🔬 Interpretability

🎞️ ECCV2026 · 3 paper notes

📌 Same area in other venues: 📷 CVPR2026 (34) · 🔬 ICLR2026 (196) · 💬 ACL2026 (63) · 🧪 ICML2026 (91) · 🤖 AAAI2026 (37) · 🧠 NeurIPS2025 (80)

Blind to Position, Biased in Language: Probing Mid-Layer Representational Bias in Vision-Language Encoders for Zero-Shot Language-Grounded Spatial Understanding

B2G probes frozen vision-language encoders and reuses spatially sensitive visual features and more language-consistent text features for region ranking, increasing average multilingual referring segmentation mIoU from 42.42 to 49.62 across nine benchmarks, with additional multilingual inference cost.

Evaluating the Interpretability of Sparse Autoencoders with Concept Annotations

This paper proposes an interpretability evaluation framework for SAEs based on human concept annotations. It features the FBMP algorithm supporting many-to-one matching and the TAPAScore causal validation metric based on target attribute perturbation. Furthermore, two synthetic perturbation datasets, synCUB and synCOCO, are constructed. Experiments show that existing automated evaluation metrics (FMS, MS, CKNNA) fail the sanity checks, whereas the proposed matching metrics and TAPAScore reliably distinguish trained from untrained SAEs. Additionally, increasing overcompleteness is found to degrade the quality of perturbation alignment.

On the Faithfulness of Post-Hoc Concept Bottleneck Models

This paper systematically analyzes the faithfulness of concept projection in post-hoc CBMs. It demonstrates that classifier accuracy cannot serve as a proxy metric for the quality of the bottleneck layer (since random projections can also achieve competitive performance), identifies two fundamental causes of unfaithfulness—covariate shift in auxiliary datasets and systematic errors in VLM surrogate labels—and proposes corresponding diagnostic metrics (\(\mathcal{H}\Delta\mathcal{H}\)-divergence and error-activation correlation).