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Qiang Qi

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4 papers

1 author row

AAAI Conference 2026 Conference Paper

MCI-Net: A Robust Multi-Domain Context Integration Network for Point Cloud Registration

Shuyuan Lin
Wenwu Peng
Junjie Huang
Qiang Qi
Miaohui Wang
Jian Weng

Robust and discriminative feature learning is critical for high-quality point cloud registration. However, existing deep learning–based methods typically rely on Euclidean neighborhood-based strategies for feature extraction, which struggle to effectively capture the implicit semantics and structural consistency in point clouds. To address these issues, we propose a multi-domain context integration network (MCI-Net) that improves feature representation and registration performance by aggregating contextual cues from diverse domains. Specifically, we propose a graph neighborhood aggregation module, which constructs a global graph to capture the overall structural relationships within point clouds. We then propose a progressive context interaction module to enhance feature discriminability by performing intra-domain feature decoupling and inter-domain context interaction. Finally, we design a dynamic inlier selection method that optimizes inlier weights using residual information from multiple iterations of pose estimation, thereby improving the accuracy and robustness of registration. Extensive experiments on indoor RGB-D and outdoor LiDAR datasets show that the proposed MCI-Net significantly outperforms existing state-of-the-art methods, achieving the highest registration recall of 96.4% on 3DMatch.

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AAAI Conference 2026 Conference Paper

MSTDiff: Multiscale-Aware Transformer Diffusion Network for Video Object Detection

Qiang Qi
Wenqi Shang
Xiao Wang
Yanjie Liang
Shuyuan Lin

Video object detection is a fundamental yet challenging task in computer vision. Recently, DETR-based methods have gained prominence in this domain owing to their powerful global modeling capabilities. However, these methods are still confronted with two key limitations: frame-agnostic initialization of object queries and scale-agnostic attention mechanisms, which hinder their capability to capture the appearance variations of dynamic objects and model the temporal consistency across frames. To alleviate these limitations, we propose a multiscale-aware transformer diffusion network (MSTDiff), a novel framework designed for the video object detection task, including two technical improvements over existing methods. First, we design a diffusion-driven adaptive query module, which models the object query distribution through a diffusion process conditioned on input frames, enabling an adaptive and content-aware initialization of object queries. Second, we develop a multiscale-aware transformer encoder module, which combines multi-head convolutional units with attention mechanisms to enhance multi-scale feature representations while preserving global dependence modeling. We conduct extensive experiments on the public ImageNet VID dataset, and the results demonstrate that our MSTDiff achieves 87.7% mAP with ResNet-101, outperforming most previous state-of-the-art video object detection methods.

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AAAI Conference 2026 Conference Paper

SC-Net: Robust Correspondence Learning via Spatial and Cross-Channel Context

Shuyuan Lin
Hailiang Liao
Qiang Qi
Junjie Huang
Taotao Lai
Jian Weng

Recent research has focused on using convolutional neural networks (CNNs) as the backbones in two-view correspondence learning, demonstrating significant superiority over methods based on multilayer perceptrons. However, CNN backbones that are not tailored to specific tasks may fail to effectively aggregate global context and oversmooth dense motion fields in scenes with large disparity. To address these problems, we propose a novel network named SC-Net, which effectively integrates bilateral context from both spatial and channel perspectives. Specifically, we design an adaptive focused regularization module (AFR) to enhance the model's position-awareness and robustness against spurious motion samples, thereby facilitating the generation of a more accurate motion field. We then propose a bilateral field adjustment module (BFA) to refine the motion field by simultaneously modeling long-range relationships and facilitating interaction across spatial and channel dimensions. Finally, we recover the motion vectors from the refined field using a position-aware recovery module (PAR) that ensures consistency and precision. Extensive experiments demonstrate that SC-Net outperforms state-of-the-art methods in relative pose estimation and outlier removal tasks on YFCC100M and SUN3D datasets.

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AAAI Conference 2025 Conference Paper

TGBFormer: Transformer-GraphFormer Blender Network for Video Object Detection

Qiang Qi
Xiao Wang

Video object detection has made significant progress in recent years thanks to convolutional neural networks (CNNs) and vision transformers (ViTs). Typically, CNNs excel at capturing local features but struggle to model global representations. Conversely, ViTs are adept at capturing long-range global features but face challenges in representing local feature details. Off-the-shelf video object detection methods solely rely on CNNs or ViTs to conduct feature aggregation, which hampers their capability to simultaneously leverage global and local information, thereby resulting in limited detection performance. In this paper, we propose a Transformer-GraphFormer Blender Network (TGBFormer) for video object detection, with three key technical improvements to fully exploit the advantages of transformers and graph convolutional networks while compensating for their limitations. First, we develop a spatial-temporal transformer module to aggregate global contextual information, constituting global representations with long-range feature dependencies. Second, we introduce a spatial-temporal GraphFormer module that utilizes local spatial and temporal relationships to aggregate features, generating new local representations that are complementary to the transformer outputs. Third, we design a global-local feature blender module to adaptively couple transformer-based global representations and GraphFormer-based local representations. Extensive experiments demonstrate that our TGBFormer establishes new state-of-the-art results on the ImageNet VID dataset. Particularly, our TGBFormer achieves 86.5% mAP while running at around 41.0 FPS on a single Tesla A100 GPU.

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