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The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)In this paper, we tackle the problem of convolutional neural network design. Instead of focusing on the design of the overall architecture, we investigate a design space that is usually overlooked, i.e. adjusting the channel configurations of predefined networks. We find that this adjustment can be achieved by shrinking widened baseline networks and leads to superior performance. Based on that, we articulate the "heterogeneity hypothesis": ...Conference Paper -
The Ninth Visual Object Tracking VOT2021 Challenge Results
(2021)2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, ...Conference Paper -
Generalized Real-World Super-Resolution through Adversarial Robustness
(2021)2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)Real-world Super-Resolution (SR) has been traditionally tackled by first learning a specific degradation model that resembles the noise and corruption artifacts in low-resolution imagery. Thus, current methods lack generalization and lose their accuracy when tested on unseen types of corruption. In contrast to the traditional proposal, we present Robust Super-Resolution (RSR), a method tha' leverages the generalization capability of ...Conference Paper -
Shadow removal with paired and unpaired learning
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photorealistic restoration of the image contents. Decades of research produced a multitude of hand-crafted restoration techniques and, more recently, learned solutions from shadowed and shadow-free training image pairs. In this work, we propose a single image shadow removal solution ...Conference Paper -
SMILE: Semantically-guided Multi-attribute Image and Layout Editing
(2021)2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)Attribute image manipulation has been a very active topic since the introduction of Generative Adversarial Networks (GANs). Exploring the disentangled attribute space within a transformation is a very challenging task due to the multiple and mutually-inclusive nature of the facial images, where different labels (eyeglasses, hats, hair, identity, etc.) can co-exist at the same time. Several works address this issue either by exploiting the ...Conference Paper -
Zero-Pair Image to Image Translation using Domain Conditional Normalization
(2021)2021 IEEE Winter Conference on Applications of Computer Vision (WACV)In this paper, we propose an approach based on domain conditional normalization (DCN) for zero-pair image-to-image translation, i.e., translating between two domains which have no paired training data available but each have paired training data with a third domain. We employ a single generator which has an encoder-decoder structure and analyze different implementations of domain conditional normalization to obtain the desired target ...Conference Paper -
Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Segmenting objects in videos is a fundamental computer vision task. The current deep learning based paradigm offers a powerful, but data-hungry solution. However, current datasets are limited by the cost and human effort of annotating object masks in videos. This effectively limits the performance and generalization capabilities of existing video segmentation methods. To address this issue, we explore weaker form of bounding box annotations.We ...Conference Paper -
Towards Efficient Graph Convolutional Networks for Point Cloud Handling
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)We aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds. The basic graph convolution that is composed of a K-nearest neighbor (KNN) search and a multilayer perceptron (MLP) is examined. By mathematically analyzing the operations there, two findings to improve the efficiency of GCNs are obtained. (1) The local geometric structure information of 3D representations propagates ...Conference Paper -
Conditional Probability Models for Deep Image Compression
(2018)2018 IEEE/CVF Conference on Computer Vision and Pattern RecognitionDeep Neural Networks trained as image auto-encoders have recently emerged as a promising direction for advancing the state-of-the-art in image compression. The key challenge in learning such networks is twofold: To deal with quantization, and to control the trade-off between reconstruction error (distortion) and entropy (rate) of the latent image representation. In this paper, we focus on the latter challenge and propose a new technique ...Conference Paper -
Practical full resolution learned lossless image compression
(2019)Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)Conference Paper