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...
By Van Bach Nguyen, J\"org Schl\"otterer, Christin Seifer
The paper introduces a classifier‑free method for generating visual counterfactual explanations (VCEs) using Contrastive Analysis (CA). By separating generative factors common to two datasets from those specific to each class, the approach swaps only the salient factors to produce counterfactual images, thereby avoiding reliance on classifier decision boundaries. Leveraging StyleGAN2’s high‑quality synthesis and a feature‑space latent representation, the method supports multiple salient factors per dataset and achieves superior counterfactual quality on three medical imaging datasets.
By Yunlong He, Pietro Gori
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?
By Yassine Oueslati, Daniil Kirilenko, Martin Gjoreski, Marc Langheinrich
DVLA-RL++ introduces Dual-Level Vision‑Language Alignment with Reinforcement Learning Gating for few‑shot learning. It adds complementary semantic purification (CSP) to separate intrinsic from nuisance textual descriptions and counterfactual reinforcement‑learning gating (CRG) to learn layer‑wise fusion strengths. Experiments on standard, fine‑grained, and cross‑domain benchmarks demonstrate state‑of‑the‑art accuracy, improving on DVLA‑RL by an average of 1.4%.
By Wenhao Li, Xianjing Meng, Qiangchang Wang, Zhongyi Han, Yilong Yin, Liqiang Nie
arXiv:2610.01942v1 Announce Type: new
Abstract: Predicting the future evolution of a scene is a fundamental capability for world modeling. Recent work has shown that operating in the feature space of...
By Efstathios Karypidis, Spyros Gidaris, Nikos Komodakis
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.