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This paper introduces a multi-branch feature fusion framework for detecting and propagating health misinformation, integrating semantic, rhetorical, and psychological cues to achieve strong performance on benchmark datasets.
This paper proposes a multimodal framework that jointly improves Automatic Speech Recognition (ASR) and Dialect Identification (DID) for Indian languages, using a Bottleneck Encoder and RoBERTa with a gating mechanism. Evaluated on eight languages with 33 dialects, it achieves 81.63% DID accuracy and reduces CER/WER to 4.65%/17.73%.
Proposes a reinforcement learning framework that uses locally linear embeddings to capture environment dynamics and an attention mechanism to adaptively fuse dynamics-specific and reward-specific features, inspired by neural principles, improving learning efficiency.
MM++ is a fully unsupervised, post-hoc framework for out-of-distribution detection that fuses discriminative intermediate layers via top-K gated feature fusion and uses a regularized tied covariance matrix for scale-invariant distance estimation.
This paper introduces DRoRAE, a method that improves visual tokenization by fusing multi-layer features from pretrained vision encoders rather than relying solely on the last layer. It demonstrates significant improvements in reconstruction and generation quality on ImageNet and establishes a scaling law between fusion capacity and performance.