B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens | Paper Citation Record

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This paper cites Accessed: 2024-09-30.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Accessed: 2024-09-30

Reference 1

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Observation 5cce60a5-a4a9-4ed6-90ef-b513cafce434 · outbound

This paper cites Flamingo: A Visual Language Model For Few-shot Learning.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Flamingo: A Visual Language Model For Few-shot Learning

Reference 2

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Observation 5b1a2745-d8f5-40b8-8072-e37ceeccd44d · outbound

This paper cites Deepspeed-Inference: Enabling Efficient Inference of Trans- former Models at Unprecedented Scale.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Deepspeed-Inference: Enabling Efficient Inference of Trans- former Models at Unprecedented Scale

Reference 3

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This paper cites Qwen Technical Report.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Qwen Technical Report

Reference 4

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

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Frozen in time: A joint video and image encoder for end-to-end retrieval

Reference 5

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This paper cites Token Merging for Fast Stable Diffusion.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Token Merging for Fast Stable Diffusion

Reference 6

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Observation 165db5d1-38ec-480f-bb5e-65fb6fdd29d3 · outbound

This paper cites Token Merging: Your ViT But Faster.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Token Merging: Your ViT But Faster

Reference 7

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Observation fb4e4d0b-64f3-44c5-bbd6-066c0d878c26 · outbound

This paper cites Dif- fusiondet: Diffusion model for object detection.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Dif- fusiondet: Diffusion model for object detection

Reference 8

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Observation 97cfd238-8e33-4990-bfe2-38f7dded1efd · outbound

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

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

Reference 9

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

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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This paper cites The Llama 3 Herd of Models.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens The Llama 3 Herd of Models

Reference 11

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This paper cites ActivityNet: A Large-Scale Video Benchmark for Human Activity Understanding.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens ActivityNet: A Large-Scale Video Benchmark for Human Activity Understanding

Reference 12

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Observation 6c8bf51d-4b97-4f92-8a27-f03d3374c844 · outbound

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

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 13

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This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models, 2024.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models, 2024

Reference 14

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

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Reference 15

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This paper cites Making the V in VQA Matter: Ele- vating the Eole of Image Understanding in Visual Question Answering.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Making the V in VQA Matter: Ele- vating the Eole of Image Understanding in Visual Question Answering

Reference 16

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Observation f1228d8d-5a4c-4164-9e72-a3f607853442 · outbound

This paper cites Vizwiz Grand Challenge: Answering Visual Questions from Blind People.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Vizwiz Grand Challenge: Answering Visual Questions from Blind People

Reference 17

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

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens LoRA: Low-Rank Adaptation of Large Language Models

Reference 18

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This paper cites Vision-based freezing of gait detection with anatomic patch based representation.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Vision-based freezing of gait detection with anatomic patch based representation

Reference 19

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This paper cites GQA: A New Dataset for Real-World Visual Eeasoning and Compositional Question Answering.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens GQA: A New Dataset for Real-World Visual Eeasoning and Compositional Question Answering

Reference 20

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This paper cites LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models

Reference 21

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This paper cites Chat-univi: Unified Visual Representation Em- powers Large Language Models with Image and Video Un- derstanding.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Chat-univi: Unified Visual Representation Em- powers Large Language Models with Image and Video Un- derstanding

Reference 22

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This paper cites ReferItGame: Referring to objects in pho- tographs of natural scenes.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens ReferItGame: Referring to objects in pho- tographs of natural scenes

Reference 23

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This paper cites Shamma, Michael S.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Shamma, Michael S

Reference 24

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

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Reference 25

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This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 26

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This paper cites MVBench: A Comprehensive Multi-Modal Video Under- standing Benchmark.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens MVBench: A Comprehensive Multi-Modal Video Under- standing Benchmark

Reference 27

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This paper cites VidToMe: Video Token Merging for Zero-Shot Video Edit- ing.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens VidToMe: Video Token Merging for Zero-Shot Video Edit- ing

Reference 28

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This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Evaluating Object Hallucination in Large Vision-Language Models

Reference 29

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This paper cites LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models

Reference 30

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B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

Reference 31

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B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens VILA: On Pre-training for Vi- sual Language Models

Reference 32

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B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Visual Instruction Tuning

Reference 33

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This paper cites MMBench: Is Your Multi-Modal Model an All-Around Player? In ECCV, pages 216–233.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens MMBench: Is Your Multi-Modal Model an All-Around Player? In ECCV, pages 216–233

Reference 34

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B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Decoupled Weight Decay Regularization

Reference 35

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B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

Reference 36

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This paper cites Autoregressive omni-aware outpainting for open- vocabulary 360-degree image generation.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Autoregressive omni-aware outpainting for open- vocabulary 360-degree image generation

Reference 37

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B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Valley: Video Assistant with Large Language model Enhanced abilitY

Reference 38

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B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Mod- els

Reference 39

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This paper cites Egoschema: A Diagnostic Benchmark for Bery Long-Form Video Language Understanding.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Egoschema: A Diagnostic Benchmark for Bery Long-Form Video Language Understanding

Reference 40

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This paper cites Generation and comprehension of unambiguous object descriptions, 2016.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Generation and comprehension of unambiguous object descriptions, 2016

Reference 41

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This paper cites Ocr-vqa: Visual question answering by reading text in images.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Ocr-vqa: Visual question answering by reading text in images

Reference 42

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B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Perception Test: A Diagnostic Benchmark for Multimodal Video Models

Reference 43

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B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Learning Transferable Visual Models from Natural Language Supervi- sion

Reference 44

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This paper cites A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge, 2022.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge, 2022

Reference 45

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This paper cites Llava-prumerge: Adaptive Token Reduction for Efficient Large Multimodal Models.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Llava-prumerge: Adaptive Token Reduction for Efficient Large Multimodal Models

Reference 46

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This paper cites Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning

Reference 47

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This paper cites TextCaps: a Dataset for Image Captioning with Reading Comprehension, 2020.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens TextCaps: a Dataset for Image Captioning with Reading Comprehension, 2020

Reference 48

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This paper cites Towards VQA Models That Can Read.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Towards VQA Models That Can Read

Reference 49

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This paper cites Moviechat: From Dense To- ken to Sparse Memory for Long Video Understanding.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Moviechat: From Dense To- ken to Sparse Memory for Long Video Understanding

Reference 50

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

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 51

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B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens [CLS] Token Tells Everything Needed for Training-free Efficient MLLMs

Reference 52

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This paper cites VisionLLM: Large Language Model is Also An Open-ended Decoder for Vision-Centric Tasks.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens VisionLLM: Large Language Model is Also An Open-ended Decoder for Vision-Centric Tasks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:38:23.975446Z

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This paper cites LongVLM: Efficient Long Video Under- standing Via Large Language Models.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens LongVLM: Efficient Long Video Under- standing Via Large Language Models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:38:23.959253Z

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Observation e7dd3e38-dc97-4301-ab75-fa784b31980b · outbound

This paper cites Video Question Answer- ing via Gradually Refined Attention over Appearance and Motion.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Video Question Answer- ing via Gradually Refined Attention over Appearance and Motion

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:38:23.928832Z

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This paper cites Qwen2 Technical Report.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Qwen2 Technical Report

Reference 56

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This paper cites SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation

Reference 57

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This paper cites Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

Reference 58

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Observation de2b71cb-7949-42ce-9489-ce89004ba5e7 · outbound

This paper cites LLaV A-NeXT: A Strong Zero-shot Video Understanding Model, 2024.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens LLaV A-NeXT: A Strong Zero-shot Video Understanding Model, 2024

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:38:23.914436Z

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Observation 2e7fbe65-0514-494c-b5a9-b5f9956e60c7 · outbound

This paper cites Needle In A Video Haystack: A Scalable Syn- thetic Framework for Benchmarking Video MLLMs.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Needle In A Video Haystack: A Scalable Syn- thetic Framework for Benchmarking Video MLLMs

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:38:23.897803Z

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Observation 1d57da18-8074-49fb-bc71-4fbfdcd2012d · outbound

This paper cites Clip in medical imaging: A survey.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Clip in medical imaging: A survey

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:38:23.880456Z

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Observation 4e9f2669-99f0-4716-809f-fdfd699558a7 · outbound

This paper cites LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment

Reference 62

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This paper cites A closer look at the cls token for cross-domain few-shot learning.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens A closer look at the cls token for cross-domain few-shot learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:38:23.864704Z

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Observation 1a170a45-5858-417e-ad21-d9e763a9e8ce · outbound

This paper cites Training Details We adopt a two-stage training strategy [9, 30, 33], dividing training into pretraining for modality alignment and fine- tuning for instruction tuning.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Training Details We adopt a two-stage training strategy [9, 30, 33], dividing training into pretraining for modality alignment and fine- tuning for instruction tuning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:38:23.849232Z

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Observation be9a685a-eca2-48a4-a5dc-bca19ff2bb72 · outbound

This paper cites Additional Discussion on Different Frame Se- lection Features.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Additional Discussion on Different Frame Se- lection Features

Reference 66

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Observation f95bd0b0-8aba-4b16-b736-e932672b0924 · outbound

This paper cites Game Science.

B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Game Science

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:38:23.817966Z

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Observation 3039831d-a05b-48b2-935a-8e8423126b96 · outbound

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B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens Unresolved cited work

Reference 470

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Outbound references

Pith citing papers