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Found 9 papers, 1 papers with code
RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texture
The proposed framework consists of two significant components: Geometry Guided Diffusion and Mesh Optimization.
Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach
Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming increasingly useful.
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection
The deep regionlets framework consists of a region selection network and a deep regionlet learning module.
A Fast and Accurate System for Face Detection, Identification, and Verification
We provide evaluation results of the proposed face detector on challenging unconstrained face detection datasets.
Learning Simple Thresholded Features with Sparse Support Recovery
The thresholded feature has recently emerged as an extremely efficient, yet rough empirical approximation, of the time-consuming sparse coding inference process.
Cross-Domain Visual Recognition via Domain Adaptive Dictionary Learning
These intermediate domains form a smooth path and bridge the gap between the source and target domains.
Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition
We show that integrating this simple step in the training pipeline significantly improves the performance of face verification and recognition systems.
Deep Regionlets for Object Detection
The deep regionlets framework consists of a region selection network and a deep regionlet learning module.
Deep Heterogeneous Feature Fusion for Template-Based Face Recognition
Thus, in this work, we propose a deep heterogeneous feature fusion network to exploit the complementary information present in features generated by different deep convolutional neural networks (DCNNs) for template-based face recognition, where a template refers to a set of still face images or video frames from different sources which introduces more blur, pose, illumination and other variations than traditional face datasets.
