# Designing High-Performing Networks for Multi-Scale Computer Vision

arXiv: https://arxiv.org/abs/2402.12536 · HTML: https://mlsanity.com/paper/2402.12536/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 122 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Pruning Filters for Efficient ConvNets](https://mlsanity.com/paper/1608.08710/) (2016)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://mlsanity.com/paper/2006.04388/) (2020)
- [Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation](https://mlsanity.com/paper/2206.02777/) (2022)
- [DETRs with Collaborative Hybrid Assignments Training](https://mlsanity.com/paper/2211.12860/) (2022)
- [K-Net: Towards Unified Image Segmentation](https://mlsanity.com/paper/2106.14855/) (2021)
- [RTMDet: An Empirical Study of Designing Real-Time Object Detectors](https://mlsanity.com/paper/2212.07784/) (2022)
- [Searching for Activation Functions](https://mlsanity.com/paper/1710.05941/) (2017)
- [Pruning Convolutional Neural Networks for Resource Efficient Inference](https://mlsanity.com/paper/1611.06440/) (2016)
- [Detection Transformer with Stable Matching](https://mlsanity.com/paper/2304.04742/) (2023)
- [DAMO-YOLO : A Report on Real-Time Object Detection Design](https://mlsanity.com/paper/2211.15444/) (2022)
- [SOLOv2: Dynamic and Fast Instance Segmentation](https://mlsanity.com/paper/2003.10152/) (2020)
- [Beyond Skip Connections: Top-Down Modulation for Object Detection](https://mlsanity.com/paper/1612.06851/) (2016)
- [GiraffeDet: A Heavy-Neck Paradigm for Object Detection](https://mlsanity.com/paper/2202.04256/) (2022)
- [FQDet: Fast-converging Query-based Detector](https://mlsanity.com/paper/2210.02318/) (2022)
- [EffSeg: Efficient Fine-Grained Instance Segmentation using Structure-Preserving Sparsity](https://mlsanity.com/paper/2307.01545/) (2023)

## Abstract (arXiv)

Since the emergence of deep learning, the computer vision field has flourished with models improving at a rapid pace on more and more complex tasks. We distinguish three main ways to improve a computer vision model: (1) improving the data aspect by for example training on a large, more diverse dataset, (2) improving the training aspect by for example designing a better optimizer, and (3) improving the network architecture (or network for short). In this thesis, we chose to improve the latter, i.e. improving the network designs of computer vision models. More specifically, we investigate new network designs for multi-scale computer vision tasks, which are tasks requiring to make predictions about concepts at different scales. The goal of these new network designs is to outperform existing baseline designs from the literature. Specific care is taken to make sure the comparisons are fair, by guaranteeing that the different network designs were trained and evaluated with the same settings. Code is publicly available at https://github.com/CedricPicron/DetSeg.