DETR - End to end object detection with transformers (ECCV2020)

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This is the talk associated with the ECCV 2020 oral paper "End to end object detection using transformer" by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.

  1. Nicolas Carion
  2. Deep learning
  3. DETR
  4. object detection
  5. transformer
  6. resnet
Рекомендации по теме
DETR: End-to-End Object 0:40:57

DETR: End-to-End Object Detection with Transformers (Paper Explained)

DETR - End 0:09:35

DETR - End to end object detection with transformers (ECCV2020)

DETR: End-to-End Object 0:31:19

DETR: End-to-End Object Detection with Transformers | Paper Explained

DETR: End-to-End Object 0:06:14

DETR: End-to-End Object Detection with Transformers

Object detection Using 0:18:21

Object detection Using Detection Transformer (Detr) on custom dataset

DETR (DEtection TRansformer) 0:15:08

DETR (DEtection TRansformer) | Lecture 38 (Part 2) | Applied Deep Learning (Supplementary)

[Tutorial] Training End-to-end 0:25:16

[Tutorial] Training End-to-end Object Detection with Transformer(DETR) model on custom dataset

Facebook DETR | 1:42:52

Facebook DETR | ML Coding Series | End to end object detection with transformers

DETR - End-to-End 0:02:02

DETR - End-to-End Object Detection with Transformers - DEMO

Object Detection Part 0:04:28

Object Detection Part 7: Detection Transformers (DETR), Object Queries

DETR: End to 0:03:21

DETR: End to End Object Detection and Panoptic Segmentation

DETR - End-to-End 0:02:53

DETR - End-to-End Object Detection with Transformers - DEMO

ELI5: DETR 0:01:52

ELI5: DETR

Reimplementation Showcase DETR: 0:01:07

Reimplementation Showcase DETR: End-to-End Object Detection with Transformers | CVDL Final Project

[Paper Review] DETR: 0:15:36

[Paper Review] DETR: End-to-End Object Detection with Transformer

DETR End to 0:26:55

DETR End to End Object Detection with Transformers

Deformable DETR | 0:08:17

Deformable DETR | Lecture 38 (Part 3) | Applied Deep Learning (Supplementary)

DINO - DETR 0:41:10

DINO - DETR with Improved DeNoising AnchorBoxes for End-to-End Object Detection

PR-284: End-to-End Object 0:34:56

PR-284: End-to-End Object Detection with Transformers(DETR)

Decoding DETR 1:08:51

Decoding DETR

L-7 | DETR 0:17:44

L-7 | DETR | Object detection Using Detection Transformer on custom dataset

How I Read 0:45:24

How I Read a Paper: Facebook's DETR (Video Tutorial)

VPS-01: DETR: End-to-End 0:24:04

VPS-01: DETR: End-to-End Object Detection with Transformers paper review

YOLOv8 with Real 0:00:21

YOLOv8 with Real Time Detection Transformer RT-DETR

Комментарии
Автор

A very nice presentation with clear visualizations and easy-to-understand explanations! Great Work!!🌟🌟🌟🌟🌟
Smooth animations 👌

kvnptl
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Outstanding work. I’m also very interested in the, arguably more difficult, small object detection problem.

QuintinMassey
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Nice work!
A small correction to what you said: "Semantic segmentation labels each pixel in the whole image. It is not restricted to only pixels in the background".

MarioHari
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Excellent Explanation.
But I want to know the most important thing in this video,

syedabdul
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Thank you for the great work and the presentation!

Ninomff
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Thanks for sharing!
Could you please explain what you mean by full differentiable and how other methods might not be fully differentiable?

Ramakrishnan-bqis
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Amazing! What was the main motivation behind using a sequence model for an object detection?

rohinim
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What this mean?: "since the transformer is a permutation
equivalent some extra care is required to retain
the 2d structure of the image."

ZobeirRaisi