📐 Optimization & Theory¶

🎞️ ECCV2024 · 2 paper notes

Fine-Grained Scene Graph Generation via Sample-Level Bias Prediction: This paper proposes a sample-level bias prediction method named SBP. By leveraging a Bias-Oriented GAN, it utilizes the contextual information of the union region of object pairs to predict sample-specific bias correction vectors, reforming coarse-grained relationships into fine-grained ones. SBP outperforms dataset-level bias correction methods by an average of 5.6%/3.9%/3.2% in Average@K on VG/GQA/VG-1800 datasets, respectively.
Handling the Non-smooth Challenge in Tensor SVD: A Multi-objective Tensor Recovery Framework: A multi-objective tensor recovery framework (MOTC) based on learnable tensor nuclear norm is proposed. By introducing learnable unitary matrices in place of fixed transforms, this approach addresses the performance degradation of t-SVD methods on non-smooth tensor data, while effectively exploiting the low-rankness of tensors across all dimensions through multi-objective optimization.