9 accepted papers
Large-scale Video Foundation Models (VFMs) have significantly advanced various video-related tasks, either through task-specific models or Multi-modal Large Language Models (MLLMs). However, the open accessibility of VFMs also introduces critical security risks, as adversaries can exploit full knowl
Personalized generation models for a single subject have demonstrated remarkable effectiveness, highlighting their significant potential. However, when extended to multiple subjects, existing models often exhibit degraded performance, particularly in maintaining subject consistency and adhering to t
Vision-Language-Action (VLA) models are vulnerable to adversarial attacks, yet universal and transferable attacks remain underexplored, as most existing patches overfit to a single model and fail in black-box settings. To address this gap, we present a systematic study of universal, transferable adv
Video understanding requires the extraction of rich spatio-temporal representations, achieved by transformer models through self-attention. Unfortunately, self-attention poses a computational burden. In NLP, Mamba has surfaced as an efficient alternative for transformers. However, Mamba's successes…
A key challenge in continuous sign language recognition (CSLR) is to efficiently capture long-range spatial interactions over time from the video input. To address this challenge, we propose TCNet, a hybrid network that effectively models spatio-temporal information from Trajectories and Correlated…
We propose unifying one-shot voice conversion and cloning into a single model that can be end-to-end optimized. To achieve this, we introduce a novel extension to a speech variational auto-encoder (VAE) that disentangles speech into content and speaker representations. Instead of using a fixed Gauss…
We introduce a framework for navigating through cluttered environments by connecting multiple cameras together while simultanously preserving privacy. Occlusions and obstacles in large environments are often challenging situations for navigation agents because the environment is not fully observable…
Dysarthric speech reconstruction (DSR), which aims to improve the quality of dysarthric speech, remains a challenge, not only because we need to restore the speech to be normal, but also must preserve the speaker’s identity. The speaker representation extracted by the speaker encoder (SE) optimized…
Voice conversion can benefit from WaveNet vocoder with improvement in converted speech's naturalness and quality. However, nowadays approaches segregate the training of conversion module and WaveNet vocoder towards different optimization objectives, which might lead to the difficulty in model tuning…