Ultra-low altitude UAVs (below 120 meters) are gaining importance in the booming low-altitude economy, where GNSS signals are often unreliable or unavailable. Vision-based localization emerges as a promising alternative; however, existing benchmarks are not designed for ultra-low flight and typically adopt pinhole cameras with limited field of view, making them less effective in handling occlusions and repetitive textures near the ground. To address these limitations, we introduce the first panoramic UAV localization dataset tailored for ultra-low altitude scenarios. Built on a four-fisheye-camera system in the high-fidelity RflySim platform, our dataset captures diverse conditions — including day/night cycles, extreme weather, and dynamic obstacles — and contains over hundreds of thousands of frames. It is further enhanced with real-world UAV panoramic data to narrow the sim-to-real gap and will be continuously updated for broader applicability. Comprehensive experiments confirm the effectiveness and transferability of our dataset, establishing it as a robust benchmark for future research in vision-based UAV localization.

Ensuring the safety of LLM-generated content is essential for real-world deployment.Most existing guardrail models formulate moderation as a fixed binary classification task, implicitly assuming a fixed definition of harmfulness.In practice, enforcement strictnesshow conservatively harmfulness is defined and enforced-varies across platforms and evolves over time, making binary moderators brittle under shifting requirements.We first introduce FlexBench, a strictness-adaptive LLM moderation benchmark that enables controlled evaluation under multiple strictness regimes.Experiments on FlexBench reveal substantial cross-strictness inconsistency in existing moderators: models that perform well under one regime can degrade substantially under others, limiting their practical usability.To address this, we propose FlexGuard, an LLM-based moderator that outputs a calibrated continuous risk score reflecting risk severity and supports strictness-specific decisions via thresholding.We train FlexGuard via risk-alignment optimization to improve score-severity consistency and provide practical threshold selection strategies to adapt to target strictness at deployment.Experiments on FlexBench and public benchmarks demonstrate that FlexGuard achieves higher moderation accuracy and substantially improved robustness under varying strictness.

We introduce IndEgo, a multimodal egocentric and exocentric dataset addressing common industrial tasks, including assembly/disassembly, logistics and organisation, inspection and repair, woodworking, and others. The dataset contains 3,460 egocentric recordings (approximately 197 hours), along with 1,092 exocentric recordings (approximately 97 hours). A key focus of the dataset is collaborative work, where two workers jointly perform cognitively and physically intensive tasks. The egocentric recordings include rich multimodal data and added context via eye gaze, narration, sound, motion, and others. We provide detailed annotations (actions, summaries, mistake annotations, narrations), metadata, processed outputs (eye gaze, hand pose, semi-dense point cloud), and benchmarks on procedural and non-procedural task understanding, Mistake Detection, and reasoning-based Question Answering. Baseline evaluations for Mistake Detection, Question Answering and collaborative task understanding show that the dataset presents a challenge for the state-of-the-art multimodal models. Our dataset is available at: https://huggingface.co/datasets/FraunhoferIPK/IndEgo

Artificial neural networks achieve state-of-the-art performance in many branches of engineering. As such they are used for all kinds of tasks and nowadays are desired to be used on mobile devices like smartphones. Due to limited hardware resources or limited channel capacity on mobile devices, compression of neural network models to reduce storage or transmission costs is desired. Furthermore, reduced complexity is of interest. This work investigates introducing the curvature of the loss surface in the training of artificial neural networks and analyzes its benefit for the compression of neural networks through quantization and pruning of its weights. As proof-of-concept, three small LeNet-based neural networks were trained using a novel loss function consisting of a weighted average of the cross-entropy loss and the Frobenius norm of the hessian matrix. That way both, the loss as well as the local curvature, are minimized concurrently. Using the proposed method, mean test accuracies on the MNIST and FashionMNIST datasets after quantization were considerably improved by up to about 47.6 % for 1 bit quantization on MNIST and about 27.8 % on FashionMNIST compared to quantization after training without curvature information. Additionally, pruning was found to benefit from introducing curvature in the training as well with an increase of up to about 14.6 % mean test accuracy compared to pruning after training without curvature except for isolated cases. Training the artificial neural networks first without curvature information and subsequent training by only one epoch using curvature information allowed to increase the mean test accuracy after quantization at 1 bit by about 16 %. The proposed method can potentially improve the accuracy after compression irrespective of the compression method applied.

The fusion of multiple features is important for achieving state-of-the-art face recognition results. This has been proven in both traditional and deep learning approaches. Existing feature fusion methods either reduce the dimensionality of each feature first and then concatenate all low-dimensional feature vectors, named as DR-Cat, or the vice versa, named as Cat-DR. However, DR-Cat ignores the correlation information between different features which is useful for classification. In Cat-DR, on the other hand, the correlation information estimated from the training data may not be reliable especially when the number of training samples is limited. We propose a covariance matrix regularization (CMR) technique to solve problems of DR-Cat and Cat-DR. It works by assigning weights to cross-feature covariances in the covariance matrix of training data . Thus the feature correlation estimated from training data is regularized before being used to train the feature fusion model. The proposed CMR is applied to 4 feature fusion schemes: fusion of pixel values from 3 color channels, fusion of LBP features from 3 color channels, fusion of pixel values and LBP features from a single color channel, and fusion of CNN features extracted by 2 deep models. Extensive experiments of face recognition and verification are conducted on databases including MultiPIE, Georgia Tech, AR and LFW. Results show that the proposed CMR technique significantly and consistently outperforms the best single feature , DR-Cat and Cat-DR.

Face images captured by surveillance cameras are often of low resolution (LR), which adversely affects the performance of their matching with high-resolution (HR) gallery images. Existing methods including super resolution, coupled mappings (CMs), multidimensional scaling, and convolutional neural network yield only modest performance. In this letter, we propose the deep coupled ResNet (DCR) model. It consists of one trunk network and two branch networks. The trunk network, trained by face images of three significantly different resolutions, is used to extract discriminative features robust to the resolution change. Two branch networks, trained by HR images and images of the targeted LR, work as resolution-specific CMs to transform HR and corresponding LR features to a space where their difference is minimized. Model parameters of branch networks are optimized using our proposed CM loss function, which considers not only the discriminability of HR and LR features, but also the similarity between them. In order to deal with various possible resolutions of probe images, we train multiple pairs of small branch networks while using the same trunk network. Thorough evaluation on LFW and SCface databases shows that the proposed DCR model achieves consistently and considerably better performance than the state of the arts.

LBP-based color features have shown excellent performance for color face recognition tasks, such as Color LBP, CLBP and LCVBP. However, existing methods encode the inter-channel information on pairs of color channels by applying the same spatial structure as that used in the intra-channel encoding. This results in a very high dimensional feature vector yet ineffective in encoding inter-channel information. Moreover, the difference of pixel values across color channels may not be a proper measure if they are not quantitatively comparable. To tackle these problems of existing methods, we propose a novel LBP-based color feature, Ternary-Color LBP (TCLBP), to encode the inter-channel information more effectively and efficiently. Extensive experiments on 4 public face databases, Color FERET, Georgia Tech, FRGC and LFW, are conducted to verify the effectiveness of the proposed TCLBP color feature for face recognition. Results show that the proposed TCLBP leads to visibly better face recognition performance than Color LBP, CLBP and LCVBP consistently over the 4 databases.

The three color components specifying a color can be defined in various ways leading to significantly different classification abilities. Several effective color spaces including RQCr, DCS and ZRG have been proposed to achieve better face recognition performance. However, their performance is not consistent on different databases. What's more, the framework of effective color spaces has not been thoroughly studied yet. In this paper, we propose an effective color space LC\C2 based on a framework of effective color spaces. LC\C2 consists of one discriminant luminance component L and two discriminant chrominance components C\C2. To find the discriminant luminance component, 4 luminance components from existing effective color models are compared. After that, the weighted color space normalization technique (WCSN) is applied on the DCS color space to generate two complementary and discriminative chrominance components. Experiments conducted on three databases (FRGC, AR and CMU Multi-PIE) show that the proposed color space LC\C2 achieves the best face recognition performance consistently.

Due to high dimensionality of images or generated color features, different color channels are usually processed separately and then concatenated together into a feature vector for classification. This makes channel fusion a crucial step in color face recognition (FR) systems. However, existing methods simply concatenate channel-wise color features without identifying the importance or reliability of features in different color channels. In this paper, we propose a color channel fusion (CCF) approach using jointly dimension reduction algorithms to select more features from reliable and discriminative channels. Experiments using two different dimension reduction approaches, two different types of features on three image datasets show that CCF achieves consistently better performance than color channel concatenation (CCC) method which deals with different color channels equally.