Search Results for author:
Found 10 papers, 4 papers with code
Unleash the Potential of CLIP for Video Highlight Detection
Multimodal and large language models (LLMs) have revolutionized the utilization of open-world knowledge, unlocking novel potentials across various tasks and applications.
Ranked #1 on
Highlight Detection
on QVHighlights
HourglassNeRF: Casting an Hourglass as a Bundle of Rays for Few-shot Neural Rendering
Addressing this, we propose HourglassNeRF, an effective regularization-based approach with a novel hourglass casting strategy.
Fast Sun-aligned Outdoor Scene Relighting based on TensoRF
In this work, we introduce our method of outdoor scene relighting for Neural Radiance Fields (NeRF) named Sun-aligned Relighting TensoRF (SR-TensoRF).
ConcatPlexer: Additional Dim1 Batching for Faster ViTs
Transformers have demonstrated tremendous success not only in the natural language processing (NLP) domain but also the field of computer vision, igniting various creative approaches and applications.
FlipNeRF: Flipped Reflection Rays for Few-shot Novel View Synthesis
Neural Radiance Field (NeRF) has been a mainstream in novel view synthesis with its remarkable quality of rendered images and simple architecture.
MixNeRF: Modeling a Ray with Mixture Density for Novel View Synthesis from Sparse Inputs
In this work, we propose MixNeRF, an effective training strategy for novel view synthesis from sparse inputs by modeling a ray with a mixture density model.
MDPose: Real-Time Multi-Person Pose Estimation via Mixture Density Model
In this work, we propose a novel framework of single-stage instance-aware pose estimation by modeling the joint distribution of human keypoints with a mixture density model, termed as MDPose.
End-to-End Multi-Object Detection with a Regularized Mixture Model
Recent end-to-end multi-object detectors simplify the inference pipeline by removing hand-crafted processes such as non-maximum suppression (NMS).
MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing label information.
MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing label information.
