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arXiv Computer Vision research summaries for March 28, 2024.
Today's Research Themes (AI-Generated):
• CLAP4CLIP introduces Continual LeArning with Probabilistic finetuning for Vision-Language Models, enhancing reliability and reducing forgetting in continual learning tasks.
• QNCD presents Quantization Noise Correction Scheme for Diffusion Models to improve image synthesis quality in low-bit settings.
• MoDiTalker proposes a novel motion-disentangled diffusion model for high-quality talking head generation from audio.
• Uncertainty-Aware Deep Video Compression uses ensembles to better capture predictive uncertainty and achieve significant bit rate savings.
• Dyco leverages delta pose sequence representation for improved 3D human modeling, capturing appearance changes caused by motion inertia.
arXiv Computer Vision research summaries for March 28, 2024.
Today's Research Themes (AI-Generated):
• CLAP4CLIP introduces Continual LeArning with Probabilistic finetuning for Vision-Language Models, enhancing reliability and reducing forgetting in continual learning tasks.
• QNCD presents Quantization Noise Correction Scheme for Diffusion Models to improve image synthesis quality in low-bit settings.
• MoDiTalker proposes a novel motion-disentangled diffusion model for high-quality talking head generation from audio.
• Uncertainty-Aware Deep Video Compression uses ensembles to better capture predictive uncertainty and achieve significant bit rate savings.
• Dyco leverages delta pose sequence representation for improved 3D human modeling, capturing appearance changes caused by motion inertia.