AI with Papers - Artificial Intelligence & Deep Learning – Telegram
AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates about #DeepLearning #MachineLearning #LLM & #ComputerVision

Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/

#AI #chatGPT
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🏖️ HumanNeRF: source code is out! 🏖️

👉Pausing the video at any frame and rendering the subject from arbitrary views!

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Synthesizing photorealistic humans
Synthesizing details, ie. cloth & face
Volumetric canonical T-pose
Skeletal rigid/non-rigid decomposition

More: https://bit.ly/3NEkTNY
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🎒 EG3D: source code is out! 🎒

👉#Nvidia just opened EG3D: real time multi-view faces w/ HQ #3D geometry!

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Tri-plane-based 3D GAN framework
Pose-correlated attribute (expression)
SOTA in uncond. 3D-aware synthesis
Source code & models NOW available!

More: https://bit.ly/3aOfHs0
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🔥One Millisecond Backbone. Fire!🔥

👉MobileOne by #Apple: efficient mobile backbone with inference <1 ms on #iPhone12!

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
75.9% top-1 accuracy on ImageNet
38× faster than MobileFormer net
Classification, detection & segmentation
Source code & model soon available!

More: https://bit.ly/3tsT7f2
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🧨 Scaling Transformers to GigaPixels!🧨

👉Novel ViT called Hierarchical Image Pyramid Transformer (HIPT) -> Scaling to GigaPixels!

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Gigapixel whole-slide imaging (WSI)
Leveraging natural hier. structure of WSI
Self-supervised Hi-Res representations
Source code and models available!

More: https://bit.ly/3xLuzkg
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👗BodyMap: Hyper-Detailed Humans👗

👉#META unveils 1st-ever dense continuous correspondence for clothed humans

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
1st-ever dense continuous corresp.
HQ fingers, hair, and clothes
Novel ViT-based architecture
SOTA on DensePose COCO

More: https://bit.ly/39nEPps
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🐹 NOAH just open-sourced! 🐹

👉A novel approach to find the optimal design of prompt modules through NAS algos.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
NOAH from Neural prOmpt seArcH
Parameter-efficient “prompt modules”
Efficient NAS-based implementation
Better than transfer, few-shot & domain gen.

More: https://bit.ly/3MKfVhi
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🏄🏻‍♀️Neural Super-Resolution in Movies🏄🏻‍♀️

👉Implicit neural representation to get arbitrary spatial resolution & FPS -> Super Resolution!

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Video as continuous video representation
Clips in arbitrary space/time resolution
OOD generalization in space-time
Source code and models available

More: https://bit.ly/3xsqccf
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🧠 Bias in #AI, explained simple 🧠

👉Asking DallE-Mini to help me to show what the BIAS in #AI is

𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞𝐝 𝐒𝐚𝐦𝐩𝐥𝐞𝐬:
Best eng.->men/Caucasians
Best doctors->men/Caucasians
Top CEOs->men/Caucasians
Chef, kitchen->men/Caucasians
Rich People->only Caucasians
Poor People->non-Caucasians
Italian engineers->back in 30's
Chinese eng.->infrastructures
Italian working->local market
Chinese working->vegetables
Men workers->constructions
Women workers->only office

More: https://bit.ly/3b0UFqd
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🦕 SAVi++: Segmentation by #Google 🦕

👉Novel unsupervised object-centric #AI to predict depth signals from slot-based video representation

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Segmenting complex dynamic scenes
Static/Moving objects on naturalistic BG
LiDAR-SAVi: segmenting in the wild
Source code and model soon available!

More: https://bit.ly/3n3hywd
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HaGRID : Half Million Hands👋

👉Russian Sberbank opens HaGRID, enormous dataset for HGR. "Peace" label is present 🔵🟡

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
552,992 samples, 18 classes
HD resolution in RGB format
BBox, gesture, leading hands
Dataset/models available

More: https://bit.ly/3n2cd8r
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🔥 #AIwithPapers: we are 2,900+! 🔥

💙💛 Cheers from "Black Metal Lady Gaga" plotted by DallE-mini 💙💛

😈 Invite your friends -> https://news.1rj.ru/str/AI_DeepLearning
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🍅Segmentation with INSANE Occlusions🍅

👉CMU unveils WALT: segmenting in severe occlusion scenarios. Performance over human.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
WALT: Watch & Learn Time-lapse
4K/1080p cams on streets over a year
Performance over human-supervised
Object-occluder-occluded neural layers
Source code under MIT license

More: https://bit.ly/3n7pvjO
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🐠Largest Dataset for #autonomousdriving🐠

👉SHIFT: largest synthetic dataset for #selfdrivingcars. Shifts in cloud, rain, fog, time of day, vehicle & pedestrian density🤯

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
4,800+ clips, multi-view sensor suite
Semantic/instance, M/stereo depth
2D/3D object detection, MOT
Optical flow, point cloud registration
Visual-Odo, trajectory & human pose

More: https://bit.ly/3HJBUUT
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🦑Big Egocentric Dataset by #Meta 🦑

👉Novel dataset to speed-up research on egocentric MR/AI

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
159 sequences, multiple sensors
Scenarios: cooking, exercising, etc.
‘Desktop Activities’ via multi-view mocap
Dataset available upon request

More: https://bit.ly/3QDccVW
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🦋Transf-Codebook HD-Face Restoration🦋

👉S-Lab unveils CodeFormer: hyper-datailed face restoration from degraded clips

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Face restoration as a code prediction
Discrete CB prior in small proxy space
Controllable transformation for LQ->HQ
Robustness and global coherence
Code and models soon available

More: https://bit.ly/3QEa9B5
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🍔 Fully Controllable "NeRF" Faces 🍔

👉Neural control of pose/expressions from single portrait video

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
NeRF-control of the human head
Loss of rigidity by dynamic NeRF
3D full control/modelling of faces
No source code or models yet 😢

More: https://bit.ly/3OEjwi7
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🫀I M AVATAR: source code is out!🫀

👉Neural implicit head avatars from monocular videos

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
#3D morphing-based implicit avatar
Detailed Geometry/appearance
D-Rendering e2e learning from clips
Novel synthetic dataset for evaluation

More: https://bit.ly/3A2yzy9
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🗺️Neural Translation Image -> Map🗺️

👉A novel method for instantaneous mapping as a translation problem

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Bird’s-eye-view (BEV) map from image
A restricted data-efficient transformer
Monotonic attention from lang.domain
SOTA across several datasets

More: https://bit.ly/39MQ76Z
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🥶 E2V-SDE: biggest troll ever? 🥶

👉E2V-SDE paper (accepted to #CVPR2022) consists of texts copied from 10+ previously published papers 😂

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Latent ODEs for Irregularly-Sampled TS
Stochastic Adversarial Video Prediction
Continuous Latent Process Flows
More papers....


More: https://bit.ly/3bsL8Zw (AUDIO ON!)
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🔥🔥YOLOv6 is out: PURE FIRE!🔥🔥

👉YOLOv6 is a single-stage object detection framework for industrial applications

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Efficient Decoupled Head with SIoU Loss
Hardware-friendly for Backbone/Neck
520+ FPS on T4 + TensorRT FP16
Released under GNU General Public v3.0

More: https://bit.ly/3OLjncK
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