Search Results for author:
Found 7 papers, 4 papers with code
InstructAvatar: Text-Guided Emotion and Motion Control for Avatar Generation
Recent talking avatar generation models have made strides in achieving realistic and accurate lip synchronization with the audio, but often fall short in controlling and conveying detailed expressions and emotions of the avatar, making the generated video less vivid and controllable.
LaDiC: Are Diffusion Models Really Inferior to Autoregressive Counterparts for Image-to-Text Generation?
Diffusion models have exhibited remarkable capabilities in text-to-image generation.
Ranked #8 on
Image Captioning
on COCO Captions
(ROUGE-L metric)
UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing
Recent advances in text-guided video editing have showcased promising results in appearance editing (e. g., stylization).
TeG-DG: Textually Guided Domain Generalization for Face Anti-Spoofing
Enhancing the domain generalization performance of Face Anti-Spoofing (FAS) techniques has emerged as a research focus.
Robustness-Guided Image Synthesis for Data-Free Quantization
Quantization has emerged as a promising direction for model compression.
Towards Distribution-Agnostic Generalized Category Discovery
In this paper, we formally define a more realistic task as distribution-agnostic generalized category discovery (DA-GCD): generating fine-grained predictions for both close- and open-set classes in a long-tailed open-world setting.
On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning
Recent work shows that the long-tailed learning performance could be boosted by sampling extra in-domain (ID) data for self-supervised training, however, large-scale ID data which can rebalance the minority classes are expensive to collect.
