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  • InstructBLIP: Towards General-purpose Vision-Language Models with . . .
    Large-scale pre-training and instruction tuning have been successful at creating general-purpose language models with broad competence However, building general-purpose vision-language models is
  • InstructBLIP: Towards General-purpose Vision-Language Models with . . .
    To address the aforementioned challenges, this paper presents InstructBLIP, a vision-language instruction tuning framework that enables general-purpose models to solve a wide range of visual-language tasks through a unified natural language interface InstructBLIP uses a diverse set of instruction data to train a multimodal LLM
  • X-InstructBLIP: A Framework for aligning X-Modal instruction-aware . . .
    This paper proposed X-InstructBLIP for aligning multimodal to LLMs, by extending InstructBLIP to more modalities such as image, audio, video and point clouds Besides, this paper collect 31K audio QA data and 250K point cloud QA data, and contribute a discriminative cross-modal reasoning evaluation task
  • X-INSTRUCTBLIP: A FRAMEWORK FOR ALIGNING X-
    tion being trained individually, X-InstructBLIP shows strong joint and cross-modal reasoning abilities Table 4 demonstrates X-InstructBLIP’s capability to rea-son jointly over video (V) a
  • AntifakePrompt: Prompt-Tuned Vision-Language Models are . . . - OpenReview
    In this paper, being inspired by the zero-shot advantages of Vision-Language Models (VLMs), we propose a novel approach using VLMs (e g InstructBLIP) and prompt tuning techniques to improve the deepfake detection accuracy over unseen data
  • Dongxu Li - OpenReview
    InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning Wenliang Dai, Junnan Li, Dongxu Li, Anthony Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi Published: 21 Sept 2023, Last Modified: 02 Nov 2023 NeurIPS 2023 poster
  • Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative. . .
    The InstructBLIP's training datasets share considerable similarity with tasks in the DEMON and MME benchmarks For instance, the OCR-VQA dataset used in InstructBLIP is also employed in the DEMON benchmark
  • MiniGPT-4: Enhancing Vision-Language Understanding with Advanced. . .
    It surpasses InstructBlip in several key areas: logical reasoning (LR), fine-grained perception for single instance (FP-S), and fine-grained perception across instances (FP-C) Additionally, MiniGPT-4 achieves competitive results in relation reasoning (RR), attribute reasoning (AR), and coarse perception (CP)
  • Qwen-VL: A Versatile Vision-Language Model for Understanding. . .
    In this work, we introduce the Qwen-VL series, a set of large-scale vision-language models (LVLMs) designed to perceive and understand both texts and images Starting from the Qwen-LM as a
  • LLaMA-Adapter: Efficient Fine-tuning of Large Language Models with. . .
    Compared to the latest InstructBLIP and LLaVA-1 5, our approach can attain competitive results, while using less instruction tuning data and adopting parameter-efficient tuning This demonstrates our method is still effective and efficient in the multi-modal domain Q3: More quantitative comparisons with Alpaca and Alpaca-LoRA Thanks for your





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