One-Shot Object Localization Using Learnt Visual Cues via Siamese Networks

Feature extraction with regularized siamese networks for outlier detection: application to lesion screening in medical imaging

Computer aided diagnosis (CAD) systems are designed to assist clinicians in various tasks, including highlighting abnormal regions in a medical image. A common approach consists in training a voxel-level binary classifier on a set of feature vectors extracted from normal and pathological areas in patients' scans. However, many pathologies (such as epilepsy) are characterized by lesions that may be located anywhere in the brain, have various shapes, sizes and texture. An adequate representation of such a heterogeneity requires a significant amount of annotated data which is a major issue in the…

Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues

We present a learning-based method for detecting real and fake deepfake multimedia content. To maximize information for learning, we extract and analyze the similarity between the two audio and visual modalities from within the same video. Additionally, we extract and compare affective cues corresponding to perceived emotion from the two modalities within a video to infer whether the input video is "real" or "fake". We propose a deep learning network, inspired by the Siamese network architecture and the triplet loss. To validate our model, we report the AUC metric on two large-scale deepfake d…

Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images

Visual cues of enforcing bilaterally symmetric anatomies as normal findings are widely used in clinical practice to disambiguate subtle abnormalities from medical images. So far, inadequate research attention has been received on effectively emulating this practice in CAD methods. In this work, we exploit semantic anatomical symmetry or asymmetry analysis in a complex CAD scenario, i.e., anterior pelvic fracture detection in trauma PXRs, where semantically pathological (refer to as fracture) and non-pathological (e.g., pose) asymmetries both occur. Visually subtle yet pathologically critical f…

T-SiamTPN：用于鲁棒且高效无人机跟踪的时序孪生Transformer金字塔网络

T-SiamTPN: Temporal Siamese Transformer Pyramid Networks for Robust and Efficient UAV Tracking

本文提出T-SiamTPN，通过引入时序特征融合和注意力交互扩展SiamTPN架构，解决了无人机跟踪中的时序依赖和非线性外观变化问题，在Jetson Nano上实现7.1 FPS实时运行，成功率和精度分别提升13.7%和14.7%。

Unifying Training and Inference for Panoptic Segmentation

We present an end-to-end network to bridge the gap between training and inference pipeline for panoptic segmentation, a task that seeks to partition an image into semantic regions for "stuff" and object instances for "things". In contrast to recent works, our network exploits a parametrised, yet lightweight panoptic segmentation submodule, powered by an end-to-end learnt dense instance affinity, to capture the probability that any pair of pixels belong to the same instance. This panoptic submodule gives rise to a novel propagation mechanism for panoptic logits and enables the network to output…

双流胜过单流：用于高效视觉跟踪的非对称Siamese网络

Two-stream Beats One-stream: Asymmetric Siamese Network for Efficient Visual Tracking

本文提出非对称Siamese跟踪器AsymTrack，通过解耦模板与搜索流、单向调制机制及对象感知增强模块，在多平台实现更优速度精度权衡。