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.

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.

Recent advancements in diffusion models (DMs) have showcased superior capabilities in generating images and text. This paper first introduces DMs for image change captioning (ICC) and proposes a novel Context-aware Contrastive Diffusion model with Mediator-bridging Cross-modal Transformer (MCT-CCDiff) to accurately predict visual difference descriptions conditioned on two similar images. Technically, MCT-CCDiff develops a Text Embedding Contrastive Loss (TECL) that leverages both positive and negative samples to more effectively distinguish text embeddings, thus generating more discriminative text representations for ICC. To accurately predict visual difference descriptions, MCT-CCDiff introduces a Mediator-bridging Cross-modal Transformer (MCTrans) designed to efficiently explore the cross-modal correlations between visual differences and corresponding text by using a lightweight mediator, mitigating interference from visual redundancy and reducing interaction overhead. Additionally, it incorporates context-augmented denoising to further understand the contextual relationships within caption words implemented by a revised diffusion loss, which provides a tighter optimization bound, leading to enhanced optimization effects for high-quality text generation. Extensive experiments conducted on four benchmark datasets clearly demonstrate that our MCT-CCDiff significantly outperforms state-of-the-art methods in the field of ICC, marking a notable advancement in the generation of precise visual difference descriptions.