Search Results for author:
Found 7 papers, 3 papers with code
Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint in a Driving Scene
Self-driving cars relying solely on ego-centric perception face limitations in sensing, often failing to detect occluded, faraway objects.
Static Segmentation by Tracking: A Frustratingly Label-Efficient Approach to Fine-Grained Segmentation
Powered by Segment Anything Model 2 (SAM~2) initially developed for video segmentation, we show that SST can achieve high-quality trait and part segmentation with merely one labeled image per species -- a breakthrough for analyzing specimen images.
Learning 3D Perception from Others' Predictions
We investigate a new scenario to construct 3D object detectors: learning from the predictions of a nearby unit that is equipped with an accurate detector.
Semantic-Aware Dynamic Parameter for Video Inpainting Transformer
Recent learning-based video inpainting approaches have achieved considerable progress.
Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution
Recent transformer-based super-resolution (SR) methods have achieved promising results against conventional CNN-based methods.
Self-Supervised Adaptation for Video Super-Resolution
Recent single-image super-resolution (SISR) networks, which can adapt their network parameters to specific input images, have shown promising results by exploiting the information available within the input data as well as large external datasets.
Fast Adaptation to Super-Resolution Networks via Meta-Learning
In the training stage, we train the network via meta-learning; thus, the network can quickly adapt to any input image at test time.
