4.5k citations · 8.5k across the 90 of their papers we have counts for
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Simplified priors for Object-Centric Learning
Vihang Patil, Andreas Radler, Daniel Klotz +1
Humans excel at abstracting data and constructing \emph{reusable} concepts, a capability lacking in current continual learning systems. The field of object-centric learning address…
Comparison Visual Instruction Tuning
Wei Lin, Muhammad Jehanzeb Mirza, Sivan Doveh +4
Comparing two images in terms of Commonalities and Differences (CaD) is a fundamental human capability that forms the basis of advanced visual reasoning and interpretation. It is e…
Vision-LSTM: xLSTM as Generic Vision Backbone
Benedikt Alkin, Maximilian Beck, Korbinian Pöppel +2
Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing. Recently, the Long Short-Term Memory (LSTM) has…
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter +1
We introduce MIM (Masked Image Modeling)-Refiner, a contrastive learning boost for pre-trained MIM models. MIM-Refiner is motivated by the insight that strong representations withi…
Linear Alignment of Vision-language Models for Image Captioning
Fabian Paischer, Markus Hofmarcher, Sepp Hochreiter +1
Recently, vision-language models like CLIP have advanced the state of the art in a variety of multi-modal tasks including image captioning and caption evaluation. Many approaches l…
Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget
Johannes Lehner, Benedikt Alkin, Andreas Fürst +3
Masked Image Modeling (MIM) methods, like Masked Autoencoders (MAE), efficiently learn a rich representation of the input. However, for adapting to downstream tasks, they require a…
Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks
Yonghao Xu, Weikang Yu, Pedram Ghamisi +2
The synthesis of high-resolution remote sensing images based on text descriptions has great potential in many practical application scenarios. Although deep neural networks have ac…
Patch Refinement -- Localized 3D Object Detection
Johannes Lehner, Andreas Mitterecker, Thomas Adler +3
We introduce Patch Refinement a two-stage model for accurate 3D object detection and localization from point cloud data. Patch Refinement is composed of two independently trained V…