The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website ( http://votchallenge.net ).

In this paper, for the first time, we investigate the problem of generating 3D shapes from professional 2D sketches via deep learning. We target sketches done by professional artists, as these sketches are likely to contain more details than the ones produced by novices, and thus the reconstruction from such sketches poses a higher demand on the level of detail in the reconstructed models. This is importantly different to previous work, where the training and testing was conducted on either synthetic sketches or sketches done by novices. Novices sketches often depict shapes that are physically unrealistic, while models trained with synthetic sketches could not cope with the level of abstraction and style found in real sketches. To address this problem, we collected the first large-scale dataset of professional sketches, where each sketch is paired with a reference 3D shape, with a total of 1,500 professional sketches collected across 500 3D shapes. The dataset is available at http://sketchx.ai/downloads/. We introduce two bespoke designs within a deep adversarial network to tackle the imprecision of human sketches and the unique figure/ground ambiguity problem inherent to sketch-based reconstruction. We show that existing 3D shapes generation methods designed for images fail to be naively applied to our problem, and demonstrate the effectiveness of our method both qualitatively and quantitatively.

In recent years, retinal vessel segmentation technology has become an important component for disease screening and diagnosing in clinical medicine. However, retinal vessel segmentation is a challenging task due to complex distribution of blood vessels, relatively low contrast between target and background, and potential presence of illumination and pathologies. In this paper, we propose an automatic retinal vessel segmentation network using deep supervision and smoothness regularization, which integrates holistically-nested edge detector (HED) and global smoothness regularization from conditional random ?elds (CRFs). It is an end-to-end and pixel-to-pixel deep convolutional network, can perform better results than HED-based methods and the methods where CRF inference is applied as a post-processing method. With co-constraints between pixels, the proposed DSSRN obtains better results. Finally, we show that our proposed method obtains a sate-of-the-art vessel segmentation performance on all three benchmarks, DRIVE, STARE and CHASE DB1.

Human free-hand sketches provide the useful data for studying human perceptual grouping, where the grouping principles such as the Gestalt laws of grouping are naturally in play during both the perception and sketching stages. In this paper, we make the first attempt to develop a universal sketch perceptual grouper. That is, a grouper that can be applied to sketches of any category created with any drawing style and ability, to group constituent strokes/segments into semantically meaningful object parts. The first obstacle to achieving this goal is the lack of large-scale datasets with grouping annotation. To overcome this, we contribute the largest sketch perceptual grouping dataset to date, consisting of 20 000 unique sketches evenly distributed over 25 object categories. Furthermore, we propose a novel deep perceptual grouping model learned with both generative and discriminative losses. The generative loss improves the generalization ability of the model, while the discriminative loss guarantees both local and global grouping consistency. Extensive experiments demonstrate that the proposed grouper significantly outperforms the state-of-the-art competitors. In addition, we show that our grouper is useful for a number of sketch analysis tasks, including sketch semantic segmentation, synthesis, and fine-grained sketch-based image retrieval.

Deep networks have been used for semantic segmentation tasks on scenes of outdoor environments with increasing popularity. However, the majority of existing work centers on daytime scenes with favorable illumination and weather conditions, and relies on supervision with pixel-level annotations. This paper seeks to address the problem of semantic segmentation for rainy, night-time scenes without using pixel-level annotations. We introduce a near scene semantic approach that uses images of daytime scenes as a bridge for transferring knowledge from pre-trained segmentation models to rainy night images. Specifically, we first present near scene oriented Representation Adaptation (RA) to reduce the domain shift on the representation level. Next, we adapt the segmentation model from the daytime scenario, under varying weather conditions, to the rainy night scenario by using near scene oriented Segmentation Space Adaptation (SSA). Consequently, this further reduces the impact of the domain shift on the segmentation space level. For evaluation, we created a new dataset containing 7000 distinct daytime-night-time image pairs of near scenes obtained by a webcam, and 5266 daytime-rainy night image pairs collected by a car-mounted camera. In addition, we carefully annotated 226 rainy night images with classes defined in Cityscapes. The experimental results clearly demonstrate the advantage of the proposed algorithm.