Online scams are a growing form of digital harm, yet scam research remains constrained by fragmented datasets and limited annotation depth. We introduce SCAMBENCH, a benchmark of 1,836 authentic scam messages curated from victim-reported incidents across six scam types: employment, investment, imposter, sweepstakes/lottery/prizes, tech support, and advance fee loan scams, along with 1,834 real-world non-scam examples from legitimate sources. SCAMBENCH provides multi-dimensional annotations that capture persuasion strategies from the scammer perspective, red flags from the user perspective, and severity levels reflecting potential harm and urgency. These annotations are produced through a human-AI collaborative pipeline that uses expert-labeled examples to calibrate and validate LLM-assisted annotation at scale. Our analysis shows that these signals vary significantly across scam types, highlighting substantial scam-type heterogeneity. We further use annotated evidence spans to generate cue-removed hard scam examples by rephrasing explicit persuasion and red-flag cues. Evaluating five LLM-based detectors on original and rewritten scams shows that all five detectors perform worse on cue-removed examples, and the performance drop persists on a human-validated subset, suggesting sensitivity to surface-level cues. A qualitative case study identifies five recurring failure patterns. SCAMBENCH provides a resource for scam detection, robustness evaluation, and scam education, publicly available at https://huggingface.co/datasets/Shouninger/ScamBench.

Tensor-based fusion methods have been proven effective in multimodal fusion tasks. However, existing tensor-based methods make a poor use of the fine-grained temporal dynamics of multimodal sequential features. Motivated by this observation, this paper proposes a novel multimodal fusion method called Fine-Grained Temporal Low-Rank Multimodal Fusion (FT-LMF). FT-LMF correlates the features of individual time steps between multiple modalities, while it involves multiplications of high-order tensors in its calculation. This paper further proposes Dual Low-Rank Multimodal Fusion (Dual-LMF) to reduce the computational complexity of FT-LMF through low-rank tensor approximation along dual dimensions of input features. Dual-LMF is conceptually simple and practically effective and efficient. Empirical studies on benchmark multimodal analysis tasks show that our proposed methods outperform the state-of-the-art tensor-based fusion methods with a similar computational complexity.

This paper addresses the challenging task of video captioning which aims to generate descriptions for video data. Recently, the attention-based encoder-decoder structures have been widely used in video captioning. In existing literature, the attention weights are often built from the information of an individual modality, while, the association relationships between multiple modalities are neglected. Motivated by this, we propose a video captioning model with High-Order Cross-Modal Attention (HOCA) where the attention weights are calculated based on the high-order correlation tensor to capture the frame-level cross-modal interaction of different modalities sufficiently. Furthermore, we novelly introduce Low-Rank HOCA which adopts tensor decomposition to reduce the extremely large space requirement of HOCA, leading to a practical and efficient implementation in real-world applications. Experimental results on two benchmark datasets, MSVD and MSR-VTT, show that Low-rank HOCA establishes a new state-of-the-art.