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afflictive    
a. 给人痛苦的,苦恼的,难受的

给人痛苦的,苦恼的,难受的

afflictive
adj 1: causing misery or pain or distress; "it was a sore trial
to him"; "the painful process of growing up" [synonym:
{afflictive}, {painful}, {sore}]

Afflictive \Af*flic"tive\, a. [Cf. F. afflictif.]
Giving pain; causing continued or repeated pain or grief;
distressing. "Jove's afflictive hand." --Pope.
[1913 Webster]

Spreads slow disease, and darts afflictive pain.
--Prior.
[1913 Webster]


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  • CVPR2025 | 苹果提出高效视觉编码器FastVLM - 知乎
    基于对图像分辨率、视觉延迟、标记数量和LLM大小之间相互作用的全面效率分析，我们介绍了FastVLM——一个模型，实现了延迟、模型大小和准确性之间的优化折衷。 FastVLM整合了FastViTHD，这是一种新型混合视觉编码器，旨在输出更少的标记并显著减少高分辨率图像的编码时间。 与以往方法不同，FastVLM仅通过缩放输入图像，实现了视觉标记数量和图像分辨率之间的最佳平衡，无需额外的标记修剪，简化了模型设计。 在LLaVA-1 5设置中，与之前的工作相比，FastVLM在保持类似的VLM基准表现的同时，实现了时间至首个标记（TTFT）的3 2倍改进。
  • EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge . . .
    We apply our framework to train EfficientVLM, a fast and accurate vision-language model consisting of 6 vision layers, 3 text layers, and 3 cross-modal fusion layers, accounting for only 93 million parameters in total, which is 44 3% of the teacher model
  • FastVLM: Efficient Vision Encoding for Vision Language Models
    This is the official repository of FastVLM: Efficient Vision Encoding for Vision Language Models (CVPR 2025) We introduce FastViTHD, a novel hybrid vision encoder designed to output fewer tokens and significantly reduce encoding time for high-resolution images
  • FastVLM: Efficient Vision Encoding for Vision Language Models
    We present FastViTHD, a new hybrid vision encoder designed for efficient high-resolution VLM We provide comprehensive ablations to demonstrate the op-timality of FastViTHD over FastViT and prior works for different LLMs and input resolutions
  • arXiv2022. 10 | EfficientVLM: 基于Knowledge Distillation and . . .
    研究提出了EfficientVLM，它是大型预训练模型X-VLM的压缩版，旨在解决大模型在实际应用中的空间、内存和延迟挑战。 通过知识蒸馏和特定模态修剪，EfficientVLM在保持高性能的同时，减少了参数数量和计算需求。 实验显示，即使在高稀疏度下，模型仍能
  • EfcientVLM: Fast and Accurate Vision-Language Models via Knowledge . . .
    EfcientVLM shrinks the size of X-VLM by half, thus consisting of 6 vi- sion layers, 3 text layers, and 3 cross-modal layers, accounting for only 92 million parameters in total, which is 43 6% of the X-VLM model
  • [ICLR 2026] 视觉大模型只需要4个Vision Token | MMTok: 免 . . .
    TL;DR: 我们提出了 MMTok，一个免训练的 vision token 选择算法，把 token 剪枝问题形式化为 最大覆盖问题。 核心思想：同时覆盖文本相关信息（text-vision coverage）和图像整体信息（vision-vision coverage）。 简单高效：只需相似度计算 + 贪心选择，无需训练。 效果：使用 LLaVA-NeXT-13B (最多2880个vision token)作为基础模型，在POPE数据上, 用最多 160 个 vision token（5 5%） 保留 95%+ 的性能，推理加速近 2 倍；极限压缩到 4 个 token，还能保留 87 7% 的性能。
  • FastVLM: Efficient Vision Encoding for Vision Language Models
    Based on a comprehensive efficiency analysis of the interplay between image resolution, vision latency, token count, and LLM size, we introduce FastVLM, a model that achieves an optimized trade-off between latency, model size and accuracy
  • GitHub - swaggy-TN EfficientVLM: EfficientVLM: Fast and Accurate Vision . . .
    Our code and scripts are based on X-VLM and CoFiPruning We thank the authors for opening source their code
  • EfficientVLM: Fast and Accurate Vision-Language Models . . .
    We apply our framework to train EfficientVLM, a fast and accurate vision-language model consisting of 6 vision layers, 3 text layers, and 3 cross-modal fusion layers, accounting for only 93 million parameters in total, which is 44 3% of the teacher model





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