Conventional deep learning based methods for 3D object detection require a large amount of 3D bounding box annotations for training, which is expensive to obtain in practice. Sparsely annotated object detection, which can largely reduce the annotations, is very challenging since the missing-annotated instances would be regarded as the background during training. In this paper, we propose a sparsely-supervised 3D object detection method, named SS3D. Aiming to eliminate the negative supervision caused by the missing annotations, we design a missing-annotated instance mining module with strict filtering strategies to mine positive instances. In the meantime, we design a reliable background mining module and a point cloud filling data augmentation strategy to generate the confident data for iteratively learning with reliable supervision. The proposed SS3D is a general framework that can be used to learn any modern 3D object detector. Extensive experiments on the KITTI dataset reveal that on different 3D detectors, the proposed SS3D framework with only 20% annotations required can achieve onpar performance comparing to fully-supervised methods. Comparing with the state-of-the-art semi-supervised 3D objection detection on KITTI, our SS3D improves the benchmarks by significant margins under the same annotation workload. Moreover, our SS3D also out-performs the state-of-the-art weakly-supervised method by remarkable margins, highlighting its effectiveness.

In real-world crowd counting applications, the crowd densities vary greatly in spatial and temporal domains. A detection based counting method will estimate crowds accurately in low density scenes, while its reliability in congested areas is downgraded. A regression based approach, on the other hand, captures the general density information in crowded regions. Without knowing the location of each person, it tends to overestimate the count in low density areas. Thus, exclusively using either one of them is not sufficient to handle all kinds of scenes with varying densities. To address this issue, a novel end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density Estimation Network) is proposed. It can adaptively decide the appropriate counting mode for different locations on the image based on its real density conditions. DecideNet starts with estimating the crowd density by generating detection and regression based density maps separately. To capture inevitable variation in densities, it incorporates an attention module, meant to adaptively assess the reliability of the two types of estimations. The final crowd counts are obtained with the guidance of the attention module to adopt suitable estimations from the two kinds of density maps. Experimental results show that our method achieves state-of-the-art performance on three challenging crowd counting datasets.

Data association, which could be categorized into offline approaches and the online counterparts, is a crucial part of a multi-object tracker in the tracking-by-detection framework. On the one hand, classical offline data association methods exploit all the video data and have high computation cost, which makes them unscalable to long-term offline video data. On the other hand, online approaches have much lower computation cost, but they suffer from ID-switches and tracklet drifting problem when directly applied to offline data as they are only aware of “past” observations. In this paper, we propose a mixed style tracker, which is not only as efficient as the online tracker but also aware of “future” observations in offline setting. We start from a Markov Decision Process (MDP) online tracker and design a parallelized apprenticeship learning algorithm to learn both the reward function and transition policy in MDP. By proposing a rewind to track strategy to generate backward tracklets, future detections in offline data are efficiently utilized to obtain a more stable similarity measurement for association. Experiment results show that our approach achieves the state-of-the-art performance on challenging datasets.

Human's cognition system prompts that context information provides potentially powerful clue while recognizing objects. However, for fine-grained image classification, the contribution of context may vary over different images, and sometimes the context even confuses the classification result. To alleviate this problem, in our work, we develop a novel approach, two-stream contextualized Convolutional Neural Network, which provides a simple but efficient context-content joint classification model under deep learning framework. The network merely requires the raw image and a coarse segmentation as input to extract both content and context features without need of human interaction. Moreover, our network adopts a weighted fusion scheme to combine the content and the context classifiers, while a subnetwork is introduced to adaptively determine the weight for each image. According to our experiments on public datasets, our approach achieves considerable high recognition accuracy without any tedious human's involvements, as compared with the state-of-the-art approaches.