3 accepted papers
We introduce MoS (Mixture of States), a novel fusion paradigm for multimodal diffusion models that merges modalities using flexible, state-based interactions. The core of MoS is a learnable, token-wise router that creates denoising timestep- and input-dependent interactions between modalities' hidde
Deep neural networks are vulnerable to small input perturbations known as adversarial attacks. Inspired by the fact that these adversaries are constructed by iteratively minimizing the confidence of a network for the true class label, we propose the anti-adversary layer, aimed at countering this eff…
We address the problem of segmenting an object given a natural language expression that describes it. Current techniques tackle this task by either ( extit{i}) directly or recursively merging linguistic and visual information in the channel dimension and then performing convolutions; or by ( extit{i…