CRANE：首个面向多模态分类器的后验反事实解释框架
CRANE: Post-Hoc Counterfactual Retrieval for Multimodal Classifiers
一篇给多模态模型做反事实解释的论文，思路是直接从训练集里检索真实样本当解释，比逐模态归因更连贯，可解释性方向的同学可以看看。
CRANE（Counterfactual Retrieval via Agnostic Nearest-neighbour Explanations）是首个面向多模态分类器的后验、模型无关反事实解释框架，通过检索目标类别的真实训练样本作为解释。它提出的 Multimodal Consensus Retrieval（MC-R）策略对每个模态的近邻集合取交集，找到在各模态上同时接近查询的实例。在四个异构数据集和三种融合策略上，MC-R 在其中三个数据集上取得最优或并列最优结果。当单一模态主导决策边界时，单模态策略仍然具有竞争力。
CRANE: Post-Hoc Counterfactual Retrieval for Multimodal Classifiers
Counterfactual explanation methods have been introduced for unimodal settings, with dedicated frameworks for tabular, timeseries, text, and image data, yet no post-hoc method has been designed for classifiers that learn from multimodal inputs. Existing explainability methods for multimodal classifiers apply modality-specific attribution independently to each input, which fails to yield coherent explanations and does not transfer to arbitrary pre-trained multimodal architectures. To address these shortcomings, we present CRANE (Counterfactual Retrieval via Agnostic Nearest-neighbour Explanations), the first post-hoc, model-agnostic framework for nearest-neighbour multimodal counterfactual generation that retrieves real training examples from the target class as candidate explanations, ensuring plausibility by construction. Across four heterogeneous datasets and three fusion strategies, we show that Multimodal Consensus Retrieval (MC-R), which intersects per-modality neighbour sets to identify instances jointly close to the query in every modality, emerges as the strongest multimodal strategy, achieving the best or joint-best outcome on three of four datasets while grounding every explanation in the joint training distribution. Unimodal strategies remain competitive when a single modality dominates the decision boundary.
