7 accepted papers
Generating anomalies is a crucial method to enhance detection and classification performance by expanding anomalous data repository. However, existing anomaly generation methods overlook the intrinsic entanglement between diverse anomaly types and product structures, leading to semantic ambiguity. W
Affective Forecasting is an psychology task that involves predicting an individual
Anomaly detection is critical in industrial manufacturing for ensuring product quality and improving efficiency in automated processes. The scarcity of anomalous samples limits traditional detection methods, making anomaly generation essential for expanding the data repository. However, recent gener
The current advancements in Dynamic Facial Expression Recognition (DFER) methods mainly focus on better capturing the spatial and temporal features of facial expressions. However, DFER datasets contain a substantial amount of noisy samples, and few have addressed the issue of handling this noise. We…
The rapid spread of multimodal misinformation on social media has raised growing concerns, while research on video misinformation detection remains limited due to the lack of large-scale, diverse datasets. Existing methods often overfit to rigid templates and lack deep reasoning over deceptive conte…
Dynamic Facial Expression Recognition (DFER) is crucial for affective computing but often overlooks the impact of scene context. We have identified a significant issue in current DFER tasks: human annotators typically integrate emotions from various angles, including environmental cues and body lang…
The Janus Problem is a common issue in SDS-based text-to-3D methods. Due to view encoding approach and 2D diffusion prior guidance, the 3D representation model tends to learn content with higher certainty from each perspective, leading to view inconsistency. In this work, we first model and analyze…