arXiv AI
CounterFlow: Target-Conditioned Flow Matching for Visual Counterfactuals
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arXiv Computer Vision
Oct 19
CLEAR: Cooperative Latent Flow for Visual Counterfactual Explanations with Accurate Region Preservation
The paper introduces CLEAR, a latent Flow Matching framework designed to generate visual counterfactual explanations (VCEs) that alter a classifier’s prediction while preserving realistic and decision‑irrelevant content. CLEAR addresses two main issues in latent‑space VCEs: opposing update directions between latent generation and classifier guidance, and unintended changes from VAE reconstruction. By decomposing classifier guidance into cooperative and conflicting components and selectively attenuating the conflicting part, CLEAR achieves counterfactual quality comparable to image‑space methods while being 2.5× faster.
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?
arXiv Computer Vision
Sep 30
Targeted Visual Counterfactual Explanations for Contrastive Vision-Language Model
arXiv:2609.37638v1 Announce Type: new Abstract: Current explanation methods for contrastive vision--language models such as CLIP mainly identify important regions without showing how to change the in...
arXiv AI
Oct 5
Contextual Flow Matching: Adaptive Step Selection in Flow Models for Efficient Visual Generation
arXiv:2610.03202v1 Announce Type: cross Abstract: Flow Matching enables high-quality visual generation via continuous-time dynamics, but inference remains costly due to multiple sequential function e...
arXiv Computer Vision
Aug 24
RECOUNT: Reference-guided Counting with Synthetic Visual Exemplars
arXiv:2608.20621v1 Announce Type: new Abstract: Text-guided zero-shot object counters excel at spatial localization but categorize poorly on novel or fine-grained classes: natural language is too coa...
arXiv AI
Aug 28
CounterVid: Counterfactual Video Generation for Mitigating Action and Temporal Hallucinations in Video-Language Models
CounterVid introduces a scalable counterfactual video generation framework that creates videos differing only in actions or temporal structure while keeping scene context intact. The approach uses multimodal LLMs for action proposals and diffusion models for editing, producing a synthetic dataset of ~26k preference pairs for action recognition and sequence ordering. With the MixDPO optimization method, the authors demonstrate significant improvements in action recognition and temporal ordering on Qwen2.5‑VL and InternVL3 backbones, while maintaining overall video understanding.