Vision Generalist Model: A Survey | Paper Citation Record

Observation 170492ca-7001-4dd5-b2c7-0adcd413ce29 · outbound

This paper cites GPT-4 Technical Report.

Vision Generalist Model: A Survey GPT-4 Technical Report

Reference 1

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Observation 11b6f81b-f26d-4862-8abb-66cd72e73c3e · outbound

This paper cites Deep Learning using Rectified Linear Units (ReLU).

Vision Generalist Model: A Survey Deep Learning using Rectified Linear Units (ReLU)

Reference 2

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Observation fd7ab959-d6a4-4d1d-8a68-11faf61f2e16 · outbound

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

Vision Generalist Model: A Survey Flamingo: a visual language model for few-shot learning

Reference 3

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Observation dda91768-a04a-4500-8903-48c57bc8f19f · outbound

This paper cites Bottom-up and top-down attention for image captioning and visual question answering.

Vision Generalist Model: A Survey Bottom-up and top-down attention for image captioning and visual question answering

Reference 4

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Observation b3433fa7-8647-45a5-a85b-412f0a3ab4e9 · outbound

This paper cites Neural module networks.

Vision Generalist Model: A Survey Neural module networks

Reference 5

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Observation d088b8bf-1a37-4879-abb3-8a7b17cf882a · outbound

This paper cites Vqa: Visual question answering.

Vision Generalist Model: A Survey Vqa: Visual question answering

Reference 6

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Observation 8be7d67e-4201-4abc-acb6-e92aa4708ba6 · outbound

This paper cites MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens.

Vision Generalist Model: A Survey MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens

Reference 7

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Observation 92a4b703-3a1e-4f75-a2fa-9085b94f0e45 · outbound

This paper cites Foundational Models Defining a New Era in Vision: A Survey and Outlook.

Vision Generalist Model: A Survey Foundational Models Defining a New Era in Vision: A Survey and Outlook

Reference 8

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Observation 4d794112-46c5-4cc3-99bb-02288db94b87 · outbound

This paper cites Layer Normalization.

Vision Generalist Model: A Survey Layer Normalization

Reference 9

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Observation da406ff8-ded6-4204-a7c5-c206e9a54b5d · outbound

This paper cites Multimae: Multi-modal multi-task masked autoencoders.

Vision Generalist Model: A Survey Multimae: Multi-modal multi-task masked autoencoders

Reference 10

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Observation eddf264e-d766-4325-b410-eab7a6996f9f · outbound

This paper cites Graph Perceiver IO: A General Architecture for Graph Structured Data.

Vision Generalist Model: A Survey Graph Perceiver IO: A General Architecture for Graph Structured Data

Reference 11

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Observation 55253928-6d62-4585-8989-bc968d08efce · outbound

This paper cites Data2vec: A general framework for self-supervised learning in speech, vision and language.

Vision Generalist Model: A Survey Data2vec: A general framework for self-supervised learning in speech, vision and language

Reference 12

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Observation d1f3a525-27a8-4630-a34b-89abd0681f0e · outbound

This paper cites OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models.

Vision Generalist Model: A Survey OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models

Reference 13

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Observation bf4d225b-6fa5-4514-bf1a-065548e5d0fe · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Vision Generalist Model: A Survey Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 14

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Observation 127adca0-bb3f-4319-8cbc-aea36c14eda3 · outbound

This paper cites Qwen2.5-VL Technical Report.

Vision Generalist Model: A Survey Qwen2.5-VL Technical Report

Reference 15

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Observation f0484fda-7151-4cb3-8b01-1c6adab20335 · outbound

This paper cites Sequential modeling enables scalable learning for large vision models.

Vision Generalist Model: A Survey Sequential modeling enables scalable learning for large vision models

Reference 16

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Observation 81e0a385-e574-44b8-84f6-362c34c4d02a · outbound

This paper cites Visual prompting via image inpainting.

Vision Generalist Model: A Survey Visual prompting via image inpainting

Reference 17

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Observation 3fd5a6a7-ad46-4fb6-b29d-59a30bc2ac10 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Vision Generalist Model: A Survey Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 18

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Observation 59a1f3ea-9d07-4c95-bde1-319ffce3e17a · outbound

This paper cites Mult: An end-to-end multitask learning transformer.

Vision Generalist Model: A Survey Mult: An end-to-end multitask learning transformer

Reference 19

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Observation d3e906be-d1bc-428e-bcac-c705dee63da9 · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

Vision Generalist Model: A Survey RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 20

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Observation 3527e882-6dab-4a35-ae35-e46fa12c112e · outbound

This paper cites What one intelligence test measures: a theoretical account of the processing in the raven progressive matrices test.

Vision Generalist Model: A Survey What one intelligence test measures: a theoretical account of the processing in the raven progressive matrices test

Reference 21

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Observation f8c778f6-75ea-4d8d-9587-6f59cba1f1bb · outbound

This paper cites HiP: Hierarchical Perceiver.

Vision Generalist Model: A Survey HiP: Hierarchical Perceiver

Reference 22

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Observation d3b70e7e-b3d8-45c1-8cf4-919570648fb2 · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

Vision Generalist Model: A Survey Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 23

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Observation ef691314-189e-4fde-9261-15bab6d652eb · outbound

This paper cites MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning.

Vision Generalist Model: A Survey MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:38.064012Z

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Observation 6aca9191-08e3-412e-818a-2e2fa2143fde · outbound

This paper cites Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic.

Vision Generalist Model: A Survey Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:38.403191Z

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Observation 68732d81-8f1b-4a22-abae-203b7895dc93 · outbound

This paper cites Autoformer: Searching transformers for visual recognition.

Vision Generalist Model: A Survey Autoformer: Searching transformers for visual recognition

Reference 26

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Observation 20d558c9-03d1-460a-bf15-f7bd0da26a2b · outbound

This paper cites A Generalist Framework for Panoptic Segmentation of Images and Videos.

Vision Generalist Model: A Survey A Generalist Framework for Panoptic Segmentation of Images and Videos

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:44:08.653600Z

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Observation b7cbd325-4c3d-404c-9a13-e4a87d1106bd · outbound

This paper cites A unified sequence interface for vision tasks.

Vision Generalist Model: A Survey A unified sequence interface for vision tasks

Reference 28

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Observation 5b8afdf7-8fad-4379-8551-339e586231ee · outbound

This paper cites PaLI: A Jointly-Scaled Multilingual Language-Image Model.

Vision Generalist Model: A Survey PaLI: A Jointly-Scaled Multilingual Language-Image Model

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:39.103950Z

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Observation 6d47b4a3-6534-4612-9afc-9bfa39b894ce · outbound

This paper cites PaLI-X: On Scaling up a Multilingual Vision and Language Model.

Vision Generalist Model: A Survey PaLI-X: On Scaling up a Multilingual Vision and Language Model

Reference 31

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Observation 56a37139-f307-4f1d-b085-0b1804378a07 · outbound

This paper cites Learning a low-level vision generalist via visual task prompt.

Vision Generalist Model: A Survey Learning a low-level vision generalist via visual task prompt

Reference 32

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Observation 86253100-b9d1-466e-872a-67e9ae83c7dc · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Vision Generalist Model: A Survey Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 33

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Observation 3449b306-e90d-4667-8150-d155d9118250 · outbound

This paper cites Uniter: Universal image-text representation learning.

Vision Generalist Model: A Survey Uniter: Universal image-text representation learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:39.786113Z

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Observation 808eb52b-9a43-4682-89a6-bbda36e0dd47 · outbound

This paper cites Uniter: Universal image-text representation learning.

Vision Generalist Model: A Survey Uniter: Universal image-text representation learning

Reference 35

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Observation ef054bdd-1664-49f3-9945-9a196a359437 · outbound

This paper cites Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks.

Vision Generalist Model: A Survey Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks

Reference 36

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Observation 1254a781-eb71-4467-895a-e0596341041a · outbound

This paper cites Per-pixel classification is not all you need for semantic segmentation.

Vision Generalist Model: A Survey Per-pixel classification is not all you need for semantic segmentation

Reference 37

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Observation 6f797c5f-c547-49b1-8f41-2270661b9308 · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

Vision Generalist Model: A Survey Masked-attention mask transformer for universal image segmentation

Reference 38

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Observation 6818b099-ca34-4e96-8c1c-65b719307360 · outbound

This paper cites Unifying vision-and-language tasks via text generation.

Vision Generalist Model: A Survey Unifying vision-and-language tasks via text generation

Reference 39

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Observation dcc5ce5f-76eb-4247-8dea-4353601196d6 · outbound

This paper cites Conditional Positional Encodings for Vision Transformers.

Vision Generalist Model: A Survey Conditional Positional Encodings for Vision Transformers

Reference 40

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Observation 394a616e-3668-4058-adba-c3c6372cd344 · outbound

This paper cites Instance-aware semantic segmentation via multi-task network cascades.

Vision Generalist Model: A Survey Instance-aware semantic segmentation via multi-task network cascades

Reference 41

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Observation 5d7f965a-1865-44fc-978d-b74a856fa843 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Vision Generalist Model: A Survey BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 42

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Observation 7c327d4b-604d-40e0-97fe-0b3d6755a824 · outbound

This paper cites Decoupling zero-shot semantic segmentation.

Vision Generalist Model: A Survey Decoupling zero-shot semantic segmentation

Reference 43

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Observation f74f887e-624d-4bc1-b7b7-c877a16aa6c6 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Vision Generalist Model: A Survey An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 44

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Observation 76496712-ffb0-4f33-a0c8-dcf07cc78315 · outbound

This paper cites PaLM-E: An Embodied Multimodal Language Model.

Vision Generalist Model: A Survey PaLM-E: An Embodied Multimodal Language Model

Reference 45

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Observation 27e29feb-4249-43a0-80c4-70078c272ae5 · outbound

This paper cites Glam: Efficient scaling of language models with mixture-of-experts.

Vision Generalist Model: A Survey Glam: Efficient scaling of language models with mixture-of-experts

Reference 46

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Observation fd737cc9-e891-4ce8-8965-e2194fa52c25 · outbound

This paper cites Multi-modal alignment using representation codebook.

Vision Generalist Model: A Survey Multi-modal alignment using representation codebook

Reference 47

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Observation 8a70b3b4-f3b5-4b38-aa5a-dbcb44eb9a9b · outbound

This paper cites Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser.

Vision Generalist Model: A Survey Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser

Reference 48

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Observation 7f022ef0-546d-47ef-87af-fef343b19160 · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Vision Generalist Model: A Survey Taming transformers for high-resolution image synthesis

Reference 49

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Observation 3bf1de95-4651-418a-9a3e-43aa77d84d8c · outbound

This paper cites Mmbench-video: A long-form multi-shot benchmark for holistic video understanding.

Vision Generalist Model: A Survey Mmbench-video: A long-form multi-shot benchmark for holistic video understanding

Reference 50

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Observation a1c4fa58-0474-4b07-93af-27137f993a10 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

Vision Generalist Model: A Survey Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 51

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Observation 31a5d409-1186-47a8-b152-dc606bfc46eb · outbound

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

Vision Generalist Model: A Survey MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:41.874281Z

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Observation 6bce6f40-7fc0-436d-b6ef-710168cd00f2 · outbound

This paper cites Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis.

Vision Generalist Model: A Survey Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:41.983897Z

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Observation 1198782c-3978-4d7f-bd57-b5177a52c381 · outbound

This paper cites MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs.

Vision Generalist Model: A Survey MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:42.097080Z

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Observation 9aa2c5a8-29d4-4069-bd67-2e16e1ac11fb · outbound

This paper cites Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding.

Vision Generalist Model: A Survey Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:42.214314Z

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Observation 9539f517-a2bb-476d-8466-6d9f13caac59 · outbound

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

Vision Generalist Model: A Survey LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:42.303030Z

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Observation 4c99319a-658e-431f-a42e-278aeaaaed50 · outbound

This paper cites Mtl-nas: Task-agnostic neural architecture search towards general-purpose multi-task learning.

Vision Generalist Model: A Survey Mtl-nas: Task-agnostic neural architecture search towards general-purpose multi-task learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:42.405980Z

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Observation afa323e0-db9f-45fe-819a-ef9ed949a51a · outbound

This paper cites Instructdiffusion: A generalist modeling interface for vision tasks.

Vision Generalist Model: A Survey Instructdiffusion: A generalist modeling interface for vision tasks

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:42.538463Z

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Observation a4ba17bf-996a-45b2-97b6-2119824461a4 · outbound

This paper cites Scaling open-vocabulary image segmentation with image-level labels.

Vision Generalist Model: A Survey Scaling open-vocabulary image segmentation with image-level labels

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:42.648552Z

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Observation 59e09b5c-2843-4fe1-8a00-3791ea115380 · outbound

This paper cites Omnivore: A single model for many visual modalities.

Vision Generalist Model: A Survey Omnivore: A single model for many visual modalities

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:42.724865Z

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Observation 64ad3603-6dd5-4182-8e21-9d7114ca8a36 · outbound

This paper cites Mixture of experts models.

Vision Generalist Model: A Survey Mixture of experts models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:42.783410Z

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Observation 108fbebd-7adc-449c-843f-58344ee2372e · outbound

This paper cites 3d semantic segmentation with submanifold sparse convolutional networks.

Vision Generalist Model: A Survey 3d semantic segmentation with submanifold sparse convolutional networks

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:42.847069Z

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Observation 6c8fcf7c-9e11-41e3-988f-e7aae0c1e61c · outbound

This paper cites Open-vocabulary Object Detection via Vision and Language Knowledge Distillation.

Vision Generalist Model: A Survey Open-vocabulary Object Detection via Vision and Language Knowledge Distillation

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:42.929374Z

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Observation 524f39e1-6351-4720-8dde-b722bf31fbc3 · outbound

This paper cites Deepfake video detection using recurrent neural networks.

Vision Generalist Model: A Survey Deepfake video detection using recurrent neural networks

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:43.022561Z

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Observation dfc7ad55-1a4b-46bd-85ba-0aff07b70425 · outbound

This paper cites Dynamic task prioritization for multitask learning.

Vision Generalist Model: A Survey Dynamic task prioritization for multitask learning

Reference 65

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Observation 94e049cb-e175-4c4b-84bd-d7ed481aa2a6 · outbound

This paper cites GRIT: General Robust Image Task Benchmark.

Vision Generalist Model: A Survey GRIT: General Robust Image Task Benchmark

Reference 66

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Observation 5909fec8-b3bb-47b2-80de-f956ce27527a · outbound

This paper cites Onellm: One framework to align all modalities with language.

Vision Generalist Model: A Survey Onellm: One framework to align all modalities with language

Reference 67

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Observation e867694b-33ca-4192-8dcf-810361659346 · outbound

This paper cites Deep residual learning for image recognition.

Vision Generalist Model: A Survey Deep residual learning for image recognition

Reference 68

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Observation bbeb827e-2db2-4ef1-863e-5886a6606636 · outbound

This paper cites Deep residual learning for image recognition.

Vision Generalist Model: A Survey Deep residual learning for image recognition

Reference 69

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Observation 7f6f8551-b070-484f-b446-4d752e3b9172 · outbound

This paper cites Mask r-cnn.

Vision Generalist Model: A Survey Mask r-cnn

Reference 70

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Observation 6184590f-6af9-4971-a072-c13aab04b41f · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Vision Generalist Model: A Survey Distilling the Knowledge in a Neural Network

Reference 71

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Observation 832914b1-78db-4b26-9364-e372c3f3f8a0 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Vision Generalist Model: A Survey Classifier-Free Diffusion Guidance

Reference 72

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Observation 1e3ed28a-94cc-4a54-be49-322a99ba2435 · outbound

This paper cites Denoising diffusion probabilistic models.

Vision Generalist Model: A Survey Denoising diffusion probabilistic models

Reference 73

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Observation 3c883db3-43b4-4db6-9b60-9ea54adb2b37 · outbound

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Vision Generalist Model: A Survey Long short-term memory

Reference 74

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Observation 14bc7247-bcb2-4667-809a-57246b0a91cf · outbound

This paper cites Unit: Multimodal multitask learning with a unified transformer.

Vision Generalist Model: A Survey Unit: Multimodal multitask learning with a unified transformer

Reference 75

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Observation f25d31fa-61a2-4121-a7cd-e61f9fc7b8b5 · outbound

This paper cites An Embodied Generalist Agent in 3D World.

Vision Generalist Model: A Survey An Embodied Generalist Agent in 3D World

Reference 76

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Observation 109d6306-2545-47c5-899b-c9395d515211 · outbound

This paper cites Language Is Not All You Need: Aligning Perception with Language Models.

Vision Generalist Model: A Survey Language Is Not All You Need: Aligning Perception with Language Models

Reference 77

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Observation d1552308-4ba0-4b04-b8f0-3a44805f168c · outbound

This paper cites Perceiver IO: A General Architecture for Structured Inputs & Outputs.

Vision Generalist Model: A Survey Perceiver IO: A General Architecture for Structured Inputs & Outputs

Reference 78

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Observation ff85f8d2-94f1-4363-9875-2957dcc4ffa8 · outbound

This paper cites Perceiver: General perception with iterative attention.

Vision Generalist Model: A Survey Perceiver: General perception with iterative attention

Reference 79

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Observation 06cfafc9-e956-4a90-a212-cbacda38b73a · outbound

This paper cites Oneformer: One transformer to rule universal image segmentation.

Vision Generalist Model: A Survey Oneformer: One transformer to rule universal image segmentation

Reference 80

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Observation 283d2273-548b-42f1-8710-1b6284021692 · outbound

This paper cites VIMA: General Robot Manipulation with Multimodal Prompts.

Vision Generalist Model: A Survey VIMA: General Robot Manipulation with Multimodal Prompts

Reference 81

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Observation f4c42f47-a091-49cf-9006-eedc72a1839c · outbound

This paper cites Unified language-vision pretraining with dynamic discrete visual tokenization.

Vision Generalist Model: A Survey Unified language-vision pretraining with dynamic discrete visual tokenization

Reference 82

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Observation 19153742-5e8b-45c7-8dcf-9fa41894dff8 · outbound

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

Vision Generalist Model: A Survey Deep visual-semantic alignments for generating image descriptions

Reference 83

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Observation f62d91b2-1762-4e9d-94ad-f0c8621573a0 · outbound

This paper cites Multi-task learning using uncertainty to weigh losses for scene geometry and semantics.

Vision Generalist Model: A Survey Multi-task learning using uncertainty to weigh losses for scene geometry and semantics

Reference 84

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Observation e66a144c-082c-4c5c-abfb-8a2ef80199ae · outbound

This paper cites Segment anything.

Vision Generalist Model: A Survey Segment anything

Reference 85

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Observation 56e22b71-6341-4b29-b253-d599cf106d49 · outbound

This paper cites Segment Anything.

Vision Generalist Model: A Survey Segment Anything

Reference 86

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Observation 3e65f32f-fda9-4c12-9da9-ba44a80e60e4 · outbound

This paper cites Uvim: A unified modeling approach for vision with learned guiding codes.

Vision Generalist Model: A Survey Uvim: A unified modeling approach for vision with learned guiding codes

Reference 87

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Observation 05aee257-12ce-42cb-a0d3-d062bb16a7c9 · outbound

This paper cites Tabddpm: Modelling tabular data with diffusion models.

Vision Generalist Model: A Survey Tabddpm: Modelling tabular data with diffusion models

Reference 88

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Observation 722487cb-9dd2-4d78-97f4-8292a40b8eb1 · outbound

This paper cites Visual genome: Connecting language and vision using crowdsourced dense image annotations.

Vision Generalist Model: A Survey Visual genome: Connecting language and vision using crowdsourced dense image annotations

Reference 89

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Observation fcf16247-114b-4183-ac79-25779cbfa097 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Vision Generalist Model: A Survey Imagenet classification with deep convolutional neural networks

Reference 90

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Observation 60f0e233-0d61-4ae9-846c-abe12bd7ed1f · outbound

This paper cites Babytalk: Understanding and generating simple image descriptions.

Vision Generalist Model: A Survey Babytalk: Understanding and generating simple image descriptions

Reference 91

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Observation 12beba6b-9608-4375-aee5-8cd89e2565f0 · outbound

This paper cites Base layers: Simplifying training of large, sparse models.

Vision Generalist Model: A Survey Base layers: Simplifying training of large, sparse models

Reference 92

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Observation d7a90b71-5da5-46df-af46-7e3b8af095e2 · outbound

This paper cites SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension.

Vision Generalist Model: A Survey SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Reference 93

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Observation 594b128c-0f69-44c4-a94c-102c4f6df394 · outbound

This paper cites Multimodal Foundation Models: From Specialists to General-Purpose Assistants.

Vision Generalist Model: A Survey Multimodal Foundation Models: From Specialists to General-Purpose Assistants

Reference 94

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Observation 12500a93-70ca-4ca8-80c0-8f904f0bac2c · outbound

This paper cites Mask dino: Towards a unified transformer-based framework for object detection and segmentation.

Vision Generalist Model: A Survey Mask dino: Towards a unified transformer-based framework for object detection and segmentation

Reference 95

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Observation 1a796a68-0850-4bff-9da1-2123fcf53024 · outbound

This paper cites Uni-perceiver v2: A generalist model for large-scale vision and vision-language tasks.

Vision Generalist Model: A Survey Uni-perceiver v2: A generalist model for large-scale vision and vision-language tasks

Reference 96

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Observation 42360ffd-43e2-4579-a9fe-22f6808caddc · outbound

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

Vision Generalist Model: A Survey Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 97

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Observation 37db749b-7cc5-4cda-a039-5ffb7e93afd4 · outbound

This paper cites Lavender: Unifying video-language understanding as masked language modeling.

Vision Generalist Model: A Survey Lavender: Unifying video-language understanding as masked language modeling

Reference 98

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Observation 1327ed3c-d85d-480b-ac66-2fc4e89e18cf · outbound

This paper cites VisualBERT: A Simple and Performant Baseline for Vision and Language.

Vision Generalist Model: A Survey VisualBERT: A Simple and Performant Baseline for Vision and Language

Reference 99

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Source-reported events for the cited work

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Observation b42aa6ee-2894-45bf-99dd-8907276e4d7f · outbound

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Vision Generalist Model: A Survey Grounded language-image pre-training

Reference 100

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Observation 779fed7b-b38a-4d86-8a5e-eb7c7f3a422e · outbound

This paper cites Omg-seg: Is one model good enough for all segmentation? In CVPR, 2024.

Vision Generalist Model: A Survey Omg-seg: Is one model good enough for all segmentation? In CVPR, 2024

Reference 101

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