Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs | Paper Citation Record

Observation b787420d-6acc-4f61-a52a-79299a9128bc · outbound

This paper cites ManagerTower: Aggregating the insights of uni-modal experts for vision-language representation learning,.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs ManagerTower: Aggregating the insights of uni-modal experts for vision-language representation learning,

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This paper cites Making the V in VQA matter: Elevating the role of image understanding in visual question answering,.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Making the V in VQA matter: Elevating the role of image understanding in visual question answering,

Reference 2

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This paper cites Visual Entailment: A Novel Task for Fine-Grained Image Understanding.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Visual Entailment: A Novel Task for Fine-Grained Image Understanding

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This paper cites A corpus for reasoning about natural language grounded in photographs,.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs A corpus for reasoning about natural language grounded in photographs,

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This paper cites From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions,.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions,

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This paper cites An empirical study of training end-to-end vision-and-language transformers,.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs An empirical study of training end-to-end vision-and-language transformers,

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This paper cites Bridgetower: Building bridges between encoders in vision-language representation learning,.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Bridgetower: Building bridges between encoders in vision-language representation learning,

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

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Learning transferable visual models from natural language supervi- sion,

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This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs RoBERTa: A Robustly Optimized BERT Pretraining Approach

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This paper cites Learning deep transformer models for machine translation,.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Learning deep transformer models for machine translation,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs LLaVA-OneVision: Easy Visual Task Transfer

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This paper cites Llava- next: Improved reasoning, ocr, and world knowledge,.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Llava- next: Improved reasoning, ocr, and world knowledge,

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This paper cites How much can CLIP benefit vision-and-language tasks?.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs How much can CLIP benefit vision-and-language tasks?

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs UNIMO-2: End-to-end unified vision-language grounded learning,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Neural machine translation of rare words with subword units,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Language models are unsupervised multitask learners,

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This paper cites Attention is all you need,.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Attention is all you need,

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This paper cites Vilbert: Pretraining task- agnostic visiolinguistic representations for vision-and-language tasks,.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Vilbert: Pretraining task- agnostic visiolinguistic representations for vision-and-language tasks,

Reference 18

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Multi-layer representation fusion for neural machine translation,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Multiscale collaborative deep models for neural machine translation,

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This paper cites Layer Normalization.

Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Layer Normalization

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Decoupled weight decay regularization,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Vilt: Vision-and-language transformer without convolution or region supervision,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Uniter: Universal image-text representation learning,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs UNIMO: Towards unified-modal understanding and generation via cross-modal contrastive learning,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Align before fuse: Vision and language representation learning with momentum distillation,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Vlmo: Unified vision-language pre- training with mixture-of-modality-experts,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Simvlm: Simple visual language model pretraining with weak supervision,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Im2text: Describing images using 1 million captioned photographs,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Microsoft COCO Captions: Data Collection and Evaluation Server

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Visual genome: Connect- ing language and vision using crowdsourced dense image annotations,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Improved baselines with visual instruction tuning,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Monkey: Image resolution and text label are important things for large multi-modal models,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Docvqa: A dataset for vqa on document images,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Sigmoid loss for language image pre-training,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Qwen2 Technical Report

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Exploring Plain Vision Transformer Backbones for Object Detection

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs OK-VQA: A visual question answering benchmark requiring external knowledge,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs GQA: A new dataset for real-world visual reasoning and compositional question answering,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs MM-vet: Evaluating large multimodal models for integrated capabilities,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Grok-1.5 vision preview

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Towards VQA models that can read,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs ChartQA: A benchmark for question answering about charts with visual and logical reasoning,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Infographicvqa,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs A diagram is worth a dozen images,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Learn to explain: Multimodal reasoning via thought chains for science question answering,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Llava-next: What else influences visual instruction tuning beyond data?

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Visual instruction tuning,

Reference 57

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 58

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Mini-Monkey: Alleviating the Semantic Sawtooth Effect for Lightweight MLLMs via Complementary Image Pyramid

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Neural machine translation by jointly learning to align and translate,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Revealing the dark secrets of masked image modeling,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs On information and sufficiency,

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs VL-BERT: pre-training of generic visual-linguistic representations,

Reference 63

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Unicoder-vl: A universal encoder for vision and language by cross-modal pre-training,

Reference 64

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Oscar: Object-semantics aligned pre-training for vision-language tasks,

Reference 65

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

Reference 66

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 68

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Exploring vision-language foundation model for novel object captioning,

Reference 69

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Unsupervised domain adaption harnessing vision-language pre-training,

Reference 70

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Do vision transformers see like convolutional neural networks?

Reference 71

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Intriguing properties of vision transformers,

Reference 72

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Dissecting contextual word embeddings: Architecture and representation,

Reference 73

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Linguistic knowledge and transferability of contextual representa- tions,

Reference 74

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs What does BERT learn about the structure of language?

Reference 75

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs BERT: Pre- training of deep bidirectional transformers for language understanding,

Reference 76

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Feature pyramid networks for object detection,

Reference 77

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Densely connected convolutional networks,

Reference 78

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Deep layer aggregation,

Reference 79

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 80

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Clsr: Cross-layer interaction pyramid super-resolution network,

Reference 81

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Attention-based layer fusion and token masking for weakly supervised semantic segmentation,

Reference 82

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Artificial- spiking hierarchical networks for vision-language representation learn- ing,

Reference 83

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Deep contextualized word representations,

Reference 84

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Coarse-to-fine vision-language pre-training with fusion in the backbone,

Reference 85

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Dense Connector for MLLMs

Reference 86

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs TokenPacker: Efficient Visual Projector for Multimodal LLM

Reference 87

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Language models are few-shot learners,

Reference 88

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 89

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Large Language Models Meet NLP: A Survey

Reference 90

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models,

Reference 91

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Introducing our multimodal models,

Reference 92

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

Reference 93

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 94

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution

Reference 95

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models

Reference 96

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs When do we not need larger vision models?

Reference 97

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document

Reference 98

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Honeybee: Locality-enhanced projector for multimodal llm,

Reference 99

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Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs Unified language model pre-training for natural language understanding and generation,

Reference 100

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