#Thread Hero Stones

The proof of bravery of our ancestors etched on stones. A Hero Stone is essentially a memorial commemorating the fame of the warriors who had died in a battle, while defending a king/cattle/village/women /border.
1/n

Most of these stones erected between the 3rd CE BC -18th CE AD,can be found all over India, although the higher concentration is in Karnataka, which is thought to have over 2,500 examples.
2/n
An archetypal Hero Stone has sculptures in 3 panels -
Upper Panel – The upper panel depicts the subject worshiping a deity, most commonly a Shiva linga, with an attendant/priest.
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Middle Panel – Here, the hero seated in a palanquin or shrine, often being lifted to the heavens by nymphs (apsaras).
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Lower Panel – This panel usually depicts how the hero died, so battle scenes are often shown, but on occasions you do come across something a little different.
There are some with even 4,5,6 and 7 panels.
5/n
There are also hero stones depicting the bravery of women who sacrificed their lives immolating themselves in fire, called Sati stones.They depict a set of sun ,moon, an upright open arm and hand bent at the elbow with bangles intact.
6/n
It has been noted that in the construction of later Hindu temples the process of the selection of a stone for carving the image of a god, the consecration and other rites are the same as those for the erection of these memorial stones.
7/n
A massive hero stone housed in a mandir in Agrahara Bachahalli, Karnataka

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How can we use language supervision to learn better visual representations for robotics?

Introducing Voltron: Language-Driven Representation Learning for Robotics!

Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z

🧵👇(1 / 12)


Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.

Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)

The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (
https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).

The secret is *balance* (3/12)

Starting with a masked autoencoder over frames from these video clips, make a choice:

1) Condition on language and improve our ability to reconstruct the scene.

2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)

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