VALSE2019总结(4)-主题报告

4. 主题报告

4.1 无人驾驶的环境感知与理解 (jian yang, NJUST)

  1. outline

    • 无人驾驶发展简介
      • 遥控驾驶,自主驾驶,南理工无人车,
    • 行车环境视觉感知与理解 (具体介绍贴图片)
      • 阴影检测与去除
      • 车道线检测
      • 行人检测与姿态估计
      • 场景分割与深度估计
  2. 具体,如图

  3. https://blog.csdn.net/qq_15698613/article/details/89303060

4.2:Learning to track and segment objects in videos

  • 很迷的一个报告,没啥干货

4.3 AI破晓——机遇与挑战 (陶大程)

  • 没听

4.4 深度学习处理器 (陈云ji)

  • 没意思,贴图待定

4.5 基于知识驱动的行为理解

  1. outline

    • knowledge engine - a possible direction: HAKE
    • pose - open the door of activity understanding: Alphapose, Crowdpose
    • sequence modeling: Deep RNN: semi-couple prociple
    • summary
  2. why activity understanding is difficult ?

    • huge semantic Noise (compare to object recognition)
    • Long-tail distribution, few-shot problem (DL fails)
    • 结论:pose is not enough, we need konwledge pose
  3. Human activity konwledge engine (HAKE)

    • to see/parse/understand the activity
    • knowledge engine construction: 见图片
    • reasoning via part states(HAKE): 见图片
    • human-object interaction
      • 见图片,几个 HOI Dataset 有:AVA, ActivityNet, Kinetics
    • conclusion:
      • activity data is semantically noisy
      • knowledge at body part can help to denote
      • HAKE:
      • HAKE based Two-stage paradigm,见图片
  4. pose - open the door of activity understanding: Alphapose, Crowdpose

    • 没记录
  5. sequence modeling: Deep RNN: semi-couple prociple

    • 没记录
  6. summary

    • 没记录,等他主页公布PPT吧
  7. 部分图片,

4.6 人工智能与未来出行

  • 没学术性,贴图待定

4.7 计算机视觉的下一步:迈向大AI (罗杰波)

  • 没注意,贴图待定

4.8 梯度之谜 (孟德宇)

  1. issue
    • limitations of model-driven methodology
      • generally with nonconvex model
      • only fit one unsupervised image
      • slow prediction speed
    • limitations of data-driven methodology
      • require supervised-data
      • black box issue: interpretability
      • network parameters/structure are hard/easy to be designed
  2. 从梯度角度思考,解决上述问题
  3. 贴图片,有一些论文

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转载自www.cnblogs.com/LS1314/p/10885105.html
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