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.

pdf/recent-few-shot-object-detection-algorithms-a-survey-with-3kz7dixx.pdf

Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison

Object detection as a subfield within computer vision has achieved remarkable progress, which aims to accurately identify and locate a specific object from images or videos. Such methods rely on large-scale labeled training samples for each object category to ensure accurate detection, but obtaining extensive annotated data is a labor-intensive and expensive process in many real-world scenarios. To tackle this challenge, researchers have explored few-shot object detection (FSOD) that combines few-shot learning and object detection techniques to rapidly adapt to novel objects with limited annotated samples. This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years and summarize the existing challenges and solutions. Specifically, we first introduce the background and definition of FSOD to emphasize potential value in advancing the field of computer vision. We then propose a novel FSOD taxonomy method and survey the plentifully remarkable FSOD algorithms based on this fact to report a comprehensive overview that facilitates a deeper understanding of the FSOD problem and the development of innovative solutions. Finally, we discuss the advantages and limitations of these algorithms to summarize the challenges, potential research direction, and development trend of object detection in the data scarcity scenario. 

Few-shot object detection: Research advances and challenges

Few-shot object detection (FSOD) enables the detector to recognize novel objects only using limited training samples, which could greatly alleviate model’s dependency on data. Most existing methods include two training stages, namely base training and fine-tuning. However, the unlabeled novel instances in the base set were untouched in previous works, which can be re-used to enhance the FSOD performance. Thus, a new instance mining model is proposed in this paper to excavate the novel samples from the base set. The detector is thus fine-tuned again by these additional free novel instances. Meanwhile, a novel pseudo-margin evaluation algorithm is designed to address the quality problem of pseudo-labels brought by those new novel instances. The experimental results on MS-COCO dataset show the effectiveness of the proposed model, which does not require any additional training samples or parameters. Our code is available at: https://github.com/liuweijie19980216/NimPme. 

Novel Instance Mining with Pseudo-Margin Evaluation for Few-Shot Object Detection

M-RRFS: A Memory-Based Robust Region Feature Synthesizer for Zero-Shot Object Detection

Food computing brings various perspectives to computer vision like vision-based food analysis for nutrition and health. As a fundamental task in food computing, food detection needs Zero-Shot Detection (ZSD) on novel unseen food objects to support real-world scenarios, such as intelligent kitchens and smart restaurants. Therefore, we first benchmark the task of Zero-Shot Food Detection (ZSFD) by introducing FOWA dataset with rich attribute annotations. Unlike ZSD, fine-grained problems in ZSFD like inter-class similarity make synthesized features inseparable. The complexity of food semantic attributes further makes it more difficult for current ZSD methods to distinguish various food categories. To address these problems, we propose a novel framework ZSFDet to tackle fine-grained problems by exploiting the interaction between complex attributes. Specifically, we model the correlation between food categories and attributes in ZSFDet by multi-source graphs to provide prior knowledge for distinguishing fine-grained features. Within ZSFDet, Knowledge-Enhanced Feature Synthesizer (KEFS) learns knowledge representation from multiple sources (e.g., ingredients correlation from knowledge graph) via the multi-source graph fusion. Conditioned on the fusion of semantic knowledge representation, the region feature diffusion model in KEFS can generate fine-grained features for training the effective zero-shot detector. Extensive evaluations demonstrate the superior performance of our method ZSFDet on FOWA and the widely-used food dataset UECFOOD-256, with significant improvements by 1.8% and 3.7% ZSD mAP compared with the strong baseline RRFS. Further experiments on PASCAL VOC and MS COCO prove that enhancement of the semantic knowledge can also improve the performance on general ZSD. Code and dataset are available at https://github.com/LanceZPF/KEFS.

Synthesizing Knowledge-Enhanced Features for Real-World Zero-Shot Food Detection

Zero-shot object detection aims to simultaneously identify and localize classes that were not presented during training. Many generative model-based methods have shown promising performance by synthesizing the visual features of unseen classes from semantic embeddings. However, these synthetic features are inevitably of varied quality, which may be far from the ground truth. It degrades the performance of trained unseen classifier. Instead of tweaking the generative model, a new idea of feature quality assessment is proposed to utilize both the good and bad features to optimize the classifier in the right direction. Moreover, contrastive learning is also introduced to enhance the feature uniqueness between unseen and seen classes, which helps the feature assessment implicitly. To demonstrate the effectiveness of the proposed algorithm, comprehensive experiments are conducted on the MS COCO dataset and PASCAL VOC dataset, the state-of-the-art performance is achieved. Our code is available at: https://github.com/Dai1029/SFA-ZSD. 

Synthetic Feature Assessment for Zero-Shot Object Detection

Current approaches for spatiotemporal action recognition have achieved impressive progress, especially in temporal information processing. Meanwhile, the power of spatial information may be underestimated. Thus, a non-uniform key frame selector is proposed to pick the most representative frames according to the relationship between frames along the temporal dimension. Specifically, the reweight high-level frame features are used to generate an importance score sequence, while the key frames, in each temporal section, are selected based on the above scores. Such selected frames have richer semantic information, which has positive impact on the network training. The proposed model is evaluated on two action recognition, namely datasets HMDB51 and UCF101, and promising accuracy improvement is achieved.

Action Recognition with Non-Uniform Key Frame Selector

Expensive bounding-box annotations have limited the development of object detection task. Thus, it is necessary to focus on more challenging task of few-shot object detection. It requires the detector to recognize objects of novel classes with only a few training samples. Nowadays, many existing popular methods based on meta-learning have achieved promising performance, such as Meta R-CNN series. However, only a single category of support data is used as the attention to guide the detecting of query images each time. Their relevance to each other remains unexploited. Moreover, a lot of recent works treat the support data and query images as independent branch without considering the relationship between them. To address this issue, we propose a dynamic relevance learning model, which utilizes the relationship between all support images and Region of Interest (RoI) on the query images to construct a dynamic graph convolutional network (GCN). By adjusting the prediction distribution of the base detector using the output of this GCN, the proposed model can guide the detector to improve the class representation implicitly. Comprehensive experiments have been conducted on Pascal VOC and MS-COCO dataset. The proposed model achieves the best overall performance, which shows its effectiveness of learning more generalized features. Our code is available at this https URL.

Dynamic Relevance Learning for Few-Shot Object Detection.

Despite advancements in user-guided video segmentation, extracting complex objects consistently for highly complex scenes is still a labor-intensive task, especially for production. It is not uncommon that a majority of frames need to be annotated. We introduce a novel semi-supervised video object segmentation (SSVOS) model, XMem++, that improves existing memory-based models, with a permanent memory module. Most existing methods focus on single frame annotations, while our approach can effectively handle multiple user-selected frames with varying appearances of the same object or region. Our method can extract highly consistent results while keeping the required number of frame annotations low. We further introduce an iterative and attention-based frame suggestion mechanism, which computes the next best frame for annotation. Our method is real-time and does not require retraining after each user input. We also introduce a new dataset, PUMaVOS, which covers new challenging use cases not found in previous benchmarks. We demonstrate SOTA performance on challenging (partial and multi-class) segmentation scenarios as well as long videos, while ensuring significantly fewer frame annotations than any existing method. Project page: https://max810.github.io/xmem2-project-page/

pdf/xmem-production-level-video-segmentation-from-few-annotated-4cskz2nib2.pdf

XMem++: Production-level Video Segmentation From Few Annotated Frames

How to properly align the extracted visual features with certain semantic embeddings of unseen objects is crucial to the problem of Zero-Shot Object Detection (ZSD). To give a better guess of those unseen visual features, a partitioned contrast strategy is proposed in this paper to train the visual and attribute feature alignment networks. To be specific, four types of contrast are considered, including the visual-to-visual, visual-to-attribute, attribute-to-visual and attribute-to-attribute contrasts. Combining with two cross-batch memory banks of the visual features and unseen attribute features, it is effective to adjust the alignment rules for unseen visual features. Experimental results on the MS-COCO dataset show the superiority of the proposed model. Our code is available at: https://github.com/lihh1023/PCFA-ZSD. 

Zero-Shot Object Detection with Partitioned Contrastive Feature Alignment

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