While mode collapse is typically a side effect for generative modeling, it is somewhat "welcomed" in SoTA image captioning models as it usually facilitates a higher evaluation performance on reference-based metrics like CIDEr, BLEU and SPICE.

Novel object captioning (NOC) aims to describe images containing objects without observing their ground truth captions during training.

Novel object captioning (NOC) aims to describe images containing objects without observing their ground truth captions during training.

We are glad that the reviewers found our experimental setup exhaustive (R1-4). This is not feasible with prior work, e . We will clarify and highlight these challenges in the final version. Random samples in Tab. 2, 11, and 12 show that the captions from COS-CV AE are coherent (ll. COS-CV AE has a score of 0.742 while Seq-CV AE(attn) has 0.714.

We thank all the reviewers for the helpful comments. We will revise the paper to address your concerns. R1-Q1: The implementation seems straight-forward and the ablation analysis on the loss function. Thus we kept using L1 norm in the rest of experiments. We will conduct a systematic comparison between various loss functions in the next revision.

Text-to-image (T2I) models exhibit a significant yet under-explored "brand bias", a tendency to generate contents featuring dominant commercial brands from generic prompts, posing ethical and legal risks. We propose CIDER, a novel, model-agnostic framework to mitigate bias at inference-time through prompt refinement to avoid costly retraining. CIDER uses a lightweight detector to identify branded content and a Vision-Language Model (VLM) to generate stylistically divergent alternatives. We introduce the Brand Neutrality Score (BNS) to quantify this issue and perform extensive experiments on leading T2I models. Results show CIDER significantly reduces both explicit and implicit biases while maintaining image quality and aesthetic appeal. Our work offers a practical solution for more original and equitable content, contributing to the development of trustworthy generative AI.