The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans’ ability of learning to learn and intelligently transfers the learned generic object knowledge from the common heavy-tailed to the novel long-tailed object classes. Especially, the research in this emerging field has been flourishing in recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [ 58 , 59 , 74 , 78 ] that systematically study and compare them as the groups of fine-tuning/transfer learning and meta-learning methods. In contrast, we review the existing FSOD algorithms from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Specifically, we give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols. The taxonomy is then proposed that groups FSOD methods into three types. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented.

Vision-and-language navigation (VLN), a frontier study aiming to pave the way for general-purpose robots, has been a hot topic in the computer vision and natural language processing community. The VLN task requires an agent to navigate to a goal location following natural language instructions in unfamiliar environments.

Given a video demonstration, can we imitate the action contained in this video? In this paper, we introduce a novel task, dubbed mesh-based action imitation. The goal of this task is to enable an arbitrary target human mesh to perform the same action shown on the video demonstration. To achieve this, a novel Mesh-based Video Action Imitation (M-VAI) method is proposed by us. M-VAI first learns to reconstruct the meshes from the given source image frames, then the initial recovered mesh sequence is fed into mesh2mesh, a mesh sequence smooth module proposed by us, to improve the temporal consistency. Finally, we imitate the actions by transferring the pose from the constructed human body to our target identity mesh. High-quality and detailed human body meshes can be generated by using our M-VAI. Extensive experiments demonstrate the feasibility of our task and the effectiveness of our proposed method.

Non-Maximum Suppression (NMS) is essential for object detection and affects the evaluation results by incorporating False Positives (FP) and False Negatives (FN), especially in crowd occlusion scenes. In this paper, we raise the problem of weak connection between the training targets and the evaluation metrics caused by NMS and propose a novel NMS-Loss making the NMS procedure can be trained end-to-end without any additional network parameters. Our NMS-Loss punishes two cases when FP is not suppressed and FN is wrongly eliminated by NMS. Specifically, we propose a pull loss to pull predictions with the same target close to each other, and a push loss to push predictions with different targets away from each other. Experimental results show that with the help of NMS-Loss, our detector, namely NMS-Ped, achieves impressive results with Miss Rate of 5.92% on Caltech dataset and 10.08%on CityPersons dataset, which are both better than state-of-the-art competitors.

Traditional image captioning models mainly rely on one encoder-decoder architecture to generate one natural sentence for a given image. Such an architecture mostly uses deep neural networks to extract the neural representations of the image while ignoring the information of abstractive concepts as well as their intertwined relationships conveyed in the image. To this end, to comprehensively characterize the image content and bridge the gap between neural representations and high-level abstractive concepts, we make the first attempt to investigate the ability of neural symbolic representation of the image for the image captioning task. We first parse and convert a given image to neural symbolic representation in the form of an attributed relational graph, with the nodes denoting the abstractive concepts and the branches indicating the relationships between connected nodes, respectively. By performing computations over the attributed relational graph, the neural symbolic representation evolves step by step, with the node and branch representations as well as their corresponding importance weights transiting step by step. Empirically, extensive experiments validate the effectiveness of the proposed method. It enables a more comprehensive understanding of the given image by integrating the neural representation and neural symbolic representation, with the state-of-the-art results being achieved on both the MSCOCO and Flickr30k datasets. Besides, the proposed neural symbolic representation is demonstrated to better generalize to other domains with significant performance improvements compared with existing methods on the cross domain image captioning task.

Object detection for automatic visual checkout in self-service vending machines is attracting significant attention in the retail industry. However, several critical challenges have not received enough attention. First, large-scale, high-quality retail image datasets are urgently demanded to train and evaluate the detection models. Second, the trained models should be able to cope with the frequently added new goods at low cost, while most cutting-edge models cannot. In this paper, we propose a new hierarchical large-scale object detection dataset, called Take Goods from Shelves (TGFS), containing 38K images of 24 fine-grained and 3 coarse classes. A preliminary method for solving the goods-adding problem, called Faster R-CNN Class-incremental Object Detector (FCIOD), is also described and evaluated. In addition, several popular methods are benchmarked on the TGFS dataset.

Given a photograph, the task of Visual Question Answering (VQA) requires joint image and language understanding to answer a question. It is challenging in effectively extracting the visual representation of images, and efficiently embedding the textual sentences of questions. To address these challenges, we propose a VQA model that utilizes the stacked self-attention for visual understanding, and the BERT-based question embedding model. Particularly, the stacked self-attention mechanism proposed enables the model to not only focus on a simple object but also the relations between objects. Furthermore, the BERT model is learned in an end-to-end manner to better embed the question sentences. Our model is validated on the well-known VQA v2.0 dataset, and achieves the state-of-the-art results.

Emotional content is a key ingredient in user-generated videos. However, due to the emotion sparsely expressed in the user-generated video, it is very difficult to analayze emotions in videos. In this paper, we propose a new architecture--Frame-Transformer Emotion Classification Network (FT-EC-net) to solve three highly correlated emotion analysis tasks: emotion recognition, emotion attribution and emotion-oriented summarization. We also contribute a new dataset for emotion attribution task by annotating the ground-truth labels of attribution segments. A comprehensive set of experiments on two datasets demonstrate the effectiveness of our framework.

Deep neural networks have significantly improved the performance of face recognition and facial attribute prediction, which however are still very challenging on the million scale dataset, i.e. MegaFace. In this paper, we for the first time, advocate a multi-task deep neural network for jointly learning face recognition and facial attribute prediction tasks. Extensive experimental evaluation clearly demonstrates the effectiveness of our architecture. Remarkably, on the largest face recognition benchmark -- MegaFace dataset, our networks can achieve the Rank-1 identication accuracy of 77.74% and face verication accuracy 79.24% TAR at 10-6 FAR, which are the best performance on the small protocol among all the publicly released methods.

Despite growing research interest, emotion understanding for user-generated videos remains a challenging problem. Major obstacles include the diversity and complexity of video content, as well as the sparsity of expressed emotions. For the first time, we systematically study large-scale video emotion recognition by transferring deep feature encodings. In addition to the traditional, supervised recognition, we study the problem of zero-shot emotion recognition, where emotions in the test set are unseen during training. To cope with this task, we utilize knowledge transferred from auxiliary image and text corpora. A novel auxiliary Image Transfer Encoding (ITE) process is proposed to efficiently encode and generate video representation. We also thoroughly investigate different configurations of convolutional neural networks. Comprehensive experiments on multiple datasets demonstrate the effectiveness of our framework.