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Notably, the mllms are constrained by their limited context lengths and the substantial costs while processing long videos We considered four model baselines in our assessment Although several existing methods attempt to reduce visual tokens, their strategies encounter severe bottleneck, restricting mllms' ability to perceive fine.
Lilian Durán G (@lilian_durang) • Instagram photos and videos
The field of long video understanding is rapidly evolving Release the inference code w 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
It makes better and faster long video.
Video understanding has become an important area in artificial. 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) 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。 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. 🌐 blog | 📃 paper | 🤗 hugging face | 🎥 demo (left) the performance and max frames of different models (i) comprehensive long video understanding (ii) efficient long visual context processing.
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Release the inference code w/o
