12 accepted papers
Synthetic time series are vital for data augmentation, stress testing, and prototyping in quantitative finance. Yet in cryptocurrency markets, characterized by 24/7 trading, extreme volatility, and rapid regime shifts, existing Time Series Generation (TSG) methods and benchmarks often fall short, je…
Many state‑of‑the‑art anomaly detection models operate as black boxes, limiting interpretability and hindering reliable deployment. While recent advances in explainable artificial intelligence have focused on explaining why individual instances are detected as anomalous, comparatively little attenti
Web applications are prime targets for cyberattacks due to their role as entry points to vital services and sensitive data repositories. Traditional penetration testing is expensive and requires specialized expertise, creating scalability challenges for securing the expanding web ecosystem. While la…
Deep learning-based lane detection (LD) plays a critical role in autonomous driving and advanced driver assistance systems. However, its vulnerability to backdoor attacks presents a significant security concern. Existing backdoor attack methods on LD often exhibit limited practical utility due to th
Practical classification requires both high predictive accuracy and reliable confidence for human-AI collaboration. Given that a high-quality dataset is expensive and sometimes impossible, learning with noisy labels (LNL) is of great importance. The state-of-the-art works propose many denoising appr…
Image-based 3D texture transfer from a single 2D reference image enables practical customization of 3D object appearances with minimal manual effort. Adapted 2D editing and text-driven 3D editing approaches can serve this purpose. However, 2D editing typically involves frame-by-frame manipulation, o…
The proliferation of multimodal memes in the social media era demands that multimodal Large Language Models (mLLMs) effectively understand meme harmfulness. Existing benchmarks for assessing mLLMs on harmful meme understanding rely on accuracy-based, model-agnostic evaluations using static datasets.…
Programming often involves converting detailed and complex specifications into code, a process during which developers typically utilize visual aids to more effectively convey concepts. While recent developments in Large Multimodal Models have demonstrated remarkable abilities in visual reasoning an…
The fine-grained attribute descriptions can significantly supplement the valuable semantic information for person image, which is vital to the success of person re-identification (ReID) task. However, current ReID algorithms typically failed to effectively leverage the rich contextual information av…
For multilingual training, we present CrossInit, an initialization method that initializes embeddings into similar geometrical structures across languages in an unsupervised manner. CrossInit leverages a common cognitive linguistic mechanism, Zipf’s law, which indicates that similar concepts across…
Improving the performance of automatic speech recognition (ASR) in adverse acoustic environments is a long-term tough task. Although many robust ASR systems based on conventional microphones have been developed, their performance with air-conducted (AC) speech is still far from satisfactory in low s…
Tracking of items on shelves is an important but time-consuming task in inventory control. In particular, books in public libraries are frequently borrowed and returned, even misplaced, and proves a challenge to be tracked on a daily basis. To track the books efficiently, we propose the Autonomous R…