Die Rolle von Lernaufgaben zur Förderung sprachlicher Interaktion im Anfängerunterricht Chinesisch als neu einsetzende Fremdsprache

Gegenstand der vorliegenden Arbeit sind kompetenzorientierte Lernaufgaben, sprachliche Interaktion und Chinesisch als Fremdsprache. Der Schwerpunkt der Forschung liegt in der Unterrichtsreihe, die in drei verschiedenen Kursen der Einführungsphase (EF) der Oberstufe erfolgte. Jeder Kurs wurde in 15 Unterrichtsstunden mit einer komplexen Lernaufgabe unterrichtet. Die Arbeit geht der Frage nach, welche Rolle Lernaufgaben zur Förderung sprachlicher Interaktion im ChaF-Unterricht spielen. Zur Beantwortung dieser Frage wurden zunächst ein Projekt mit einer Lernaufgabe durchgeführt und schließlich eine schriftliche Befragung als methodologisches Vorgehen in allen Gruppen angewandt. Die Ergebnisse machen deutlich, dass Lernaufgaben neue Aspekte für den herkömmlichen lehrwerkorientierten Fremdsprachen- bzw. ChaFUnterricht schaffen und auch in diesem Kontext die Lehrwerke ergänzen können

Go Beyond Point Pairs: A General and Accurate Sim2Real Object Pose Voting Method with Efficient Online Synthetic Training

Object pose estimation is an important topic in 3D vision. Though most current state-of-the-art method that trains on real-world pose annotations achieve good results, the cost of such real-world training data is too high. In this paper, we propose a novel method for sim-to-real pose estimation, which is effective on both instance-level and category-level settings. The proposed method is based on the point-pair voting scheme from CPPF to vote for object centers, orientations, and scales. Unlike naive point pairs, to enrich the context provided by each voting unit, we introduce N-point tuples to fuse features from more than two points. Besides, a novel vote selection module is leveraged in order to discard those `bad' votes. Experiments show that our proposed method greatly advances the performance on both instance-level and category-level scenarios. Our method further narrows the gap between sim-to-real and real-training methods by generating synthetic training data online efficiently, while all previous sim-to-real methods need to generate data offline, because of their complex background synthesizing or photo-realistic rendering. Code repository: https://github.com/qq456cvb/BeyondPPF

DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures

Diffusion models have recently exhibited remarkable performance on synthetic data. After a diffusion path is selected, a base model, such as UNet, operates as a denoising autoencoder, primarily predicting noises that need to be eliminated step by step. Consequently, it is crucial to employ a model that aligns with the expected budgets to facilitate superior synthetic performance. In this paper, we meticulously analyze the diffusion model and engineer a base model search approach, denoted "DiffNAS". Specifically, we leverage GPT-4 as a supernet to expedite the search, supplemented with a search memory to enhance the results. Moreover, we employ RFID as a proxy to promptly rank the experimental outcomes produced by GPT-4. We also adopt a rapid-convergence training strategy to boost search efficiency. Rigorous experimentation corroborates that our algorithm can augment the search efficiency by 2 times under GPT-based scenarios, while also attaining a performance of 2.82 with 0.37 improvement in FID on CIFAR10 relative to the benchmark IDDPM algorithm

GATOR: Graph-Aware Transformer with Motion-Disentangled Regression for Human Mesh Recovery from a 2D Pose

3D human mesh recovery from a 2D pose plays an important role in various applications. However, it is hard for existing methods to simultaneously capture the multiple relations during the evolution from skeleton to mesh, including joint-joint, joint-vertex and vertex-vertex relations, which often leads to implausible results. To address this issue, we propose a novel solution, called GATOR, that contains an encoder of Graph-Aware Transformer (GAT) and a decoder with Motion-Disentangled Regression (MDR) to explore these multiple relations. Specifically, GAT combines a GCN and a graph-aware self-attention in parallel to capture physical and hidden joint-joint relations. Furthermore, MDR models joint-vertex and vertex-vertex interactions to explore joint and vertex relations. Based on the clustering characteristics of vertex offset fields, MDR regresses the vertices by composing the predicted base motions. Extensive experiments show that GATOR achieves state-of-the-art performance on two challenging benchmarks.Comment: Accepted by ICASSP 202

Co-Evolution of Pose and Mesh for 3D Human Body Estimation from Video

Despite significant progress in single image-based 3D human mesh recovery, accurately and smoothly recovering 3D human motion from a video remains challenging. Existing video-based methods generally recover human mesh by estimating the complex pose and shape parameters from coupled image features, whose high complexity and low representation ability often result in inconsistent pose motion and limited shape patterns. To alleviate this issue, we introduce 3D pose as the intermediary and propose a Pose and Mesh Co-Evolution network (PMCE) that decouples this task into two parts: 1) video-based 3D human pose estimation and 2) mesh vertices regression from the estimated 3D pose and temporal image feature. Specifically, we propose a two-stream encoder that estimates mid-frame 3D pose and extracts a temporal image feature from the input image sequence. In addition, we design a co-evolution decoder that performs pose and mesh interactions with the image-guided Adaptive Layer Normalization (AdaLN) to make pose and mesh fit the human body shape. Extensive experiments demonstrate that the proposed PMCE outperforms previous state-of-the-art methods in terms of both per-frame accuracy and temporal consistency on three benchmark datasets: 3DPW, Human3.6M, and MPI-INF-3DHP. Our code is available at https://github.com/kasvii/PMCE.Comment: Accepted by ICCV 2023. Project page: https://kasvii.github.io/PMC

Self-supervised Guided Hypergraph Feature Propagation for Semi-supervised Classification with Missing Node Features

Graph neural networks (GNNs) with missing node features have recently received increasing interest. Such missing node features seriously hurt the performance of the existing GNNs. Some recent methods have been proposed to reconstruct the missing node features by the information propagation among nodes with known and unknown attributes. Although these methods have achieved superior performance, how to exactly exploit the complex data correlations among nodes to reconstruct missing node features is still a great challenge. To solve the above problem, we propose a self-supervised guided hypergraph feature propagation (SGHFP). Specifically, the feature hypergraph is first generated according to the node features with missing information. And then, the reconstructed node features produced by the previous iteration are fed to a two-layer GNNs to construct a pseudo-label hypergraph. Before each iteration, the constructed feature hypergraph and pseudo-label hypergraph are fused effectively, which can better preserve the higher-order data correlations among nodes. After then, we apply the fused hypergraph to the feature propagation for reconstructing missing features. Finally, the reconstructed node features by multi-iteration optimization are applied to the downstream semi-supervised classification task. Extensive experiments demonstrate that the proposed SGHFP outperforms the existing semi-supervised classification with missing node feature methods.Comment: Accepted by 48th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023

Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense

Visual commonsense understanding requires Vision Language (VL) models to not only understand image and text but also cross-reference in-between to fully integrate and achieve comprehension of the visual scene described. Recently, various approaches have been developed and have achieved high performance on visual commonsense benchmarks. However, it is unclear whether the models really understand the visual scene and underlying commonsense knowledge due to limited evaluation data resources. To provide an in-depth analysis, we present a Multimodal Evaluation (ME) pipeline to automatically generate question-answer pairs to test models' understanding of the visual scene, text, and related knowledge. We then take a step further to show that training with the ME data boosts the model's performance in standard VCR evaluation. Lastly, our in-depth analysis and comparison reveal interesting findings: (1) semantically low-level information can assist the learning of high-level information but not the opposite; (2) visual information is generally under utilization compared with text.Comment: Accepted to EMNLP 2022 Long Pape

Evaluating the Effectiveness and Robustness of Visual Similarity-based Phishing Detection Models

Phishing attacks pose a significant threat to Internet users, with cybercriminals elaborately replicating the visual appearance of legitimate websites to deceive victims. Visual similarity-based detection systems have emerged as an effective countermeasure, but their effectiveness and robustness in real-world scenarios have been unexplored. In this paper, we comprehensively scrutinize and evaluate state-of-the-art visual similarity-based anti-phishing models using a large-scale dataset of 450K real-world phishing websites. Our analysis reveals that while certain models maintain high accuracy, others exhibit notably lower performance than results on curated datasets, highlighting the importance of real-world evaluation. In addition, we observe the real-world tactic of manipulating visual components that phishing attackers employ to circumvent the detection systems. To assess the resilience of existing models against adversarial attacks and robustness, we apply visible and perturbation-based manipulations to website logos, which adversaries typically target. We then evaluate the models' robustness in handling these adversarial samples. Our findings reveal vulnerabilities in several models, emphasizing the need for more robust visual similarity techniques capable of withstanding sophisticated evasion attempts. We provide actionable insights for enhancing the security of phishing defense systems, encouraging proactive actions. To the best of our knowledge, this work represents the first large-scale, systematic evaluation of visual similarity-based models for phishing detection in real-world settings, necessitating the development of more effective and robust defenses.Comment: 12 page