Luc Van Gool

TL;DR: This paper proposes an end-to-end trainable unfolding network which leverages both learningbased methods and model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning- based methods.

TL;DR: This work proposes a probabilistic regression formulation and applies it to tracking, which is capable of modeling label noise stemming from inaccurate annotations and ambiguities in the task and substantially improves the performance.

TL;DR: This paper proposes a new technique to navigate the rate-distortion trade-off for an image compression auto-encoder by using a context model: A 3D-CNN which learns a conditional probability model of the latent distribution of the auto- Encoder.

TL;DR: This paper builds on the recent Affinity Propagation Clustering algorithm, which passes messages between data points to identify cluster exemplars and shows that it provides a promising solution to the shortcomings of the greedy NMS.

TL;DR: In this paper, a novel object recognition approach based on affine invariant regions is presented, which actively counters the problems related to the limited repeatability of the region detectors, and the difficulty of matching, in the presence of large amounts of background clutter and particularly challenging viewing conditions.