WiFi-based human activity recognition (HAR) faces significant challenges in cross-domain generalization due to dynamic environmental variations, device heterogeneity, and subtle changes in human behavior. In this paper, we introduce DATTA – Domain-Adversarial Test-Time Adaptation – a novel framework that combines domain-adversarial training (DAT) with test-time adaptation (TTA) and a random weight-resetting mechanism. Unlike previous approaches that apply these techniques in isolation, DATTA is specifically tailored for WiFi-based HAR: it leverages DAT to learn robust, domain-invariant features while TTA continuously refines the model on streaming data. To mitigate catastrophic forgetting during adaptation, we incorporate a weight-resetting mechanism, ensuring sustained performance over prolonged domain shifts. Our extensive experiments on the Widar3.0-G6D dataset demonstrate that DATTA not only outperforms state-of-the-art methods by up to 8.1% in F1-Score but also achieves real-time inference with a lightweight architecture, making it a compelling solution for practical WiFi sensing applications. The PyTorch implementation of DATTA is publicly available at: https://github.com/StrohmayerJ/DATTA.

In this paper, we present ECSIC, a novel learned method for stereo image compression. Our proposed method compresses the left and right images in a joint manner by exploiting the mutual information between the images of the stereo image pair using a novel stereo cross attention (SCA) module and two stereo context modules. The SCA module performs cross-attention restricted to the corresponding epipolar lines of the two images and processes them in parallel. The stereo context modules improve the entropy estimation of the second encoded image by using the first image as a context. We conduct an extensive ablation study demonstrating the effectiveness of the proposed modules and a comprehensive quantitative and qualitative comparison with existing methods. ECSIC achieves state-of-the-art performance in stereo image compression on the two popular stereo image datasets Cityscapes and InStereo2k while allowing for fast encoding and decoding.

We propose a learned method for stereo image compression that leverages the similarity of the left and right images in a stereo pair due to overlapping fields of view. The left image is compressed by a learned compression method based on an autoencoder with a hyperprior entropy model. The right image uses this information from the previously encoded left image in both the encoding and decoding stages. In particular, for the right image, we encode only the residual of its latent representation to the optimally shifted latent of the left image. On top of that, we also employ a stereo attention module to connect left and right images during decoding. The performance of the proposed method is evaluated on two benchmark stereo image datasets (Cityscapes and InStereo2K) and outperforms previous stereo image compression methods while being significantly smaller in model size.

The detection of baselines of text is a necessary preprocessing step for many modern methods of automatic handwriting recognition. In this work, we present a two-stage system for the automatic detection of text baselines of handwritten text. In a first step, we perform pixel-wise segmentation on the document image to classify pixels as baselines, start points, end points and background. This segmentation is then used to extract the start points of lines. Starting from these points we extract the baseline using a recurrent convolutional neural network that directly outputs the baseline coordinates. This method allows the direct extraction of baseline coordinates as the output of a neural network without the use of any post-processing steps. We evaluate the model on the cBAD dataset from the ICDAR 2019 competition on baseline detection.