arXiv Computer Vision
Towards Fast and Disentangled Counterfactuals for Visual Foundation Models
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Jul 28
Concept-based Visual Counterfactual Explanations with Diffusion Models
arXiv:2607. 22544v1 Announce Type: new Abstract: Visual counterfactual explanations aim to answer "what minimal change to this image would flip the model's prediction?
Hugging Face Trending Papers
Aug 10
Perception Before Supervision: Self-Contained Visual Distillation from Counterfactual Blind Spots
Self-improvement for multimodal large language models (MLLMs) is typically driven by reward-based methods that provide only coarse scalar feedback. Distillation offers a richer alternative through dense token-level supervision, but in the visual domain it usually depends on privileged context constructed using external annotations and tools, or stronger models.
arXiv AI
Sep 1
Partition-Aware Unlearning for Removing Spurious Correlations in Large Vision-Language Models
arXiv:2608.29996v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) achieve strong performance across many multimodal tasks; however, they often exploit spurious object-background...
Hugging Face Trending Papers
Aug 30
Partition-Aware Unlearning for Removing Spurious Correlations in Large Vision-Language Models
Large Vision-Language Models (LVLMs) achieve strong performance across many multimodal tasks; however, they often exploit spurious object-background correlations, resulting in predictions driven by co...
arXiv Machine Learning
Jul 22
A Classifier That Teaches Itself: Self-Improving, Frozen-gate Training (SIFT) for Dynamic Document Classification
arXiv:2607. 18358v1 Announce Type: cross Abstract: Document classification is a solved problem in the laboratory and an unsolved one in the enterprise.
arXiv Machine Learning
Aug 11
UNMASK: Discovering and Causally Verifying Spurious Shortcuts in Text Classifiers
arXiv:2608. 09209v1 Announce Type: cross Abstract: Neural language models trained on large crowdsourced corpora frequently exploit spurious surface patterns tied to target labels without true linguistic or causal relevance, boosting benchmark performance while failing on adversarial or out-of-distribution inputs.