arXiv:2609.00411v1 Announce Type: new
Abstract: Modern face recognition (FR) owes much of its success to deep neural networks that learn to extract compact identity embeddings from face images. These...
By Fizza Rubab, Yiying Tong, Arun Ross
The paper introduces variational decomposition autoencoding (VDA), a framework that biases signal decomposition into latent representations. VDA is realized as variational decomposition autoencoders (DecVAEs), encoder-only networks that learn multiple latent subspaces aligned with time‑frequency characteristics. Experiments on simulated data and three speech datasets show that DecVAEs outperform traditional VAEs in disentanglement quality and robustness, producing factor‑specific latent subspaces when generative factors have distinct spectral signatures.
By Ioannis N. Ziogas, Aamna Al Shehhi, Ahsan H. Khandoker, Leontios J. Hadjileontiadis
The paper introduces Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework that extends Symmetric Nonnegative Matrix Tri-Factorization to supervised prediction across populations of multimodal brain graphs. SD3MF learns deep hierarchical factorizations for each modality and a shared latent representation, jointly optimizing graph reconstruction and prediction while enabling data-driven multimodal fusion. Experiments on multimodal connectome datasets demonstrate that SD3MF outperforms strong deep learning baselines such as CNNs and GNNs, providing biologically interpretable insights through community-level interaction matrices.
By Amjad Seyedi, Lifang He, Songlin Zhao, Akwum Onwunta, Nicolas Gillis
arXiv:2606. 31704v1 Announce Type: cross Abstract: The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance disparities across demographic groups.
By Maxime Moussi, Beno\^it Ronval, Siegfried Nijssen, F\'elicien Schiltz
arXiv:2604. 07282v2 Announce Type: replace-cross Abstract: Automated face recognition has made rapid strides over the past decade due to the unprecedented rise of deep neural network (DNN) models that can be trained for domain-specific tasks.
By Fizza Rubab, Yiying Tong, Arun Ross
The paper introduces Dual Quality Margin Learning for Face Recognition (DQM‑Face), a framework that combines magnitude‑based and semantic quality estimation to refine attraction and repulsion dynamics during training. By integrating squeeze‑and‑excitation semantic attention with dual margins, the method enhances intra‑class compactness and inter‑class separation, yielding a more discriminative feature geometry. Experiments on challenging benchmarks show that DQM‑Face outperforms state‑of‑the‑art face recognition models and that the learned quality signal aligns well with recognition objectives.
By El Ouanas Belabbaci, Bhavesh Wani, Philipp Terh\"orst