# Knowledge-Guided Object Discovery with Acquired Deep Impressions

arXiv: https://arxiv.org/abs/2103.10611 · HTML: https://mlsanity.com/paper/2103.10611/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 37 references that are in the map.
- First posted on arXiv 19 Mar 2021.

## Builds on (most cited first, 24 of 24)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [MONet: Unsupervised Scene Decomposition and Representation](https://mlsanity.com/paper/1901.11390/) (2019)
- [Multi-Object Representation Learning with Iterative Variational Inference](https://mlsanity.com/paper/1903.00450/) (2019)
- [Semi-Supervised Learning with Ladder Networks](https://mlsanity.com/paper/1507.02672/) (2015)
- [GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations](https://mlsanity.com/paper/1907.13052/) (2019)
- [Attend, Infer, Repeat: Fast Scene Understanding with Generative Models](https://mlsanity.com/paper/1603.08575/) (2016)
- [Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects](https://mlsanity.com/paper/1806.01794/) (2018)
- [Neural Expectation Maximization](https://mlsanity.com/paper/1708.03498/) (2017)
- [VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions](https://mlsanity.com/paper/2003.05541/) (2020)
- [Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions](https://mlsanity.com/paper/1802.10353/) (2018)
- [Building machines that adapt and compute like brains](https://mlsanity.com/paper/1711.04203/) (2017)
- [Iterative Amortized Inference](https://mlsanity.com/paper/1807.09356/) (2018)
- [Tagger: Deep Unsupervised Perceptual Grouping](https://mlsanity.com/paper/1606.06724/) (2016)
- [FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery](https://mlsanity.com/paper/1811.11155/) (2018)
- [Learning to Manipulate Individual Objects in an Image](https://mlsanity.com/paper/2004.05495/) (2020)
- [Binding via Reconstruction Clustering](https://mlsanity.com/paper/1511.06418/) (2015)
- [Efficient inference in occlusion-aware generative models of images](https://mlsanity.com/paper/1511.06362/) (2015)
- [Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping](https://mlsanity.com/paper/1902.02502/) (2019)
- [Recurrent Ladder Networks](https://mlsanity.com/paper/1707.09219/) (2017)

## Built on by (most cited first, 6 of 6)

- [Compositional Scene Representation Learning via Reconstruction: A Survey](https://mlsanity.com/paper/2202.07135/) (2022)
- [Unsupervised Learning of Compositional Scene Representations from Multiple Unspecified Viewpoints](https://mlsanity.com/paper/2112.03568/) (2021)
- [Unsupervised Object-Centric Learning from Multiple Unspecified Viewpoints](https://mlsanity.com/paper/2401.01922/) (2024)
- [Compositional Law Parsing with Latent Random Functions](https://mlsanity.com/paper/2209.09115/) (2022)
- [Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and Prediction](https://mlsanity.com/paper/2301.08951/) (2023)
- [Compositional Scene Modeling with Global Object-Centric Representations](https://mlsanity.com/paper/2211.11500/) (2022)

## Abstract (arXiv)

We present a framework called Acquired Deep Impressions (ADI) which continuously learns knowledge of objects as "impressions" for compositional scene understanding. In this framework, the model first acquires knowledge from scene images containing a single object in a supervised manner, and then continues to learn from novel multi-object scene images which may contain objects that have not been seen before without any further supervision, under the guidance of the learned knowledge as humans do. By memorizing impressions of objects into parameters of neural networks and applying the generative replay strategy, the learned knowledge can be reused to generate images with pseudo-annotations and in turn assist the learning of novel scenes. The proposed ADI framework focuses on the acquisition and utilization of knowledge, and is complementary to existing deep generative models proposed for compositional scene representation. We adapt a base model to make it fall within the ADI framework and conduct experiments on two types of datasets. Empirical results suggest that the proposed framework is able to effectively utilize the acquired impressions and improve the scene decomposition performance.