👥 Social Computing

📷 CVPR2025 · 6 paper notes

📌 Same area in other venues: 📷 CVPR2026 (3) · 🔬 ICLR2026 (17) · 💬 ACL2026 (45) · 🧪 ICML2026 (9) · 🤖 AAAI2026 (10) · 🧠 NeurIPS2025 (20)

As Language Models Scale, Low-order Linear Depth Dynamics Emerge

By treating the depth dimension of Transformers as a discrete-time dynamical system, this paper finds that a linear state-space surrogate model of just 32 dimensions can predict inter-layer sensitivity curves with high precision (Spearman up to 0.99) within a given context. Surprisingly, as the model scales, the low-order linear surrogate becomes even more accurate—unveiling a new scaling law.

Classifier-guided CLIP Distillation for Unsupervised Multi-label Classification

This paper proposes Classifier-guided CLIP Distillation (CCD), which achieves unsupervised multi-label classification performance on par with fully supervised methods (90.1% mAP on VOC12) without any manual annotations by leveraging two core techniques: CAM-guided local view label aggregation and CLIP prediction debiasing.

Classifier-to-Bias: Toward Unsupervised Automatic Bias Detection for Visual Classifiers

Proposes C2B (Classifier-to-Bias), the first framework to automatically detect biases in pre-trained visual classifiers using only the textual descriptions of the classification task (without any labeled data). By leveraging LLMs to generate class-specific bias candidates, creating retrieval queries to collect image datasets, and finally calculating bias scores, C2B outperforms supervised SOTA bias detection methods on CelebA and ImageNet-X.

Learning from Neighbors: Category Extrapolation for Long-Tail Learning

It is discovered that finer-grained category division naturally mitigates the impact of long-tail imbalance. This paper proposes using LLMs to discover fine-grained auxiliary categories semantically related to existing ones, web crawlers to collect images, and a Neighbor-Silencing Loss to prevent auxiliary classes from dominating. This achieves a 16-percentage-point improvement (\(41.4\% \to 57.4\%\)) on Few-shot classes in ImageNet-LT.

Let Samples Speak: Mitigating Spurious Correlation by Exploiting the Clusterness of Samples

Based on abstract: Deep learning models are known to often learn features that spuriously correlate with the class label during training but are irrelevant to the prediction task. Existing methods typically address this issue by annotating potential spurious attributes, or filtering spurious features based on some emp

Project-Probe-Aggregate: Efficient Fine-Tuning for Group Robustness

This paper proposes Project-Probe-Aggregate (PPA), a three-step framework that improves the group robustness of foundation models without group annotations, using less than 0.01% of trainable parameters. PPA projects features to remove class proxies and amplify bias, probes group labels corrected with group priors, and aggregates group weights.