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Long video understanding poses a significant challenge for current Multi-modal Large Language Models (MLLMs). Notably, the MLLMs are constrained by their limited context lengths and the substantial costs while processing long videos. Although several existing methods attempt to reduce visual tokens, their strategies encounter severe bottleneck, restricting MLLMs' ability to perceive fine ...
🔥🔥First-ever hour scale video understanding models - VectorSpaceLab/Video-XL
The field of long video understanding is rapidly evolving. While numerous existing models achieve strong performance on benchmarks, their substantial memory overhead and high response latency become a critical bottleneck, especially as video input lengths grow. To overcome these limitations and maintain superior performance, we're releasing Video-XL-2. It makes better and faster long video ...
Abstract Although current Multi-modal Large Language Models (MLLMs) demonstrate promising results in video understanding, processing extremely long videos remains an ongoing challenge. Typically, MLLMs struggle with handling thousands of visual tokens that exceed the maximum context length, and they suffer from the information decay due to token aggregation. Another challenge is the high ...
🔥🔥First-ever hour scale video understanding models - Video-XL/README.md at main · VectorSpaceLab/Video-XL
Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding Yan Shu1,2* Zheng Liu2,6*† Peitian Zhang2,3 Minghao Qin2,4 Junjie Zhou2,5 Zhengyang Liang2 Tiejun Huang2,7 Bo Zhao1,2†
Despite advanced token compression techniques, existing multimodal large language models (MLLMs) still struggle with hour-long video understanding. In this work, we propose Video-XL-Pro, an efficient method for extremely long video understanding, built upon Reconstructive Compression of Tokens (ReCoT), a learnable module that leverages self-supervised learning to generate comprehensive and ...
(i) Comprehensive long video understanding. Video-XL 7B achieves the leading performance among 7B models on MLVU, VideoMME, VNBench and LongVideoBench.
from videoxl.model.builder import load_pretrained_model from videoxl.mm_utils import tokenizer_image_token, process_images,transform_input_id from videoxl.constants import IMAGE_TOKEN_INDEX,TOKEN_PERFRAME from PIL import Image from decord import VideoReader, cpu import torch import numpy as np # fix seed torch.manual_seed(0)
Long video understanding poses a significant challenge for current Multi-modal Large Language Models (MLLMs). Notably, the MLLMs are constrained by their limited context lengths and the substantial costs while processing long videos. Although several existing methods attempt to reduce visual tokens, their strategies encounter severe bottleneck, restricting MLLMs' ability to perceive fine ...
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Welcome to VideoXL Our channel is dedicated to restoring and enhancing old, low-resolution videos by combining advanced editing techniques with innovative tools, including AI. We breathe new life ...
Multi-modal Large Language Models (MLLMs) have attracted widespread attention from the AI community. By augmenting large language models (LLMs) [34, 33, 40] with vision encoders, MLLMs are enabled to perform various vision-language modeling tasks, e.g., image captioning and visual question answering [23, 55, 15]. Recently, there has been growing interest in applying MLLMs for video ...
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Most existing models are optimized for short videos, typically under one minute, due to the inherent difficulty in establishing sufficient context for longer videos. To address this challenge, several approaches employ compression strategies, such as reducing the number of visual tokens [14, 15, 16] or introducing specialized memory mechanisms [17, 18] to extend the input visual context length ...
26 мар. 2025 г.
Welcome to VideoXL Our channel is dedicated to restoring and enhancing old, low-resolution videos by combining advanced editing techniques with innovative tools, including AI. We breathe new life ...
智源研究院联合国内多所顶尖高校推出超长视频理解大模型Video-XL，显著提升AI处理小时级视频的能力，仅需一张80G显卡即可高效运行。这一突破解决了现有模型在处理10分钟以上视频时性能差、效率低的问题，标志着长视频理解进入新阶段，成为迈向通用人工智能（AGI）的关键进展。未来，AI将能更 ...
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Multi-modal large language models (MLLMs) models have made significant progress in video understanding over the past few years. However, processing long video inputs remains a major challenge due to high memory and computational costs. This makes it difficult for current models to achieve both strong performance and high efficiency in long video understanding. To address this challenge, we ...
from videoxl.model.builder import load_pretrained_model from videoxl.mm_utils import tokenizer_image_token, process_images,transform_input_id from videoxl.constants import IMAGE_TOKEN_INDEX,TOKEN_PERFRAME from PIL import Image from decord import VideoReader, cpu import torch import numpy as np # fix seed torch.manual_seed (0) model_path ...
PDF | Although current Multi-modal Large Language Models (MLLMs) demonstrate promising results in video understanding, processing extremely long videos... | Find, read and cite all the research ...
VideoXL-2评估方法chunck-bilevel怎么生成数据集的selected_info.json文件 #72 · togetaname opened 3 weeks ago
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文章浏览阅读1.5k次，点赞18次，收藏17次。Video-XL的发布为长视频理解领域带来了新的突破。_video-xl
文章浏览阅读910次，点赞27次，收藏18次。Video-XL-2开源模型突破长视频理解三大瓶颈：效果超越72B参数模型，单卡支持万帧处理，2048帧分析仅需12秒。该模型通过创新的Chunk-based Prefilling技术和四阶段渐进训练法，在影视分析、安防监控等场景展现卓越性能。目前已全面开源，支持开发者快速部署 ...
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Abstract Despite advanced token compression techniques, existing multimodal large language models (MLLMs) still struggle with hour-long video understanding. In this work, we propose Video-XL-Pro, an efficient method for extremely long video understanding, built upon Reconstructive Compression of Tokens (ReCoT), a learnable module that leverages self-supervised learning to generate ...
29 окт. 2024 г.
Welcome to VideoXL Our channel is dedicated to restoring and enhancing old, low-resolution videos by combining advanced editing techniques with innovative tools, including AI. We breathe new life ...
29 дек. 2025 г.
Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding: Paper and Code. Although current Multi-modal Large Language Models (MLLMs) demonstrate promising results in video understanding, processing extremely long videos remains an ongoing challenge. Typically, MLLMs struggle with handling thousands of tokens that exceed the maximum context length of LLMs, and they ...
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27 апр. 2025 г.
文章浏览阅读895次，点赞4次，收藏7次。Video-XL 是一款由智源研究院联合多所高校开发的长视频理解大模型，能够在单块 80G GPU 上处理 2048 帧视频，并在多个视频理解基准测试中取得领先成绩。_video-xl
from videoxl.model.builder import load_pretrained_model from videoxl.mm_utils import tokenizer_image_token, process_images,transform_input_id from videoxl.constants import IMAGE_TOKEN_INDEX,TOKEN_PERFRAME from PIL import Image from decord import VideoReader, cpu import torch import numpy as np # fix seed torch.manual_seed(0)
🔥🔥First-ever hour scale video understanding models - airhors/Video-XL-
Video-XL是北京智源人工智能研究院联合上海交大、中国人民大学、中科院、北邮和北大的研究人员共同推出的专为小时级视频理解设计的超长视觉理解模型。基于视觉上下文潜在总结技术将视觉信息压缩成紧凑的形式，提高处理效率、减少信息丢失。
These CVPR 2025 papers are the Open Access versions, provided by the Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.
Here, we demonstrate more experimental results, where LongVA and VideoXL are trained with different scaled VICO data. As shown in Ta-ble, both LongVA and VideoXL can benefit from the scal-ing up of VICO, proving that VICO strength the precise and comprehensive retrieval ability of captured information.
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🔥🔥First-ever hour scale video understanding models - Video-XL/Video-XL-2/README.md at main · VectorSpaceLab/Video-XL
Most existing models are optimized for short videos, typically under one minute, due to the inherent difficulty in establishing sufficient context for longer videos. To address this challenge, several approaches employ compression strategies, such as reducing the number of visual tokens [14, 15, 16] or introducing specialized memory mechanisms [17, 18] to extend the input visual context length ...
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