Logs of large-scale cloud systems record diverse system events, ranging from routine statuses to critical errors. As the fundamental step of automated log analysis, log parsing is to transform unstructured logs into structured data for easier management and analysis. However, existing syntax-based and deep learning-based parsers struggle with complex real-world logs. Recent parsers based on large language models (LLMs) achieve higher accuracy, but they typically rely on online APIs (e.g., ChatGPT), raising privacy concerns and suffering from network latency. Moreover, with the rise of artificial intelligence for IT operations (AIOps), traditional parsers that focus on syntax-level templates fail to capture the semantics of dynamic log parameters, limiting their usefulness for downstream tasks. These challenges highlight the need for semantic log parsing that goes beyond template extraction to understand parameter semantics.This paper presents SemanticLog, an effective and efficient semantic log parser powered by open-source LLMs. SemanticLog adapts the structure of LLMs to the log parsing task, leveraging their rich knowledge while safeguarding log data privacy. It first extracts informative feature representations from log data, then refines them through fine-grained semantic perception to enable accurate template and parameter extraction together with semantic category prediction. To boost scalability, SemanticLog introduces the EffiParsing tree for faster inference on large-scale logs. Extensive experiments on the LogHub-2.0 dataset show that SemanticLog significantly outperforms the state-of-the-art log parsers in terms of accuracy. Moreover, it also surpasses existing LLM-based parsers in efficiency while showcasing advanced semantic parsing capability. Notably, SemanticLog employs much smaller open-source LLMs compared to existing LLM-based parsers (mainly based on ChatGPT), while maintaining better capability of log data privacy protection.

Real-world data often exhibits a long-tailed distribution, which fuels growing research interest in long-tailed object detection (LTOD) from both academia and industry. However, existing methods primarily focus on the head-tail imbalance issue in LTOD, neglecting the more critical issue of tail class data scarcity. This leads to overfitting on tail classes and poor performance as a whole. This paper addresses both tail class data scarcity and head-tail imbalance by proposing a Dual-Model framework named DuMo, which tackles the two challenges by two separate models, and then merges them by knowledge transfer to ensure superior performance across the entire class space. Specifically, DuMo consists of a tail class expert model (EM) and a base model (BM). EM is dedicated to detecting scarce tail classes, and enhanced by a tail class feature augmentation (TCFA) module that uses CLIP-generated semantic embeddings to diversify the tail class features. On the other hand, BM handles head-tail imbalance by employing a class-specific margin loss (CSML), which moves the decision boundary toward the underrepresented tail classes, thus reducing the suppression from head classes. Finally, a knowledge transfer (KT) module transfers expertise from EM to BM, which further enhances BM’s performance on tail classes and ensures robust detection across all classes. Extensive experiments on LVIS v1.0 and LVIS-X validate the effectiveness of the proposed method DuMo.

Zero-shot object detection (ZSD) aims to leverage semantic descriptions to localize and recognize objects of both seen and unseen classes. Existing ZSD works are mainly coarse-grained object detection, where the classes are visually quite different, thus are relatively easy to distinguish. However, in real life we often have to face fine-grained object detection scenarios, where the classes are too similar to be easily distinguished. For example, detecting different kinds of birds, fishes, and flowers. In this paper, we propose and solve a new problem called Fine-Grained Zero-Shot Object Detection (FG-ZSD for short), which aims to detect objects of different classes with minute differences in details under the ZSD paradigm. We develop an effective method called MSHC for the FG-ZSD task, which is based on an improved two-stage detector and employs a multi-level semantics-aware embedding alignment loss, ensuring tight coupling between the visual and semantic spaces. Considering that existing ZSD datasets are not suitable for the new FG-ZSD task, we build the first FG-ZSD benchmark dataset FGZSD-Birds, which contains 148,820 images falling into 36 orders, 140 families, 579 genera and 1432 species. Extensive experiments on FGZSD-Birds show that our method outperforms existing ZSD models.

In recent years, Few-shot Object Detection (FSOD) has become an increasingly important research topic in computer vision. However, existing FSOD methods require strong annotations including category labels and bounding boxes, and their performance is heavily dependent on the quality of box annotations. However, acquiring strong annotations is both expensive and time-consuming. This inspires the study on weakly supervised FSOD (WS-FSOD in short), which realizes FSOD with only image-level annotations, i.e., category labels. In this paper, we propose a new and effective weakly supervised FSOD method named WFS-DETR. By a well-designed pretraining process, WFS-DETR first acquires general object localization and integrity judgment capabilities on large-scale pretraining data. Then, it introduces object integrity into multiple-instance learning to solve the common local optimum problem by comprehensively exploiting both semantic and visual information. Finally, with simple fine-tuning, it transfers the knowledge learned from the base classes to the novel classes, which enables accurate detection of novel objects. Benefiting from this ``pretraining-refinement'' mechanism, WSF-DETR can achieve good generalization on different datasets. Extensive experiments also show that the proposed method clearly outperforms the existing counterparts in the WS-FSOD task.

Real-world data ubiquitously exhibit long-tailed distribution, which sparks the increasing interest in long-tailed object detection (LTOD). However, existing methods neglect that a lack of diverse data in tail classes will cause underrepresented tail class features, making their efforts for balancing foreground classes tend to over-fit tail classes and be less effective. In this paper, we propose a multi-class co-attention generation network to increase data diversity of tail classes by generating augmented samples. To alleviate imbalance, we develop a distribution-aware up-sampling strategy, performing differential up-sampling for different classes and design a bi-directional regulation loss to adjust both positive and negative gradients. Moreover, we construct a new dataset LVIS-X with more rare classes based on existing LTOD benchmark dataset LVIS. Experiments on LVIS and LVIS-X demonstrate the superiority of the proposed method.

Traditional graph classification requires large amounts of labeled data, which is expensive and time-consuming to acquire, especially in some special scenarios that domain knowledge is indispensable for labeling graphs. Observing that some key subgraphs can determine the properties of graphs (e.g. the toxicity of drug molecules depend on some toxic functional groups), in this paper we explore to classify graphs using unlabeled graphs plus a small number of key subgraphs for each class, which is called weakly-supervised graph classification. To this end, we develop the WeGraph method, where the graph classifier is trained with subgraph-based self-supervised learning and divergence- minimization based fine-tuning. Moreover, we design a key subgraph extraction algorithm to iteratively extract and update the key subgraphs, which makes the training process a closed loop. We conduct extensive experiments on different types of graph datasets to evaluate the effectiveness of WeGraph. Experimental results show that WeGraph can achieve high performance even when only one key subgraph is provided for each class.

Zero-shot object detection aims to localize and recognize objects of unseen classes. Most of existing works face two problems: the low recall of RPN in unseen classes and the confusion of unseen classes with background. In this paper, we present the first method that combines DETR and meta-learning to perform zero-shot object detection, named Meta-ZSDETR, where model training is formalized as an individual episode based meta-learning task. Different from Faster R-CNN based methods that firstly generate class-agnostic proposals, and then classify them with visual-semantic alignment module, Meta-ZSDETR directly predict class-specific boxes with class-specific queries and further filter them with the predicted accuracy from classification head. The model is optimized with meta-contrastive learning, which contains a regression head to generate the coordinates of class-specific boxes, a classification head to predict the accuracy of generated boxes, and a contrastive head that utilizes the proposed contrastive-reconstruction loss to further separate different classes in visual space. We conduct extensive experiments on two benchmark datasets MS COCO and PASCAL VOC. Experimental results show that our method outperforms the existing ZSD methods by a large margin.

Few-shot object detection (FSOD) is to detect objects with a few examples. However, existing FSOD methods do not consider hierarchical fine-grained category structures of objects that exist widely in real life. For example, animals are taxonomically classified into orders, families, genera and species etc. In this paper, we propose and solve a new problem called hierarchical few-shot object detection (Hi-FSOD), which aims to detect objects with hierarchical categories in the FSOD paradigm. To this end, on the one hand, we build the first large-scale and high-quality Hi-FSOD benchmark dataset HiFSOD-Bird, which contains 176,350 wild-bird images falling to 1,432 categories. All the categories are organized into a 4-level taxonomy, consisting of 32 orders, 132 families, 572 genera and 1,432 species. On the other hand, we propose the first Hi-FSOD method HiCLPL, where a hierarchical contrastive learning approach is developed to constrain the feature space so that the feature distribution of objects is consistent with the hierarchical taxonomy and the model's generalization power is strengthened. Meanwhile, a probabilistic loss is designed to enable the child nodes to correct the classification errors of their parent nodes in the taxonomy. Extensive experiments on the benchmark dataset HiFSOD-Bird show that our method HiCLPL outperforms the existing FSOD methods.