Rethinking Transformer-based Set Prediction for Object Detection

Last update: Dec 03, 2022

Related tags

Deep Learning TSP-Detection

Overview

Rethinking Transformer-based Set Prediction for Object Detection

Here are the code for the ICCV paper. The code is adapted from Detectron2 and AdelaiDet.

All the model are trained on 4 V100 GPUs.

Prerequisites

Modify the environment name and environment prefix in environment.yml and run

conda env create -f environment.yml

git clone https://github.com/facebookresearch/detectron2.git
cd detectron2
git reset --hard b88c6c06563e4db1139aafbd6d8d97d1fa7a57e4
pip install -e .

Rreproducing Results

For TSP-FCOS,

bash tsp_fcos.sh

For TSP-RCNN,

bash tsp_rcnn.sh

Citation

@InProceedings{Sun_2021_ICCV,
    author    = {Sun, Zhiqing and Cao, Shengcao and Yang, Yiming and Kitani, Kris M.},
    title     = {Rethinking Transformer-Based Set Prediction for Object Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3611-3620}
}

Owner

Zhiqing Sun

Third-year Ph.D. student at LTI, CMU

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Rethinking Transformer-based Set Prediction for Object Detection

Related tags

Overview

Rethinking Transformer-based Set Prediction for Object Detection

Prerequisites

Rreproducing Results

Citation

Owner

Zhiqing Sun

Geometry-Free View Synthesis: Transformers and no 3D Priors

[ACM MM 2021] Joint Implicit Image Function for Guided Depth Super-Resolution

NAS-Bench-x11 and the Power of Learning Curves

A graph-to-sequence model for one-step retrosynthesis and reaction outcome prediction.

This is the official implementation for the paper "Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization" in NeurIPS 2021.

Distributing reference energies for SMIRNOFF implementations

A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generative Modeling" (ICCV 2021)

StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators

N-gram models- Unsmoothed, Laplace, Deleted Interpolation

TDN: Temporal Difference Networks for Efficient Action Recognition

This repo will contain code to reproduce and build upon understanding transfer learning

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Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2002.11798)

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