THIRUNELLI MAHAVISHNU MANDIR, WAYANAD (KER)

Only mandir where devotees can perform all rituals related to one’s life, from birth to death and life after death.

It is said that the pratishtha of Vishnu was done by Brahma himself.
Also called Sahyamala Kshetram n Kashi of South

Brahma was travelling round the Universe on his hamsa, when he became attracted by the beauty of the area now known as Brahmagiri Hill. He noticed a murti in an Amla tree and installed it here. At Brahma’s request Vishnu promised that the water here would wash away all sins.
Even today the mukhya pujari leaves a portion of the puja materials in the belief that Brahma Himself will come and perform pooja in the early hours of morning.
Parashurama performed the last rites of his father, Jamdagni, here.
@SriramKannan77
On the western side is the cave mandir Gunnika, dedicated to Shiva. It is said that a fruit (Nelli fruit) plucked by a bhakt was turned into a shivlinga as he was finishing bath in the Papanashini stream.
Thus Thirunelli is blessed by the unique presence of Trideva.
@vedvyazz
The steps at the back of the mandir lead to the tank Panchteertham. It is believed that Panchteertham, at one point of time, was a meeting point of 5 rivers. The tank has the footsteps of Vishnu, called Vishnupad on a boulder in the middle.
@SriRamya21

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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)

By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.

Why is the ability to shape this balance important? (5/12)

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