HCEP tackles open-set emotion recognition: teaching multimodal AI to admit when an expression matches no known category instead of forcing a wrong label.
Teaching AI to Say “I Don’t Know”: HCEP and the Open-Set Emotion Recognition Problem Read More »
Stochastic Bregman proximal gradient methods drop the Lipschitz assumption behind SGD’s convergence proofs, match optimal sample complexity, and resist gradient explosion where standard optimizers collapse.
MVR-GLF extracts three complementary spectral views from temporal hyperspectral images and fuses them globally and locally, catching changes that single-view detectors miss.
How MVR-GLF Reads Spectral Shape to Catch What Others Miss Read More »
A criterion-wise local differential privacy framework perturbs each rating dimension on-device before it reaches the server — here’s what that privacy costs in recommendation accuracy.
What Happens When You Add Differential Privacy to a Multi-Criteria Recommender System Read More »
SMM-FNet fuses hyperspectral, multispectral, and radar imagery through purpose-built Mamba modules, showing why three sensor sources beat two in remote sensing.
When Two Sensors Are Not Enough: SMM-FNet Fuses Three Remote Sensing Sources With Mamba Read More »
Federated recommenders drift apart when clients simply average parameters. AlignFedRec shares item similarity matrices instead, aligning structure rather than embeddings.
Why Federated Recommenders Break and How Structural Alignment Fixes Them Read More »
RASD-FuNet routes each feature point to a low-rank or attention branch based on a learned confidence score, topping hyperspectral fusion benchmarks at a fraction of the FLOPs.
PFLlib benchmarks traditional and personalized federated learning algorithms across three heterogeneity scenarios, giving the field a common yardstick.
PFLlib Brings Order to Personalized Federated Learning Research Read More »
KEDR separates shared disease knowledge from modality-specific visual detail so medical AI can still diagnose reliably when one scan is missing.
Missing Modality Medical Imaging: When a Scan Is Absent, Can AI Still Diagnose You? Read More »
KECL detects multimodal misinformation with knowledge-enhanced encoding and disentangled cross-modal alignment, catching image-caption pairs that lie together.
When Images and Text Lie Together — A New AI (KECL) Framework Catches What Others Miss Read More »