IET Computer Vision最新文献

Active domain adaptation (active DA) provides an effective solution by selectively labelling a limited number of target samples to significantly enhance adaptation performance. However, existing active DA methods often struggle in real-world scenarios where, due to data privacy concerns, only a pre-trained source model is available, rather than the source samples. To address this issue, we propose a novel method called the structure-based uncertainty estimation model (SUEM) for source-free active domain adaptation (SFADA). To be specific, we introduce an innovative active sample selection strategy that combines both uncertainty and diversity sampling to identify the most informative samples. We assess the uncertainty in target samples using structure-wise probabilities and implement a diversity selection method to minimise redundancy. For the selected samples, we not only apply standard-supervised loss but also conduct interpolation consistency training to further explore the structural information of the target domain. Extensive experiments across four widely used datasets demonstrate that our method is comparable to or outperforms current UDA and active DA methods.

Skeleton-based action recognition using Graph Convolutional Networks (GCNs) has achieved remarkable performance, but recognising ambiguous actions, such as ‘waving’ and ‘saluting’, remains a significant challenge. Existing methods typically rely on a serial combination of GCNs and Temporal Convolutional Networks (TCNs), where spatial and temporal features are extracted independently, leading to an unbalanced spatial-temporal information, which hinders accurate action recognition. Moreover, existing methods for ambiguous actions often overemphasise local details, resulting in the loss of crucial global context, which further complicates the task of differentiating ambiguous actions. To address these challenges, the authors propose a lightweight plug-and-play module called Synchronised and Fine-grained Head (SF-Head), inserted between GCN and TCN layers. SF-Head first conducts Synchronised Spatial-Temporal Extraction (SSTE) with a Feature Redundancy Loss (F-RL), ensuring a balanced interaction between the two types of features. It then performs Adaptive Cross-dimensional Feature Aggregation (AC-FA), with a Feature Consistency Loss (F-CL), which aligns the aggregated feature with their original spatial-temporal feature. This aggregation step effectively combines both global context and local details, enhancing the model's ability to classify ambiguous actions. Experimental results on NTU RGB + D 60, NTU RGB + D 120, NW-UCLA and PKU-MMD I datasets demonstrate significant improvements in distinguishing ambiguous actions. Our code will be made available at https://github.com/HaoHuang2003/SFHead.

The Diffusion Probabilistic Model (DM) has emerged as a powerful generative model in the field of image synthesis, capable of producing high-quality and realistic images. However, training DM requires a large and diverse dataset, which can be challenging to obtain. This limitation weakens the model's generalisation and robustness when training data is limited. To address this issue, EDG-CDM, an innovative encoder-guided conditional diffusion model was proposed for image synthesis with limited data. Firstly, the authors pre-train the encoder by introducing noise to capture the distribution of image features and generate the condition vector through contrastive learning and KL divergence. Next, the encoder undergoes further training with classification to integrate image class information, providing more favourable and versatile conditions for the diffusion model. Subsequently, the encoder is connected to the diffusion model, which is trained using all available data with encoder-provided conditions. Finally, the authors evaluate EDG-CDM on various public datasets with limited data, conducting extensive experiments and comparing our results with state-of-the-art methods using metrics such as Fréchet Inception Distance and Inception Score. Our experiments demonstrate that EDG-CDM outperforms existing models by consistently achieving the lowest FID scores and the highest IS scores, highlighting its effectiveness in generating high-quality and diverse images with limited training data. These results underscore the significance of EDG-CDM in advancing image synthesis techniques under data-constrained scenarios.

With fine-grained classification, we identify unique characteristics to distinguish among classes of the same super-class. We are focusing on species recognition in Insecta as they are critical for biodiversity monitoring and at the base of many ecosystems. With citizen science campaigns, billions of images are collected in the wild. Once these are labelled, experts can use them to create distribution maps. However, the labelling process is time consuming, which is where computer vision comes in. The field of computer vision offers a wide range of algorithms, each with its strengths and weaknesses; how do we identify the algorithm that is in line with our application? To answer this question, we provide a full and detailed evaluation of nine algorithms among deep convolutional networks (CNN), vision transformers (ViT) and locality-based vision transformers (LBVT) on 4 different aspects: classification performance, embedding quality, computational cost and gradient activity. We offer insights that we have not yet had in this domain proving to which extent these algorithms solve the fine-grained tasks in Insecta. We found that ViT performs the best on inference speed and computational cost, whereas LBVT outperforms the others on performance and embedding quality; the CNN provide a trade-off among the metrics.

Camouflaged object detection (COD) aims to identify and segment objects that closely resemble and are seamlessly integrated into their surrounding environments, making it a challenging task in computer vision. COD is constrained by the limited availability of training data and annotated samples, and most carefully designed COD models exhibit diminished performance under low-data conditions. In recent years, there has been increasing interest in leveraging foundation models, which have demonstrated robust general capabilities and superior generalisation performance, to address COD challenges. This work proposes a knowledge-guided domain adaptation (KGDA) approach to tackle the data scarcity problem in COD. The method utilises the knowledge descriptions generated by multimodal large language models (MLLMs) for camouflaged images, aiming to enhance the model's comprehension of semantic objects and camouflaged scenes through highly abstract and generalised knowledge representations. To resolve ambiguities and errors in the generated text descriptions, a multi-level knowledge aggregation (MLKG) module is devised. This module consolidates consistent semantic knowledge and forms multi-level semantic knowledge features. To incorporate semantic knowledge into the visual foundation model, the authors introduce a knowledge-guided semantic enhancement adaptor (KSEA) that integrates the semantic knowledge of camouflaged objects while preserving the original knowledge of the foundation model. Extensive experiments demonstrate that our method surpasses 19 state-of-the-art approaches and exhibits strong generalisation capabilities even with limited annotated data.

This study presents a novel approach to crop mapping using remotely sensed satellite images. It addresses the significant classification modelling challenges, including (1) the requirements for extensive labelled data and (2) the complex optimisation problem for selection of appropriate temporal windows in the absence of prior knowledge of cultivation calendars. We compare the lightweight Dynamic Time Warping (DTW) classification method with the heavily supervised Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) using high-resolution multispectral optical satellite imagery (3 m/pixel). Our approach integrates effective practical preprocessing steps, including data augmentation and a data-driven optimisation strategy for the temporal window, even in the presence of numerous crop classes. Our findings demonstrate that DTW, despite its lower data demands, can match the performance of CNN-LSTM through our effective preprocessing steps while significantly improving runtime. These results demonstrate that both CNN-LSTM and DTW can achieve deployment-level accuracy and underscore the potential of DTW as a viable alternative to more resource-intensive models. The results also prove the effectiveness of temporal windowing for improving runtime and accuracy of a crop classification study, even with no prior knowledge of planting timeframes.

3D object detection is one of the current popular hotspots by perceiving the surrounding environment through LiDAR and camera sensors to recognise the category and location of objects in the scene. Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples. Although some approaches have begun to investigate the robustness of 3D object detection models, they are currently generating adversarial examples in a white-box setting and there is a lack of research into generating transferable adversarial examples in a black-box setting. In this paper, a non-end-to-end attack algorithm was proposed for LiDAR pipelines that crafts transferable adversarial examples against 3D object detection. Specifically, the method generates adversarial examples by restraining features with high contribution to downstream tasks and amplifying features with low contribution to downstream tasks in the feature space. Extensive experiments validate that the method produces more transferable adversarial point clouds, for example, the method generates adversarial point clouds in the nuScenes dataset that are about 10$��\%�$ and 7$��\%�$ better than the state-of-the-art method on mAP and NDS, respectively.

In contrast to batch learning where all training data is available at once, continual learning represents a family of methods that accumulate knowledge and learn continuously with data available in sequential order. Similar to the human learning process with the ability of learning, fusing and accumulating new knowledge acquired at different time steps, continual learning is considered to have high practical significance. Hence, continual learning has been studied in various artificial intelligence tasks. In this paper, we present a comprehensive review of the recent progress of continual learning in computer vision. In particular, the works are grouped by their representative techniques, including regularisation, knowledge distillation, memory, generative replay, parameter isolation and a combination of the above techniques. For each category of these techniques, both its characteristics and applications in computer vision are presented. At the end of this overview, several subareas, where continuous knowledge accumulation is potentially helpful while continual learning has not been well studied, are discussed.

Multi-object tracking (MOT) is a fundamental problem in computer vision that involves tracing the trajectories of foreground targets throughout a video sequence while establishing correspondences for identical objects across frames. With the advancement of deep learning techniques, methods based on deep learning have significantly improved accuracy and efficiency in MOT. This paper reviews several recent deep learning-based MOT methods and categorises them into three main groups: detection-based, single-object tracking (SOT)-based, and segmentation-based methods, according to their core technologies. Additionally, this paper discusses the metrics and datasets used for evaluating MOT performance, the challenges faced in the field, and future directions for research.

With the development of the 3D point cloud field in recent years, point cloud completion of 3D objects has increasingly attracted researchers' attention. Point cloud data can accurately express the shape information of 3D objects at different resolutions, but the original point clouds collected directly by various 3D scanning equipment are often incomplete and have uneven density. Tactile is one distinctive way to perceive the 3D shape of an object. Tactile point clouds can provide local shape information for unknown areas during completion, which is a valuable complement to the point cloud data acquired with visual devices. In order to effectively improve the effect of point cloud completion using tactile information, the authors propose an innovative tactile-assisted point cloud completion network, TAPCNet. This network is the first neural network customised for the input of tactile point clouds and incomplete point clouds, which can fuse two types of point cloud information in the feature domain. Besides, a new dataset named 3DVT was rebuilt, to fit the proposed network model. Based on the tactile fusion strategy and related modules, multiple comparative experiments were conducted by controlling the quantity of tactile point clouds on the 3DVT dataset. The experimental data illustrates that TAPCNet can outperform the state-of-the-art methods in the benchmark.