Visual foundation models are commonly adapted under the assumption that the appearance of incoming data may change while the semantic meaning of the prediction task remains fixed. In long-lived visual...
arXiv:2610.33877v1 Announce Type: new Abstract: Catastrophic forgetting in continual learning (CL) manifests not only as accuracy degradation but also as \emph{explanation drift}: models maintain pre...
arXiv:2603. 12055v3 Announce Type: replace-cross Abstract: Continual learning of pretrained vision-language models (VLMs) is prone to catastrophic forgetting, yet current approaches adapt to new tasks without explicitly preserving the cross-modal semantic geometry inherited from pretraining and previous stages, allowing new-task supervision to induce geometric distortion.
The paper introduces HASTEN, a hierarchical semantic tree anchoring framework that enhances CLIP-based class‑incremental learning by incorporating external hierarchical knowledge into hyperbolic space. By treating classes as part of a structured hierarchy rather than isolated labels, HASTEN stabilizes visual and textual feature mappings, reduces feature drift, and mitigates catastrophic forgetting across incremental tasks. Experiments on nine benchmarks demonstrate that HASTEN consistently outperforms existing methods.
arXiv:2607. 22705v1 Announce Type: cross Abstract: Object-centric learning aims to represent scenes as objects whose properties can be reused in new combinations.
The paper introduces Self-RefineVQA, a training‑free, inference‑time framework that lets a single Vision‑Language Model iteratively generate, critique, and refine its own answers for table visual question answering. By forming a closed self‑feedback loop guided by prompts, the method corrects common reasoning errors such as spatial misalignment and logical hallucinations without requiring additional labeled data or model updates. Experiments on tabular VQA benchmarks show consistent accuracy gains across monolingual and cross‑lingual settings, especially for lightweight models, while keeping inference cost linear and eliminating the need for expensive supervised fine‑tuning.
arXiv:2609.14523v1 Announce Type: new Abstract: Change visual question answering (Change VQA) requires understanding semantic changes across bi-temporal remote sensing images. Although vision languag...
The paper introduces a concept-guided poisoning unlearning framework that explicitly identifies semantic concepts most strongly linked to a target class or knowledge. For image classification, it uses a Post-hoc Concept Bottleneck Model to locate relevant image regions and creates poisoned samples; for large language models, it aggregates Integrated Gradients over multiple question responses to mask important content and generate poisoned training targets. Experiments across several datasets demonstrate effective unlearning while largely preserving model utility.
The paper introduces Dual-Stream Semantic Guidance (DSSG), a framework for Source‑Fully‑Free Domain Adaptation of Vision‑Language Models that mitigates dual semantic drift through a caption stream and a class‑anchor stream. It adds a Dynamic Cross‑Modal Knowledge Distillation module and a Prototype Anchor Calibration extension (DSSG‑PAC) to reduce computation while maintaining performance. Experiments show DSSG outperforms state‑of‑the‑art methods and DSSG‑PAC cuts adaptation time by 18.9% with minimal loss in accuracy.
GRACE is a new framework for concept erasure in text-to-image diffusion models that uses a semantically weighted sensitive subspace to guide localized interventions and lightweight subspace-constrained adapters to avoid global semantic disruption. It replaces manual counterfactual prompts with an automatically decoupled safe-anchor mechanism and controls intervention strength through an energy-driven dynamic gating system. Experiments show GRACE improves NSFW reduction by 17.86% over five state-of-the-art methods while also reducing target CLIP Score and FID, indicating stronger concept suppression with better preservation of generative quality.
arXiv:2610.07008v1 Announce Type: new Abstract: Few-shot class-incremental learning (FSCIL) aims to learn novel classes from limited annotations while preserving prior knowledge. Existing methods typ...
arXiv:2607. 05382v1 Announce Type: cross Abstract: Visual generators excel at rendering, but they confidently fabricate what they do not know.