vision-language instruction tuning for large language models

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This paper cites https://scholar.google.com/, accessed 3 February 2025.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective https://scholar.google.com/, accessed 3 February 2025

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This paper cites https://ccf.atom.im/, accessed 3 February 2025.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective https://ccf.atom.im/, accessed 3 February 2025

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective MAGMA – Multimodal augmentation of generative models through adapter-based finetuning

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Fusing pre-trained language models with multimodal prompts through reinforcement learning

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Aligning large multimodal models with factually augmented RLHF

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Honeybee: Locality-enhanced projector for multimodal LLM

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Tuning large multimodal models for videos using reinforcement learning from AI feedback

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Attention is all you need

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective RoBERTa: A Robustly Optimized BERT Pretraining Approach

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective DeBERTa: Decoding-enhanced BERT with disentangled attention

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Exploring the limits of transfer learning with a unified text-to-text transformer

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective LLaMA: Open and Efficient Foundation Language Models

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This paper cites VICUNA: An open-source chatbot impressing gpt-4 with 90% chatgpt quality.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective VICUNA: An open-source chatbot impressing gpt-4 with 90% chatgpt quality

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective OPT: Open Pre-trained Transformer Language Models

Reference 57

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective GPT-3: Its nature, scope, limits, and consequences

Reference 58

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective GPT-4 Technical Report

Reference 59

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 60

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This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 61

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Scaling instruction-finetuned language models

Reference 62

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Language models are few-shot learners

Reference 63

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This paper cites GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model [software].

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model [software]

Reference 64

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective An empirical analysis of compute-optimal large language model training

Reference 65

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs [software]

Reference 66

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This paper cites Releasing 3B and 7B RedPajama-INCITE family of models including base, instruction-tuned & chat models [software].

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Releasing 3B and 7B RedPajama-INCITE family of models including base, instruction-tuned & chat models [software]

Reference 67

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 68

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective GLU Variants Improve Transformer

Reference 69

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Prefix-Tuning: Optimizing continuous prompts for generation

Reference 70

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective The power of scale for parameter-efficient prompt tuning

Reference 71

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Parameter-efficient transfer learning for NLP

Reference 72

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective QLoRA: Efficient finetuning of quantized llms

Reference 73

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 74

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Visual prompt tuning

Reference 75

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Exploring versatile generative language model via parameter- efficient transfer learning

Reference 76

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Towards a unified view of parameter-efficient transfer learning

Reference 77

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective AdapterSoup: Weight averaging to improve generalization of pretrained language models

Reference 78

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Multi-Task Deep Neural Networks for Natural Language Understanding

Reference 79

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Multitask learning

Reference 80

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective A survey of multi-task learning in natural language processing: Regarding task relatedness and training methods

Reference 81

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Multi-Task Learning with LLMs for Implicit Sentiment Analysis: Data-level and Task-level Automatic Weight Learning

Reference 82

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Dai, and Quoc V Le

Reference 83

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Training language models to follow instructions with human feedback

Reference 84

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Constitutional AI: Harmlessness from AI Feedback

Reference 85

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 86

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Proximal Policy Optimization Algorithms

Reference 87

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective RLHF-V: Towards trustworthy MLLMs via behavior alignment from fine-grained correctional human feedback

Reference 88

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Learning transferable visual models from natural language supervision

Reference 89

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective An image is worth 16x16 words: Transformers for image recognition at scale

Reference 90

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective High-performance large-scale image recognition without normalization

Reference 91

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Scaling vision transformers

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:04:41.088602Z

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Observation afb0647e-2f7b-4235-8b34-c99094102e1c · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Reproducible scaling laws for contrastive language-image learning

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:04:41.080405Z

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 94

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective OpenCLIP [software], July 2021

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:04:41.072389Z

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Observation 003ac2b0-afb8-40a3-a46c-44fc642ec4c3 · outbound

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Deep residual learning for image recognition

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:04:41.064314Z

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Observation 5c142689-3d6e-4ab9-aa0a-f5ea26551c03 · outbound

This paper cites Linearly mapping from image to text space.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Linearly mapping from image to text space

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:04:41.056430Z

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This paper cites Deformable DETR: Deformable transformers for end-to-end object detection.

Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Deformable DETR: Deformable transformers for end-to-end object detection

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:04:41.048237Z

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Aggregated residual transformations for deep neural networks

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:04:41.039690Z

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Observation c7148fc6-e865-4067-9acf-6d075db9a4b0 · outbound

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Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective Conditional positional encodings for vision transformers

Reference 100

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