9 accepted papers
In response to the security risks posed by the realistic propagation of manipulated media data, detecting and grounding multi-modal media manipulation has received attention as a challenging task. However, there is multi-modal contribution imbalance on current approach for cross-modal learning, whic…
The primary objective of Visible-Infrared Image Fusion (VIF) is to combine the rich texture and color information from visible light images with the comprehensive thermal radiation data provided by infrared images. However, most current fusion algorithms focus solely on spatial domain feature transf…
Quantization is an effective method for compressing DNNs with enormous parameters and improving inference efficiency especially on resource-constrained devices like mobile phones. However, it is difficult to quantize models to extremely low-bit while maintaining considerable accuracy compared to the…
Knowledge Distillation (KD) is a promising approach for unsupervised Anomaly Detection (AD). However, the student network's over-generalization often diminishes the crucial representation differences between teacher and student in anomalous regions, leading to detection failures. To address this pro…
Realistic deepfake audio has posed significant security risk. To address this threat, current countermeasures attempt to extract forgery traces from either waveform signals or corresponding spectrograms. However, features extracted from single modality are susceptible to non-spoof disturbances and t…
"Vision transformers (ViTs) have become the popular structures and outperformed convolutional neural networks (CNNs) on various vision tasks. However, such powerful transformers bring a huge computation burden, because of the exhausting token-to-token comparison. The previous works focus on dropping…
"We present a conceptually simple, flexible, and universal visual perception head for variant visual tasks, e.g., classification, object detection, instance segmentation and pose estimation, and different frameworks, such as one-stage or two-stage pipelines. Our approach effectively identifies an ob…
Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, CenterN…
Fully convolutional neural network (FCN) has been dominating the game of face detection task for a few years with its congenital capability of sliding-window-searching with shared kernels, which boiled down all the redundant calculation, and most recent state-of-the-art methods such as Faster-RCNN,…