Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation | Paper Citation Record

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

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Flamingo: a visual language model for few-shot learning

Reference 1

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Observation a66d8743-ee4c-4cf2-a342-e0680679f100 · outbound

This paper cites Frozen in time: A joint video and image encoder for end-to- end retrieval.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Frozen in time: A joint video and image encoder for end-to- end retrieval

Reference 2

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Observation c3653f49-ce7b-4db9-bca3-17cfb9b98bfe · outbound

This paper cites Label-Efficient Semantic Segmentation with Diffusion Models.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Label-Efficient Semantic Segmentation with Diffusion Models

Reference 3

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This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 4

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This paper cites Align your latents: High-resolution video synthesis with la- tent diffusion models.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Align your latents: High-resolution video synthesis with la- tent diffusion models

Reference 5

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Language models are few-shot learners

Reference 6

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Observation 86c3547e-c80a-4fff-86e7-bd5e635aa5ee · outbound

This paper cites Collecting highly parallel data for paraphrase evaluation.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Collecting highly parallel data for paraphrase evaluation

Reference 7

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Observation 2b16fd6b-064d-41c6-964e-870eaf58ba7a · outbound

This paper cites VideoCrafter1: Open Diffusion Models for High-Quality Video Generation.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

Reference 8

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Observation c0ce776a-30ba-417f-b86f-382fbed87148 · outbound

This paper cites Panda-70m: Captioning 70m videos with multiple cross- modality teachers.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Panda-70m: Captioning 70m videos with multiple cross- modality teachers

Reference 9

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Observation 1b448b3a-e129-4e2f-ae98-5b15608bdc83 · outbound

This paper cites VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

Reference 10

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Observation 1e5ce52d-6fdd-4227-813f-db3e6673e0e7 · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation PaLM: Scaling Language Modeling with Pathways

Reference 11

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Observation ed797b34-139a-4a90-b8a4-5d1692e981bc · outbound

This paper cites DreamLLM: Synergistic Multimodal Comprehension and Creation.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation DreamLLM: Synergistic Multimodal Comprehension and Creation

Reference 12

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Observation 13653f45-ea1f-44b6-84ae-2caaae821200 · outbound

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

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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Observation 6688d341-cf16-46d8-93cb-9d55b7306015 · outbound

This paper cites Preserve your own correlation: A noise prior for video diffusion models.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Preserve your own correlation: A noise prior for video diffusion models

Reference 14

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Observation 61e1d6ab-c3e5-4ca6-b4f9-94f8caa32e1e · outbound

This paper cites Planting a SEED of Vision in Large Language Model.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Planting a SEED of Vision in Large Language Model

Reference 15

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Making LLaMA SEE and Draw with SEED Tokenizer

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation

Reference 17

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This paper cites The” something something” video database for learning and evaluating visual common sense.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation The” something something” video database for learning and evaluating visual common sense

Reference 18

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This paper cites Ego4d: Around the world in 3,000 hours of egocentric video.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Ego4d: Around the world in 3,000 hours of egocentric video

Reference 19

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Observation 0a968b18-3a95-4fea-81de-f8fef9e5b59f · outbound

This paper cites Denoising diffu- sion probabilistic models.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Denoising diffu- sion probabilistic models

Reference 20

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This paper cites CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 21

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

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation LoRA: Low-Rank Adaptation of Large Language Models

Reference 22

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Observation 5d5f184f-f8a5-4363-897c-e257a19ae903 · outbound

This paper cites Soda: Bottle- 9 neck diffusion models for representation learning.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Soda: Bottle- 9 neck diffusion models for representation learning

Reference 23

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Mistral 7B

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Unified language-vision pretraining with dynamic discrete visual tokenization

Reference 25

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Observation 75d80948-3a44-42e7-9357-0bdca8bf4644 · outbound

This paper cites Video-lavit: Unified video-language pre-training with decoupled visual-motional tokenization.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Video-lavit: Unified video-language pre-training with decoupled visual-motional tokenization

Reference 26

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This paper cites The Kinetics Human Action Video Dataset.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation The Kinetics Human Action Video Dataset

Reference 27

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Auto-Encoding Variational Bayes

Reference 28

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation VideoPoet: A Large Language Model for Zero-Shot Video Generation

Reference 29

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation LLaVA-OneVision: Easy Visual Task Transfer

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This paper cites MVBench: A Comprehensive Multi-modal Video Understanding Benchmark.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation MVBench: A Comprehensive Multi-modal Video Understanding Benchmark

Reference 31

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Mvbench: A comprehensive multi-modal video understand- ing benchmark

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Autoregressive Image Generation without Vector Quantization

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Tgif: A new dataset and benchmark on animated gif description

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

Reference 37

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This paper cites Llava-next: Improved reason- ing, ocr, and world knowledge, 2024.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Llava-next: Improved reason- ing, ocr, and world knowledge, 2024

Reference 38

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Visual instruction tuning

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation World Model on Million-Length Video And Language With Blockwise RingAttention

Reference 40

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Valley: Video Assistant with Large Language model Enhanced abilitY

Reference 42

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models

Reference 43

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Egoschema: A diagnostic benchmark for very long- form video language understanding

Reference 44

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Snap video: Scaled spatiotemporal transformers for text-to-video synthe- sis

Reference 45

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Gpt-4v(ision) system card, 2023

Reference 46

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Gpt-4o system card, 2024

Reference 47

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This paper cites Per- ception test: A diagnostic benchmark for multimodal video models.

Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Per- ception test: A diagnostic benchmark for multimodal video models

Reference 48

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Learning transferable visual models from natural language supervi- sion

Reference 49

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation High-resolution image synthesis with latent diffusion models

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Laion-5b: An open large-scale dataset for training next gen- eration image-text models

Reference 51

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning

Reference 52

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Make-A-Video: Text-to-Video Generation without Text-Video Data

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Generative Multimodal Models are In-Context Learners

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Emu: Generative Pretraining in Multimodality

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 57

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Gemini: A Family of Highly Capable Multimodal Models

Reference 58

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 59

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Reference 62

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Reference 63

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Reference 64

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Diffusion Feedback Helps CLIP See Better

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Loong: Generating Minute-level Long Videos with Autoregressive Language Models

Reference 69

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Diffusion models as masked autoencoders

Reference 70

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Reference 71

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Reference 72

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Reference 73

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Reference 75

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Reference 76

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Reference 77

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Dynamicrafter: Animating open- domain images with video diffusion priors

Reference 78

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Msr-vtt: A large video description dataset for bridging video and language

Reference 79

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Open-vocabulary panoptic segmentation with text-to-image diffusion models

Reference 80

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning

Reference 81

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation VideoGPT: Video Generation using VQ-VAE and Transformers

Reference 82

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling

Reference 83

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Reference 84

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Reference 85

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning

Reference 86

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Capsfu- sion: Rethinking image-text data at scale

Reference 87

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Activitynet-qa: A dataset for understanding complex web videos via question answering

Reference 88

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation MonoFormer: One Transformer for Both Diffusion and Autoregression

Reference 89

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Reference 90

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Reference 91

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Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation Towards auto- matic learning of procedures from web instructional videos

Reference 92

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Reference 93

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