Video and audio are perceived together, yet most generative models treat them in isolation. We examine methods that model the two modalities jointly, generate one from the other, or edit them in a coupled manner, organized around a single question: how is the output kept coherent across modalities in time and semantics...
Abhinav Sharma, S. Navuluru, Wang Wei et al.· 0 citations
Personalized image generation has remained narrowly focused on conditional synthesis from curated visual exemplars, rather than capturing who a user is. In practice, however, a user's personal context is much richer, comprising reviews, posts, images, captions, and metadata accumulated over time. A truly personalized g...
B. Ni, Ngoc N. Tran, Qin-Wen Ge et al.· 0 citations
SimLoss is proposed, a reference-free embedding-space objective for single-pass fine-grained image captioning, and results show that embedding-space supervision can recover the quality of multi-stage verification at the latency of a single-pass captioner.
Suryaansh Jain, Rahasya Barkur, G. Vishal et al.· 0 citations
The results show that pruning effectiveness depends more on where pruning is applied than on the specific scoring rule: early pruning yields the largest end-to-end savings, while later pruning mainly refines the final synthesis context.
Harshitha Kolukuluru, Reshma Ashok, Kirat Arora et al.· 0 citations
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