Human-Centered Scene Understanding via Crowd Counting
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Scene depth estimation is gaining in importance as more and more AR/VR and robot vision applications are developed. Conventional depth-from-defocus techniques can passively provide depth maps from a single image. This is especially advantageous for moving ...
Estimating the 3D poses of rigid and articulated bodies is one of the fundamental problems of Computer Vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. Despite the ever-growin ...
Imaging devices have become ubiquitous in modern life, and many of us capture an increasing number of images every day. When we choose to share or store some of these images, our primary selection criterion is to choose the most visually pleasing ones. Yet ...
Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...
Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...
We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth imag ...
State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data. However, these methods require the training of one specific model for every ...
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density in the image plane. While useful for this purpose, this image- plane density has no immediate physical meaning because it is subject to perspecti ...
We present a deep architecture and learning framework for establishing correspondences across cross-spectral visible and infrared images in an unpaired setting. To overcome the unpaired cross-spectral data problem, we design the unified image translation a ...
We address the question of what visual cues, including scene objects and demographic attributes, contribute to the automatic inference of perceived ambiance in social media venues. We first use a stateof- art, deep scene semantic parsing method and a face ...
