Image Difference Captioning (IDC) methods have advanced in highlighting subtle differences between similar images, but their performance is often constrained by limited training data. Using Large Multimodal Models (LMMs) to describe changes in image pairs mitigates data limits but adds noise. These change descriptions are often coarse summaries, obscuring fine details and hindering noise detection. In this work, we improve IDC with a noise-robust approach at both data and model levels. We use LMMs with structured prompts to generate fine-grained change descriptions during data curation. We propose a Noise-Aware Modeling and Captioning (NAMC) model with three modules: Noise Identification and Masking (NIM) to reduce noisy correspondences, Masked Image Reconstruction (MIR) to correct over-masking errors, and Fine-grained Description Generation (FDG) to produce coherent change descriptions. Experiments on four IDC benchmarks show that NAMC, pre-trained on our large-scale data, outperforms streamlined architectures and achieves competitive performance with LLM-finetuned methods, offering better inference efficiency.

The rapid proliferation of multimedia content has necessitated the development of effective multimodal video retrieval systems. Multimodal video retrieval is a non-trivial task involving retrieval of relevant information across different modalities, such as text, audio, and visual. This work aims to improve multimodal retrieval by guiding the creation of a shared embedding space with task-specific contrastive loss functions. An important aspect of our work is to propose a model that learns retrieval cues for the textual query from multiple modalities both separately and jointly within a hierarchical architecture that can be flexibly extended and fine-tuned for any number of modalities. To this end, the loss functions and the architectural design of the model are developed with a strong focus on increasing the mutual information between the textual and cross-modal representations. The proposed approach is quantitatively evaluated on the MSR-VTT and YouCook2 text-to-video retrieval benchmark datasets. The results showcase that the approach not only holds its own against state-of-the-art methods, but also outperforms them in a number of scenarios, with a notable relative improvements from baseline in R@1, R@5 and R@10 metrics.