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John See

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6 papers
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6

AAAI Conference 2026 Conference Paper

Unleashing Semantic and Geometric Priors for 3D Scene Completion

  • Shiyuan Chen
  • Wei Sui
  • Bohao Zhang
  • Zeyd Boukhers
  • John See
  • Cong Yang

Camera-based 3D semantic scene completion (SSC) provides dense geometric and semantic perception for autonomous driving and robotic navigation. However, existing methods rely on a coupled encoder to deliver both semantic and geometric priors, which forces the model to make a trade-off between conflicting demands and limits its overall performance. To tackle these challenges, we propose FoundationSSC, a novel framework that performs dual decoupling at both the source and pathway levels. At the source level, we introduce a foundation encoder that provides rich semantic feature priors for the semantic branch and high-fidelity stereo cost volumes for the geometric branch. At the pathway level, these priors are refined through specialised, decoupled pathways, yielding superior semantic context and depth distributions. Our dual-decoupling design produces disentangled and refined inputs, which are then utilised by a hybrid view transformation to generate complementary 3D features. Additionally, we introduce a novel Axis-Aware Fusion (AAF) module that addresses the often-overlooked challenge of fusing these features by anisotropically merging them into a unified representation. Extensive experiments demonstrate the advantages of FoundationSSC, achieving simultaneous improvements in both semantic and geometric metrics, surpassing prior bests by +0.23 mIoU and +2.03 IoU on SemanticKITTI. Additionally, we achieve state-of-the-art performance on SSCBench-KITTI-360, with 21.78 mIoU and 48.61 IoU.

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EAAI Journal 2025 Journal Article

Review of state-of-the-art surface defect detection on wind turbine blades through aerial imagery: Challenges and recommendations

  • Imad Gohar
  • Weng Kean Yew
  • Abderrahim Halimi
  • John See

Maintenance is a critical aspect of wind power generation as it not only ensures the efficient operation of wind turbines, but also their continuous availability and functionality. For wind turbine blades, regular maintenance is essential to optimise power output and minimise operational downtime. While various maintenance strategies are well-documented, such as predictive approaches using Machine Learning and traditional visual inspections, there is limited research on leveraging aerial imagery for detecting defects on turbine blades. The objective of this review paper is to address this by focusing on the challenges and requirements for effective surface defect detection in wind turbine blades through aerial imagery. The task of inspecting surface defects on wind turbine blades is particularly difficult due to data scarcity, substantial computational requirements, and the geometric difficulties in accurately localising defects. By addressing these issues, we aim to identify and propose promising future directions to overcome these challenges at hand, thereby ensuring a progression of research and development in this field.

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AAAI Conference 2022 Conference Paper

TA2N: Two-Stage Action Alignment Network for Few-Shot Action Recognition

  • Shuyuan Li
  • Huabin Liu
  • Rui Qian
  • Yuxi Li
  • John See
  • Mengjuan Fei
  • Xiaoyuan Yu
  • Weiyao Lin

Few-shot action recognition aims to recognize novel action classes (query) using just a few samples (support). The majority of current approaches follow the metric learning paradigm, which learns to compare the similarity between videos. Recently, it has been observed that directly measuring this similarity is not ideal since different action instances may show distinctive temporal distribution, resulting in severe misalignment issues across query and support videos. In this paper, we arrest this problem from two distinct aspects – action duration misalignment and action evolution misalignment. We address them sequentially through a Two-stage Action Alignment Network (TA2 N). The first stage locates the action by learning a temporal affine transform, which warps each video feature to its action duration while dismissing the action-irrelevant feature (e. g. background). Next, the second stage coordinates query feature to match the spatial-temporal action evolution of support by performing temporally rearrange and spatially offset prediction. Extensive experiments on benchmark datasets show the potential of the proposed method in achieving state-of-the-art performance for fewshot action recognition.

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EAAI Journal 2021 Journal Article

Deep multi-level feature pyramids: Application for non-canonical firearm detection in video surveillance

  • JunYi Lim
  • Md Istiaque Al Jobayer
  • Vishnu Monn Baskaran
  • Joanne MunYee Lim
  • John See
  • KokSheik Wong

The epidemic of gun violence worldwide necessitates the need for an active-based video surveillance network to combat this crime. In this context, autonomously detecting handguns is crucial in capturing firearm-related crimes. However, current object detectors using deep learning are unable to capture handguns at different scales in an unconstrained environment. Hence, this paper puts forward an enhanced deep multi-level feature pyramid network that addresses the difficulty in inferring handguns from a non-canonical perspective. We first construct a dataset containing handguns in an unconstrained environment for representation learning. The dataset is constructed from a set of 250 recorded videos and with over 2500 distinct labeled frames. Crucially, these labeled frames account for the absence of a proper video surveillance-based handgun dataset. We then train the dataset on a multi-level multi-scale object detector, i. e. , M2Det. We further improve the performance of M2Det by: (1) Enhancing the base features by concatenating shallow, medium and deep features from the backbone according to its relative receptive field; (2) Implementing generalized intersection-over-union as its localization loss; and (3) Integrating Focal Loss as its classification loss to improve detection of small-scale handguns. Experiments on a challenging video surveillance test dataset demonstrate that the proposed model achieves 87. 42% accuracy. In addition, we implement adaptive surveillance image partitioning to redetect handguns at specific regions. This method potentially solves the challenge of sporadically poor real-world handgun classifications. This model is capable of pioneering non-canonical handgun detection for active-based video surveillance systems. The dataset and trained models are available at: https: //github. com/MarcusLimJunYi/Monash-Guns-Dataset.

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NeurIPS Conference 2020 Conference Paper

Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation

  • Yuxi Li
  • Ning Xu
  • Jinlong Peng
  • John See
  • Weiyao Lin

In this paper, we take attempt to incorporate the cyclic mechanism with the vision task of semi-supervised video object segmentation. By resorting to the accurate reference mask of the first frame, we try to mitigate the error propagation problem in most of current video object segmentation pipelines. Firstly, we propose a cyclic scheme for offline training of segmentation networks. Then, we extend the offline pipeline to an online method by introducing a simple gradient correction module while keeping high efficiency as other offline methods. Finally we develop cycle effective receptive field (cycle-ERF) from gradient correction to provide a new perspective for analyzing object-specific regions of interests. We conduct comprehensive experiments on benchmarks of DAVIS17 and Youtube-VOS, demonstrating that our introduced cyclic mechanism is helpful to boost the segmentation quality.

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AAAI Conference 2020 Conference Paper

Finding Action Tubes with a Sparse-to-Dense Framework

  • Yuxi Li
  • Weiyao Lin
  • Tao Wang
  • John See
  • Rui Qian
  • Ning Xu
  • Limin Wang
  • Shugong Xu

The task of spatial-temporal action detection has attracted increasing attention among researchers. Existing dominant methods solve this problem by relying on short-term information and dense serial-wise detection on each individual frames or clips. Despite their effectiveness, these methods showed inadequate use of long-term information and are prone to inefficiency. In this paper, we propose for the first time, an efficient framework that generates action tube proposals from video streams with a single forward pass in a sparse-to-dense manner. There are two key characteristics in this framework: (1) Both long-term and short-term sampled information are explicitly utilized in our spatiotemporal network, (2) A new dynamic feature sampling module (DTS) is designed to effectively approximate the tube output while keeping the system tractable. We evaluate the efficacy of our model on the UCF101-24, JHMDB-21 and UCFSports benchmark datasets, achieving promising results that are competitive to state-of-the-art methods. The proposed sparse-to-dense strategy rendered our framework about 7. 6 times more efficient than the nearest competitor.

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v2026.09.13