9 citations · 12 across the 12 of their papers we have counts for
12 papers
Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory
Yuxuan Lin, Hanjing Yan, Xuan Tong +6
Few-shot multimodal industrial anomaly detection is a critical yet underexplored task, offering the ability to quickly adapt to complex industrial scenarios. In few-shot settings,…
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
Xuan Tong, Yang Chang, Qing Zhao +9
Anomaly detection is critical in industrial manufacturing for ensuring product quality and improving efficiency in automated processes. The scarcity of anomalous samples limits tra…
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Image Anomaly Detection
Yuxuan Lin, Yang Chang, Xuan Tong +8
In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the au…
All rivers run into the sea: Unified Modality Brain-like Emotional Central Mechanism
Xinji Mai, Junxiong Lin, Haoran Wang +10
In the field of affective computing, fully leveraging information from a variety of sensory modalities is essential for the comprehensive understanding and processing of human emot…
Hi-EF: Benchmarking Emotion Forecasting in Human-interaction
Haoran Wang, Xinji Mai, Zeng Tao +6
Affective Forecasting is an psychology task that involves predicting an individual's future emotional responses, often hampered by reliance on external factors leading to inaccurac…
From Efficient Multimodal Models to World Models: A Survey
Xinji Mai, Zeng Tao, Junxiong Lin +5
Multimodal Large Models (MLMs) are becoming a significant research focus, combining powerful large language models with multimodal learning to perform complex tasks across differen…
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution
Junxiong Lin, Zeng Tao, Xuan Tong +10
The problem of blind image super-resolution aims to recover high-resolution (HR) images from low-resolution (LR) images with unknown degradation modes. Most existing methods model…
D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition
Haoran Wang, Xinji Mai, Zeng Tao +10
The contemporary state-of-the-art of Dynamic Facial Expression Recognition (DFER) technology facilitates remarkable progress by deriving emotional mappings of facial expressions fr…
OUS: Scene-Guided Dynamic Facial Expression Recognition
Xinji Mai, Haoran Wang, Zeng Tao +8
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 D…
Align-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP
Zeng Tao, Yan Wang, Junxiong Lin +9
The performance of CLIP in dynamic facial expression recognition (DFER) task doesn't yield exceptional results as observed in other CLIP-based classification tasks. While CLIP's pr…
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution
Junxiong Lin, Yan Wang, Zeng Tao +10
Pre-trained diffusion models utilized for image generation encapsulate a substantial reservoir of a priori knowledge pertaining to intricate textures. Harnessing the potential of l…
A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis
Nailei Hei, Qianyu Guo, Zihao Wang +3
Well-designed prompts have demonstrated the potential to guide text-to-image models in generating amazing images. Although existing prompt engineering methods can provide high-leve…