🔬 Interpretability

🎞️ ECCV2024 · 5 paper notes

📌 Same area in other venues: 📷 CVPR2026 (34) · 🔬 ICLR2026 (196) · 💬 ACL2026 (63) · 🧪 ICML2026 (92) · 🤖 AAAI2026 (37) · 🧠 NeurIPS2025 (80)

DetailSemNet: Elevating Signature Verification through Detail-Semantic Integration

DetailSemNet is proposed for offline signature verification, which decouples features into detail and semantic branches via a Detail-Semantics Integrator, and introduces EMD-based local structural matching to achieve SOTA performance on multiple multilingual signature datasets.

EgoExo-Fitness: Towards Egocentric and Exocentric Full-Body Action Understanding

This paper proposes the EgoExo-Fitness dataset, which contains synchronized egocentric and exocentric fitness videos. It provides two-level temporal boundary annotations and innovative explainable action assessment labels (technical keypoint verification, natural language commentary, and quality scoring) and establishes five benchmark tasks.

Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models

This paper identifies that the low efficiency of human intervention in Concept Bottleneck Models (CBMs) stems from the independent processing of concepts during intervention, which neglects inter-concept correlations. It proposes a lightweight Concept Intervention Realignment Module (CIRM) that automatically realigns the predictions of related concepts post-intervention, reducing the number of interventions required to reach target performance by up to 70%.

PLOT: Text-based Person Search with Part Slot Attention for Corresponding Part Discovery

This paper proposes the PLOT framework, which utilizes a Part Discovery Module based on Slot Attention to automatically discover corresponding human body parts across modalities (image-text). Combined with Text-based Dynamic Part Attention (TDPA) to dynamically adjust the importance of each part, it thoroughly outperforms state-of-the-art (SOTA) methods on three benchmarks without requiring part-level annotations.

POA: Pre-training Once for Models of All Sizes

POA proposes introducing an elastic student branch into the self-supervised self-distillation framework. Through parameter sharing and random sub-network sampling, hundreds of pre-trained models of different sizes can be produced simultaneously with a single pre-training run (e.g., directly extracting ViT-S/B from ViT-L). Each sub-network achieves SOTA performance on k-NN, linear probing, and downstream tasks.