You intuitively know that poor quality images lead to poor object detection and vice-versa.

Image-Adaptive (IA)-YOLO improves object detection in adverse weather conditions using a hybrid task: image improvement combined with object detection.

• The paper, Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions, was published 3 days ago:

• The authors present an end-to-end hybrid data training task

• Each input image is adaptively enhanced to obtain better detection performance
• The hybrid architecture has a CNN-Parameter Predictor (PP) network that learns some appropriate Differentiable Image Processing (DIP) parameters to adaptivily enhance images for object detection, in a weakly supervised manner
• The DIP module consists of six differentiable filters with adjustable hyperparameters: Defog, White Balance(WB), Gamma, Contrast, Tone, and Sharpen.

• The CNN-PP network is trained using low-resolution images (256x256px) to speed the training
• CNN-PP parameters are used by the DIP module to improve the quality of the high-resolution images

• DIP processed (enhanced) images feed a YOLOv3 model for the object detection task
• IA-YOLO improves the baseline YOLO I by 0.89, 13.48 and 3.95 percent on VOC_norm_test, VOC_Dark_test and ExDark_test, respectively
📰 Paper: Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions
• abs: https://t.co/ytKkcswB9b
• pdf: https://t.co/gPFexKL22Z
• Repo: https://t.co/sVF4Xz6ddz

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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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The first ever world map was sketched thousands of years ago by Indian saint
“Ramanujacharya” who simply translated the following verse from Mahabharat and gave the world its real face

In Mahabharat,it is described how 'Maharishi Ved Vyasa' gave away his divine vision to Sanjay


Dhritarashtra's charioteer so that he could describe him the events of the upcoming war.

But, even before questions of war could begin, Dhritarashtra asked him to describe how the world looks like from space.

This is how he described the face of the world:

सुदर्शनं प्रवक्ष्यामि द्वीपं तु कुरुनन्दन। परिमण्डलो महाराज द्वीपोऽसौ चक्रसंस्थितः॥
यथा हि पुरुषः पश्येदादर्शे मुखमात्मनः। एवं सुदर्शनद्वीपो दृश्यते चन्द्रमण्डले॥ द्विरंशे पिप्पलस्तत्र द्विरंशे च शशो महान्।

—वेद व्यास, भीष्म पर्व, महाभारत


Meaning:-

हे कुरुनन्दन ! सुदर्शन नामक यह द्वीप चक्र की भाँति गोलाकार स्थित है, जैसे पुरुष दर्पण में अपना मुख देखता है, उसी प्रकार यह द्वीप चन्द्रमण्डल में दिखायी देता है। इसके दो अंशो मे पीपल और दो अंशो मे विशाल शश (खरगोश) दिखायी देता है।


Meaning: "Just like a man sees his face in the mirror, so does the Earth appears in the Universe. In the first part you see leaves of the Peepal Tree, and in the next part you see a Rabbit."

Based on this shloka, Saint Ramanujacharya sketched out the map, but the world laughed