Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models | Paper Citation Record

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This paper cites GPT-4 Technical Report.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models GPT-4 Technical Report

Reference 1

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This paper cites Expert gate: Lifelong learning with a network of experts.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Expert gate: Lifelong learning with a network of experts

Reference 2

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This paper cites Memory aware synapses: Learning what (not) to forget.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Memory aware synapses: Learning what (not) to forget

Reference 3

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This paper cites Generative multi-modal models are good class incremental learners.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Generative multi-modal models are good class incremental learners

Reference 4

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This paper cites Honeybee: Locality-enhanced projector for multimodal llm.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Honeybee: Locality-enhanced projector for multimodal llm

Reference 5

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This paper cites Riemannian walk for incremen- tal learning: Understanding forgetting and intransigence.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Riemannian walk for incremen- tal learning: Understanding forgetting and intransigence

Reference 6

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This paper cites CoIN: A benchmark of continual instruction tuning for multimodel large language models.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models CoIN: A benchmark of continual instruction tuning for multimodel large language models

Reference 7

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This paper cites Lifelong language pretraining with distribution-specialized experts.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Lifelong language pretraining with distribution-specialized experts

Reference 8

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This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, march 2023.URL https://lmsys.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, march 2023.URL https://lmsys

Reference 9

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This paper cites V ocabulary-free image classification.Advances in Neural Information Processing Systems, 36:30662–30680, 2023.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models V ocabulary-free image classification.Advances in Neural Information Processing Systems, 36:30662–30680, 2023

Reference 10

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This paper cites InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

Reference 11

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This paper cites RATT: Recurrent attention to transient tasks for continual image captioning.Advances in Neural Information Processing Systems, 33:16736–16748,.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models RATT: Recurrent attention to transient tasks for continual image captioning.Advances in Neural Information Processing Systems, 33:16736–16748,

Reference 12

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This paper cites DyTox: Transformers for continual learning with dynamic token expansion.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models DyTox: Transformers for continual learning with dynamic token expansion

Reference 13

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This paper cites EV A: Exploring the limits of masked visual representa- tion learning at scale.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models EV A: Exploring the limits of masked visual representa- tion learning at scale

Reference 14

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This paper cites Beyond prompt learning: Continual adapter for efficient rehearsal-free continual learning.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Beyond prompt learning: Continual adapter for efficient rehearsal-free continual learning

Reference 15

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This paper cites Continual Instruction Tuning for Large Multimodal Models.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Continual Instruction Tuning for Large Multimodal Models

Reference 16

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This paper cites The many faces of robust- ness: A critical analysis of out-of-distribution generalization.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models The many faces of robust- ness: A critical analysis of out-of-distribution generalization

Reference 17

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This paper cites CLIPScore: A reference-free evaluation metric for image captioning.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models CLIPScore: A reference-free evaluation metric for image captioning

Reference 18

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 19

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This paper cites Adaptive mixtures of local experts.Neu- ral computation, 3(1):79–87, 1991.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Adaptive mixtures of local experts.Neu- ral computation, 3(1):79–87, 1991

Reference 20

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This paper cites Vi- sual prompt tuning.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Vi- sual prompt tuning

Reference 21

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This paper cites Helpful or harmful: Inter- task association in continual learning.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Helpful or harmful: Inter- task association in continual learning

Reference 22

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This paper cites Growing a brain with sparsity-inducing genera- tion for continual learning.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Growing a brain with sparsity-inducing genera- tion for continual learning

Reference 23

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This paper cites Deep visual-semantic align- ments for generating image descriptions.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Deep visual-semantic align- ments for generating image descriptions

Reference 24

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This paper cites Overcoming catastrophic forgetting in neu- ral networks.Proceedings of the National Academy of Sci- ences of the United States of America, 114(13):3521–3526,.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Overcoming catastrophic forgetting in neu- ral networks.Proceedings of the National Academy of Sci- ences of the United States of America, 114(13):3521–3526,

Reference 25

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This paper cites Quantifying the Carbon Emissions of Machine Learning.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Quantifying the Carbon Emissions of Machine Learning

Reference 26

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This paper cites GShard: Scaling giant models with conditional computation and automatic sharding.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models GShard: Scaling giant models with conditional computation and automatic sharding

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Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

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

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 29

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Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Learning without forgetting

Reference 30

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Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models InfLoRA: Interference-free low-rank adaptation for continual learning

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This paper cites Improved baselines with visual instruction tuning.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Improved baselines with visual instruction tuning

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This paper cites Visual instruction tuning.Advances in neural information processing systems, 36, 2024.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Visual instruction tuning.Advances in neural information processing systems, 36, 2024

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This paper cites Adaptive aggregation networks for class-incremental learning.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Adaptive aggregation networks for class-incremental learning

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Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Unified-IO 2: Scaling autoregressive multimodal models with vision language audio and action

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This paper cites Not all ex- perts are equal: Efficient expert pruning and skipping for mixture-of-experts large language models.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Not all ex- perts are equal: Efficient expert pruning and skipping for mixture-of-experts large language models

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This paper cites Catastrophic inter- ference in connectionist networks: The sequential learning problem.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Catastrophic inter- ference in connectionist networks: The sequential learning problem

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This paper cites Multimodal contrastive learn- ing with limoe: the language-image mixture of experts.Ad- vances in Neural Information Processing Systems, 35:9564– 9576, 2022.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Multimodal contrastive learn- ing with limoe: the language-image mixture of experts.Ad- vances in Neural Information Processing Systems, 35:9564– 9576, 2022

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This paper cites Flickr30k entities: Collecting region-to-phrase corre- spondences for richer image-to-sentence models.Interna- tional Journal of Computer Vision, 123:74–93, 2017.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Flickr30k entities: Collecting region-to-phrase corre- spondences for richer image-to-sentence models.Interna- tional Journal of Computer Vision, 123:74–93, 2017

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This paper cites Learning transferable visual models from natural language supervi- sion.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Learning transferable visual models from natural language supervi- sion

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This paper cites iCaRL: Incremental clas- sifier and representation learning.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models iCaRL: Incremental clas- sifier and representation learning

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This paper cites Exploring models and data for image question answering.Advances in neural information processing systems, 28, 2015.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Exploring models and data for image question answering.Advances in neural information processing systems, 28, 2015

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This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

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This paper cites CODA-prompt: Con- tinual decomposed attention-based prompting for rehearsal- free continual learning.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models CODA-prompt: Con- tinual decomposed attention-based prompting for rehearsal- free continual learning

Reference 44

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This paper cites Constrained contrastive distribution learning for unsupervised anomaly detection and localisation in med- ical images.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Constrained contrastive distribution learning for unsupervised anomaly detection and localisation in med- ical images

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This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

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This paper cites DualPrompt: Complementary prompting for rehearsal-free continual learning.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models DualPrompt: Complementary prompting for rehearsal-free continual learning

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This paper cites Learning to prompt for con- tinual learning.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Learning to prompt for con- tinual learning

Reference 48

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This paper cites mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

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This paper cites Boosting continual learning of vision-language models via mixture-of-experts adapters.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Boosting continual learning of vision-language models via mixture-of-experts adapters

Reference 50

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This paper cites Contin- ual learning through synaptic intelligence.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Contin- ual learning through synaptic intelligence

Reference 51

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This paper cites Investigating the catastrophic for- getting in multimodal large language models.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Investigating the catastrophic for- getting in multimodal large language models

Reference 52

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This paper cites Prompt-Aware Adapter: Towards Learning Adaptive Visual Tokens for Multimodal Large Language Models.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Prompt-Aware Adapter: Towards Learning Adaptive Visual Tokens for Multimodal Large Language Models

Reference 53

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Observation 7893dd5b-5c4d-451e-a26d-18c1465b73f8 · outbound

This paper cites Preventing zero-shot transfer degradation in continual learning of vision-language models.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models Preventing zero-shot transfer degradation in continual learning of vision-language models

Reference 54

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This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 55

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