seminar share

1. Dense-Captioning Events
in Videos

2. Dense-Captioning

3. Highlight
• Task: dense-captioning events
• Dataset: ActivityNet Captions
• Events range across multiple time scales and can even overlap.
• generating action proposals to multi-scale detection of events,
processes each video in a forward pass to detect events as they occur
• Events in a given video are usually related to one another.
• introduce a captioning module that utilizes the context from all the
events from our proposal module to generate each sentence

4. DenseCap:
Fully Convolutional Localization Networks for Dense Captioning

5. DenseCap:
Fully Convolutional Localization Networks for Dense Captioning

6. Method V. Escorcia, F. C. Heilbron, J. C. Niebles, and B. Ghanem.
Daps: Deep action proposals for action understanding.
2016,ECCV
J. Johnson, A.
Karpathy, and L.
Fei-Fei.
DenseCap:
Fully
convolutional
localization
networks for
dense
captioning.
A. Alahi, K. Goel, V.
Ramanathan, A.
Robicquet, L. Fei-
Fei,
and S. Savarese.
Social lstm: Human
trajectory prediction
in
crowded spaces.
object-centric
in images
action-centric
in videos

7. Performance

8. Discussion Jointly Localizing and Describing Events for Dense Video Captioning

9. Discussion Joint Event Detection and Description in Continuous Video Streams