The Learning and Machine Perception (LAMP) team at the Computer Vision Center conducts fundamental research and technology transfer in the field of machine learning for semantic understanding of visual data. The group works with a wide variety of visual data sources: from multispectral, medical imagery and consumer camera images, to live webcam streams and video data. The returning objective is the design of efficient and accurate algorithms for the automatic extraction of semantic information from visual media.

Two papers at WACV 2024
1. Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning, Filip Szatkowski, Mateusz Pyla, Marcin Przewięźlikowski, Sebastian Cygert, Bartłomiej Twardowski, Tomasz Trzciński
2. Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning, Alex Gomez-Villa, Bartlomiej Twardowski, Kai Wang, Joost van de Weijer

Three papers were accepted:
1. Elastic Feature Consolidation for Cold Start Exemplar-free Incremental Learning (pdf).
2. Get What You Want, Not What You Don’t: Image Content Suppression for Text-to-Image Diffusion Models (pdf).
3. Divide and not forget: Ensemble of selectively trained experts in Continual Learning (pdf).

Two papers were accepted:
1. FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning (pdf).
2. Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing (pdf).

And one workshop paper:
1.IterInv: Iterative Inversion for Pixel-Level T2I Models (pdf)

Two papers were accepted:

1. Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection (pdf).
2. ICICLE: Interpretable Class Incremental Continual Learning (pdf).

Our paper on Planckian Jitter for better color image representation has been accepted for ICLR. Great work Simone and Alex!

1. Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training (pdf).

Kai has two papers on BMVC 2022 !
Attention Distillation: self-supervised vision transformer students need more guidance
and
Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification

Dipam has published his work on incremental semantic segmentation at WACV 2023:
Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation

Shiqi-s paper Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation got accepted for NeurIPS 2022.

The survey paper Class-incremental learning: survey and performance evaluation on image classification is accepted at PAMI.

Also check the code framework FACIL that allows to reproduce the results from the survey.

The paper on zero-shot has also been accepted a PAMI: Generative Multi-Label Zero-Shot Learning

And in IJCV: MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains