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GANmut: Learning Interpretable Conditional Space for Gamut of Emotions
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Humans can communicate emotions through a plethora of facial expressions, each with its own intensity, nuances and ambiguities. The generation of such variety by means of conditional GANs is limited to the expressions encoded in the used label system. These limitations are caused either due to burdensome labelling demand or the confounded label space. On the other hand, learning from inexpensive and intuitive basic categorical emotion ...Conference Paper -
3D CNNs with Adaptive Temporal Feature Resolutions
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)While state-of-the-art 3D Convolutional Neural Networks (CNN) achieve very good results on action recognition datasets, they are computationally very expensive and require many GFLOPs. While the GFLOPs of a 3D CNN can be decreased by reducing the temporal feature resolution within the network, there is no setting that is optimal for all input clips. In this work, we therefore introduce a differentiable Similarity Guided Sampling (SGS) ...Conference Paper -
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 -
VisDrone-MOT2021: The Vision Meets Drone Multiple Object Tracking Challenge Results
(2021)2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)Vision Meets Drone: Multiple Object Tracking (VisDrone-MOT2021) challenge - the forth annual activity organized by the VisDrone team - focuses on benchmarking UAV MOT algorithms in realistic challenging environments. It is held in conjunction with ICCV 2021. VisDrone-MOT2021 contains 96 video sequences in total, including 56 sequences (similar to 24K frames) for training, 7 sequences (similar to 3K frames) for validation and 33 sequences ...Conference Paper -
VisDrone-DET2021: The Vision Meets Drone Object detection Challenge Results
(2021)2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)Object detection on the drone faces a great diversity of challenges such as small object inference, background clutter and wide viewpoint. In contrast to traditional detection problem in computer vision, object detection in bird like angle can not be transplanted directly from common-in-use methods due to special object texture in sky's view. However, due to the lack of a comprehensive data set, the number of algorithms that focus on ...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 -
VisDrone-CC2021: The Vision Meets Drone Crowd Counting Challenge Results
(2021)2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)Crowding counting research evolves quickly by the leverage of development in deep learning. Many researchers put their efforts into crowd counting tasks and have achieved many significant improvements. However, current datasets still barely satisfy this evolution and high quality evaluation data is urgent. Motivated by high quality and quantity study in crowding counting, we collect a drone-captured dataset formed by 5,468 images(images ...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