V-Align is proposed, a structured framework that formulates VFA as an optimal mono-tonic path traversal problem over a frame–phoneme compatibility lattice and learns a soft traversal posterior and recovers discrete boundaries via dynamic programming.
The key design insight is hierarchical ordering: by first compressing spatial dimensions, VETO drastically reduces the cost of subsequent global temporal matching, bypassing the efficiency wall of single-axis approaches, with an advantage that grows with modern fully-fused attention infrastructure.
DSE-VTG is proposed, a \underline{D}ual-\underline{S}ide \underline{E}nhancement framework that addresses both temporal dynamics and task-specific training without any task-specific training, and achieves state-of-the-art performance among training-free methods.
Zhuo Cao, Bing-Qing Zhang, Sen Wang et al.· 0 citations
A VLM-native framework that detects cross-frame object correspondence and explicitly models object presence states, thereby enabling accurate and consistent visual grounding under streaming inputs, and introduces Streaming Grounding Reinforcement (SGR), which optimizes TempoGround with verifiable Grounding, Identity, a...
Le-Qian Ding, Jun-Ning Qiu, Man-Wen Yang et al.· 0 citations
Universal audio representations must preserve acoustic detail while making high-level concepts accessible across speech, music, environmental sound, and downstream models of different capacities. We study semantic refinement of an acoustically pretrained encoder by adding audio-description alignment to a foundation of...
Le-Jun Min, Jun-Yu Dai, Rui-Chen Zheng et al.· 0 citations
Inspired by the linguistic phenomenon of code-switching, MMCS interleaves vision and language by replacing textual entities with their corresponding visual objects, enforcing local vision-language grounding.
Changhao Xiang, Shangyu Xing, Zhen Wu et al.· 0 citations
A cross-modal temporal alignment framework that combines a multi-scale temporal convolutional encoder with capsule-based dynamic routing, jointly optimizing temporal boundary prediction, cross-modal semantic alignment, and capsule diversity is proposed.
Gengtian Shi, Chen-Hao Wu, Shao-Fei Wang et al.· IEEE Access· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
