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šŸ”— Causal Inference

šŸ“· CVPR2025 Ā· 4 paper notes

šŸ“Œ Same area in other venues: šŸ“· CVPR2026 (4) Ā· šŸ”¬ ICLR2026 (64) Ā· šŸ’¬ ACL2026 (7) Ā· šŸ§Ŗ ICML2026 (19) Ā· šŸ¤– AAAI2026 (7) Ā· šŸ§  NeurIPS2025 (20)

Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency

The Adventurer series of vision models is proposed, which adapts image inputs to a unidirectional causal scanning framework through two simple designs: "Heading Average token" and "Inter-layer Flipping". This allows the Mamba architecture to achieve 4-6x the training speed of existing Vision Mamba models on vision tasks, while maintaining comparable or even superior accuracy to ViT.

Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference

This paper formulates Full-Reference Image Quality Assessment (FR-IQA) as a counterfactual inference problem. By using a Structural Causal Model (SCM), it distinguishes between the causal components related to perceptual quality and the noise components in deep features. This achieves training-free, backbone-agnostic robust quality prediction, obtaining competitive performance on multiple benchmark datasets.

Joint Scheduling of Causal Prompts and Tasks for Multi-Task Learning

Proposed the JSCPT (Joint Scheduling of Causal Prompts and Tasks) framework, which first designs Multi-Task Vision-Language Prompts (MTVLP) and eliminates spurious correlation features in prompts through causal intervention, and then adjusts learning order and weights using an adaptive task scheduler based on the dynamic changes in task relationships during training, achieving significant improvements across multiple multi-task visual recognition benchmarks.

FG-VCE: Towards Fine-Grained Interpretability ā€” Counterfactual Explanations for Misclassification with Saliency Partition

This paper proposes the FG-VCE (Fine-Grained Visual Contrastive Explanation) framework. By calculating feature point contributions via Shapley values, isolating local features using a saliency partition module, and employing an iterative counterfactual generation strategy, it achieves fine-grained counterfactual explanations at both the object and part levels for the first time. It reveals the specific causes of model misclassification: "which fine-grained features led to the error" and "which local regions dominated the prediction change."