Joint Optical Flow and Temporally Consistent Semantic Segmentation

The importance and demands of visual scene understanding have been steadily increasing along with the active development of autonomous systems. Consequently, there has been a large amount of research dedicated to semantic segmentation and dense motion estimation. In this paper, we propose a method for jointly estimating optical flow and temporally consistent semantic segmentation, which closely connects these two problem domains and leverages each other. Semantic segmentation provides information on plausible physical motion to its associated pixels, and accurate pixel-level temporal correspondences enhance the accuracy of semantic segmentation in the temporal domain. We demonstrate the benefits of our approach on the KITTI benchmark, where we observe performance gains for flow and segmentation. We achieve state-of-the-art optical flow results, and outperform all published algorithms by a large margin on challenging, but crucial dynamic objects.Comment: 14 pages, Accepted for CVRSUAD workshop at ECCV 201

Deep Learning for Video Object Segmentation:A Review

As one of the fundamental problems in the field of video understanding, video object segmentation aims at segmenting objects of interest throughout the given video sequence. Recently, with the advancements of deep learning techniques, deep neural networks have shown outstanding performance improvements in many computer vision applications, with video object segmentation being one of the most advocated and intensively investigated. In this paper, we present a systematic review of the deep learning-based video segmentation literature, highlighting the pros and cons of each category of approaches. Concretely, we start by introducing the definition, background concepts and basic ideas of algorithms in this field. Subsequently, we summarise the datasets for training and testing a video object segmentation algorithm, as well as common challenges and evaluation metrics. Next, previous works are grouped and reviewed based on how they extract and use spatial and temporal features, where their architectures, contributions and the differences among each other are elaborated. At last, the quantitative and qualitative results of several representative methods on a dataset with many remaining challenges are provided and analysed, followed by further discussions on future research directions. This article is expected to serve as a tutorial and source of reference for learners intended to quickly grasp the current progress in this research area and practitioners interested in applying the video object segmentation methods to their problems. A public website is built to collect and track the related works in this field: https://github.com/gaomingqi/VOS-Review

Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery

In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, to improve the quality of the segmentation completion, we present two coupled discriminators and introduce an auxiliary 3D model pool for sampling authentic silhouettes as adversarial samples. In addition, we propose a two-path structure with a shared network to enhance the appearance recovery capability. By iteratively performing the segmentation completion and the appearance recovery, the results will be progressively refined. To evaluate our method, we present a dataset, the Occluded Vehicle dataset, containing synthetic and real-world occluded vehicle images. We conduct comparison experiments on this dataset and demonstrate that our model outperforms the state-of-the-art in tasks of recovering segmentation mask and appearance for occluded vehicles. Moreover, we also demonstrate that our appearance recovery approach can benefit the occluded vehicle tracking in real-world videos

Amodal Instance Segmentation and Multi-Object Tracking with Deep Pixel Embedding

This thesis extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial extent and depth ordering of each instance. Results demonstrate that the network can accurately estimate complete masks in the presence of occlusion and outperform leading top-down bounding-box approaches. The model is further extended to produce consistent pixel-level embeddings across two consecutive image frames from a video to simultaneously perform amodal instance segmentation and multi-object tracking. No post-processing trackers or Hungarian Algorithm is needed to perform multi-object tracking. The advantages and disadvantages of such a bounding-box-free approach are studied thoroughly. Experiments show that the proposed method outperforms the state-of-the-art bounding-box based approach on tracking animated moving objects. Advisor: Eric T. Psota and Lance C. Pére

Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work

Inspired by the fact that human brains can emphasize discriminative parts of the input and suppress irrelevant ones, substantial local mechanisms have been designed to boost the development of computer vision. They can not only focus on target parts to learn discriminative local representations, but also process information selectively to improve the efficiency. In terms of application scenarios and paradigms, local mechanisms have different characteristics. In this survey, we provide a systematic review of local mechanisms for various computer vision tasks and approaches, including fine-grained visual recognition, person re-identification, few-/zero-shot learning, multi-modal learning, self-supervised learning, Vision Transformers, and so on. Categorization of local mechanisms in each field is summarized. Then, advantages and disadvantages for every category are analyzed deeply, leaving room for exploration. Finally, future research directions about local mechanisms have also been discussed that may benefit future works. To the best our knowledge, this is the first survey about local mechanisms on computer vision. We hope that this survey can shed light on future research in the computer vision field