Synthetic data complements real data in diffusion-based controllable human video generation, with effective sample selection improving motion realism, temporal consistency, and identity preservation.
Learning transferable visual models from natural language supervi- sion
3 Pith papers cite this work. Polarity classification is still indexing.
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GA2-CLIP uses generic attribute anchors and coupled hard-soft prompts to preserve generalization in prompt-tuned video-language models on base-to-new class tasks.
FruitEnsemble uses a weighted ensemble of backbones for top-3 candidates followed by MLLM arbitration on low-confidence samples to reach 70.49% accuracy on a new 306-class fruit dataset.
citing papers explorer
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Exploring the Role of Synthetic Data Augmentation in Controllable Human-Centric Video Generation
Synthetic data complements real data in diffusion-based controllable human video generation, with effective sample selection improving motion realism, temporal consistency, and identity preservation.
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GA2-CLIP: Generic Attribute Anchor for Efficient Prompt Tuningin Video-Language Models
GA2-CLIP uses generic attribute anchors and coupled hard-soft prompts to preserve generalization in prompt-tuned video-language models on base-to-new class tasks.
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FruitEnsemble: MLLM-Guided Arbitration for Heterogeneous ensemble in Fine-Grained Fruit Recognition
FruitEnsemble uses a weighted ensemble of backbones for top-3 candidates followed by MLLM arbitration on low-confidence samples to reach 70.49% accuracy on a new 306-class fruit dataset.