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Guojin Zhong

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5 papers

2 author rows

ECAI Conference 2025 Conference Paper

CCRC: A Change-Aware Captioning and Reasoning Chain for Image Change Captioning and Segmentation

Jinhong Hu
Xiaoping Wang
Shuyin Huang
Guojin Zhong
Kaitai Liu
Kai Lu 0001

Understanding and localizing subtle changes between paired images is critical for tasks such as surveillance and image editing. However, traditional Image Change Captioning (ICC) methods lack spatial grounding, limiting their precision. We introduce Image Change Captioning and Segmentation (ICCS), a new multimodal task that jointly requires structured change description and pixel-level localization. To address ICCS, we propose the Change-aware Captioning and Reasoning Chain (CCRC), a dual-chain framework that decouples semantic reasoning from spatial segmentation. The first chain, Chain-of-Change-Captioning (CCC), enhances fine-grained change perception via a visual fusion module—based on Multi-Head Change-aware Attention—inserted between the visual and language components of a Multimodal Large Language Model (MLLM). CCC also determines whether a change is segmentable. If not, it alone generates the caption. Otherwise, the second chain, Chain-of-Change-Segmenting (CCS), is activated, leveraging spatial priors from CCC and refining masks with a Change-aware Token Refiner for accurate boundary localization. We evaluate CCRC on both synthetic and real-world change detection benchmarks with pixel-level supervision. Experiments show CCRC achieves state-of-the-art performance. Code is available at https: //github. com/user-jinhong/CCRC.

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AAAI Conference 2025 Conference Paper

DECIDER: Difference-aware Contrastive Diffusion Model with Adversarial Perturbations for Image Change Captioning

Guojin Zhong
Jinhong Hu
Jiajun Chen
Jin Yuan
Wenbo Pan

Image change captioning (ICC) poses great challenges stemming from describing subtle differences between two similar images in natural language, significantly increasing the complexity of feature extraction and cross-modal learning compared to the image captioning task. Existing ICC methods often suffer from two key challenges: 1) Massive irrelevant information of uni-image features leads to suboptimal visual difference representations; 2) Imprecise inter-modality correspondence degrades the quality of generated captions. This paper proposes a Difference-aware Contrastive Diffusion Model with Adversarial Perturbations (DECIDER) for ICC due to the excellent performance of diffusion models in image/text generation. Technically, difference-aware cross-modal learning is developed to suppress irrelevant information and learn compact yet robust visual difference representations. This is achieved by optimizing a novel objective mathematically derived from the information bottleneck principle that excels in filtering redundant features and highlighting differences. Furthermore, we propose to dynamically generate ``hard'' positive and negative samples via adversarial perturbations, which are involved in contrastive diffusion training with a tighter variational bound. This design encourages our DECIDER to excavate and construct complex correspondences between visual differences and captions, thereby improving generalization performance. Extensive experiments on four datasets demonstrate that DECIDER significantly exceeds state-of-the-art performance.

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ICLR Conference 2025 Conference Paper

Multi-Resolution Decomposable Diffusion Model for Non-Stationary Time Series Anomaly Detection

Guojin Zhong
Pan Wang 0011
Jin Yuan 0002
Zhiyong Li 0001
Long Chen 0016

Recently, generative models have shown considerable promise in unsupervised time series anomaly detection. Nonetheless, the task of effectively capturing complex temporal patterns and minimizing false alarms becomes increasingly challenging when dealing with non-stationary time series, characterized by continuously fluctuating statistical attributes and joint distributions. To confront these challenges, we underscore the benefits of multi-resolution modeling, which improves the ability to distinguish between anomalies and non-stationary behaviors by leveraging correlations across various resolution scales. In response, we introduce a **M**ulti-Res**o**lution **De**composable Diffusion **M**odel (MODEM), which integrates a coarse-to-fine diffusion paradigm with a frequency-enhanced decomposable network to adeptly navigate the intricacies of non-stationarity. Technically, the coarse-to-fine diffusion model embeds cross-resolution correlations into the forward process to optimize diffusion transitions mathematically. It then innovatively employs low-resolution recovery to guide the reverse trajectories of high-resolution series in a coarse-to-fine manner, enhancing the model's ability to learn and elucidate underlying temporal patterns. Furthermore, the frequency-enhanced decomposable network operates in the frequency domain to extract globally shared time-invariant information and time-variant temporal dynamics for accurate series reconstruction. Extensive experiments conducted across five real-world datasets demonstrate that our proposed MODEM achieves state-of-the-art performance and can be generalized to other time series tasks.

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AAAI Conference 2025 Conference Paper

SGDiff: Scene Graph Guided Diffusion Model for Image Collaborative SegCaptioning

Xu Zhang
Jin Yuan
Hanwang Zhang
Guojin Zhong
Yongsheng Zang
Jiacheng Lin
Zhiyong Li

Controllable image semantic understanding tasks, such as captioning or segmentation, necessitate users to input a prompt (e.g., text or bounding boxes) to predict a unique outcome, presenting challenges such as high-cost prompt input or limited information output. This paper introduces a new task ``Image Collaborative Segmentation and Captioning'' (SegCaptioning), which aims to translate a straightforward prompt, like a bounding box around an object, into diverse semantic interpretations represented by (caption, masks) pairs, allowing flexible result selection by users. This task poses significant challenges, including accurately capturing a user's intention from a minimal prompt while simultaneously predicting multiple semantically aligned caption words and masks. Technically, we propose a novel Scene Graph Guided Diffusion Model that leverages structured scene graph features for correlated mask-caption prediction. Initially, we introduce a Prompt-Centric Scene Graph Adaptor to map a user's prompt to a scene graph, effectively capturing his intention. Subsequently, we employ a diffusion process incorporating a Scene Graph Guided Bimodal Transformer to predict correlated caption-mask pairs by uncovering intricate correlations between them. To ensure accurate alignment, we design a Multi-Entities Contrastive Learning loss to explicitly align visual and textual entities by considering inter-modal similarity, resulting in well-aligned caption-mask pairs. Extensive experiments conducted on two datasets demonstrate that SGDiff achieves superior performance in SegCaptioning, yielding promising results for both captioning and segmentation tasks with minimal prompt input.

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IJCAI Conference 2024 Conference Paper

CF-Deformable DETR: An End-to-End Alignment-Free Model for Weakly Aligned Visible-Infrared Object Detection

Haolong Fu
Jin Yuan
Guojin Zhong
Xuan He
Jiacheng Lin
Zhiyong Li

Weakly aligned visible-infrared object detection poses significant challenges due to the imprecise alignment between visible and infrared images. Most existing methods explore the alignment strategies between visible and infrared images, yielding unbearable computation costs. This paper first proposes an end-to-end alignment-free architecture Cross-modal Fusion Deformable DEtection TRansformer (``CF-Deformable DETR'') for weakly aligned visible-infrared object detection. Abandoning the traditional image alignment, CF-Deformable DETR introduces a simple yet effective cross-modal deformable attention mechanism to directly implement automatic cross-modal point mapping, generating well-aligned bimodal features with high efficiency. Moreover, we design a Point-level Feature Consistency Loss to guide the cross-modal point mapping, ensuring the consistency of paired features to support the following fusion. Extensive experiments are conducted on three benchmark datasets. The experimental results demonstrate that CF-Deformable DETR achieves close accuracy on weakly aligned and strictly aligned data as well as maintains stable performance to a certain extent against various offset degrees of weakly aligned data. Code is available at https: //github. com/116508/CF-Deformable-DETR.

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