🔎 AIGC Detection

📷 CVPR2025 · 3 paper notes

📌 Same area in other venues: 📷 CVPR2026 (10) · 🔬 ICLR2026 (30) · 💬 ACL2026 (17) · 🧪 ICML2026 (11) · 🤖 AAAI2026 (2) · 🧠 NeurIPS2025 (9)

Enhancing Few-Shot Class-Incremental Learning via Training-Free Bi-Level Modality Calibration

This paper proposes the BiMC (Bi-level Modality Calibration) framework based on a frozen CLIP model. By leveraging intra-modal calibration (combining fine-grained class descriptions generated by LLMs with visual prototypes) and inter-modal calibration (fusing pre-trained language knowledge with task-specific visual priors), BiMC achieves state-of-the-art FSCIL performance without any parameter training, outperforming the best baseline by 4.25% on CIFAR-100.

ProAPO: Progressively Automatic Prompt Optimization for Visual Classification

Proposes ProAPO, a progressively automatic prompt optimization method based on evolutionary algorithms. With only one-shot supervision and zero human intervention, it progressively optimizes from task-level templates to category-level descriptions to address hallucination and lack of discriminativeness in LLM-generated descriptions, outperforming existing text prompting methods on 13 datasets.

SGC-Net: Stratified Granular Comparison Network for Open-Vocabulary HOI Detection

This paper proposes the Stratified Granular Comparison Network (SGC-Net), which aggregates multi-layer CLIP visual features via a Granularity-Aware Alignment (GSA) module and recursively generates discriminative descriptions using an LLM within a Hierarchical Group Comparison (HGC) module. This addresses the issues of insufficient feature granularity and semantic confusion in open-vocabulary HOI detection.