Search Results for author:
Found 28 papers, 19 papers with code
GaussianObject: Just Taking Four Images to Get A High-Quality 3D Object with Gaussian Splatting
Then we construct a Gaussian repair model based on diffusion models to supplement the omitted object information, where Gaussians are further refined.
Fast High Dynamic Range Radiance Fields for Dynamic Scenes
To extend HDR NeRF methods to wider applications, we propose a dynamic HDR NeRF framework, named HDR-HexPlane, which can learn 3D scenes from dynamic 2D images captured with various exposures.
Cascade-Zero123: One Image to Highly Consistent 3D with Self-Prompted Nearby Views
We propose a cascade generation framework constructed with two Zero-1-to-3 models, named Cascade-Zero123, to tackle this issue, which progressively extracts 3D information from the source image.
Segment Any 3D Gaussians
Interactive 3D segmentation in radiance fields is an appealing task since its importance in 3D scene understanding and manipulation.
GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions
Specifically, we first extract the region of interest (RoI) corresponding to the text instruction, aligning it to 3D Gaussians.
4D Gaussian Splatting for Real-Time Dynamic Scene Rendering
Representing and rendering dynamic scenes has been an important but challenging task.
GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models
In recent times, the generation of 3D assets from text prompts has shown impressive results.
TiAVox: Time-aware Attenuation Voxels for Sparse-view 4D DSA Reconstruction
Additionally, 2D and 3D DSA imaging results can be generated from the reconstructed 4D DSA images.
Segment Anything in 3D with Radiance Fields
The Segment Anything Model (SAM) emerges as a powerful vision foundation model to generate high-quality 2D segmentation results.
TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors
Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightweight generic detectors.
WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation
Thus a novel weight-based method is proposed to end-to-end estimate the importance of attention heads, while the self-attention maps are adaptively fused for high-quality CAM results that tend to have more complete objects.
Weakly-supervised Learning
Weakly supervised Semantic Segmentation
+1
Generalizable Neural Voxels for Fast Human Radiance Fields
Rendering moving human bodies at free viewpoints only from a monocular video is quite a challenging problem.
Fast Dynamic Radiance Fields with Time-Aware Neural Voxels
A multi-distance interpolation method is proposed and applied on voxel features to model both small and large motions.
Temporally Efficient Vision Transformer for Video Instance Segmentation
To effectively and efficiently model the crucial temporal information within a video clip, we propose a Temporally Efficient Vision Transformer (TeViT) for video instance segmentation (VIS).
Ranked #35 on
Video Instance Segmentation
on OVIS validation
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers
The past year has witnessed a rapid development of masked image modeling (MIM).
Exploring Complicated Search Spaces with Interleaving-Free Sampling
In this paper, we build the search algorithm upon a complicated search space with long-distance connections, and show that existing weight-sharing search algorithms mostly fail due to the existence of \textbf{interleaved connections}.
NeuSample: Neural Sample Field for Efficient View Synthesis
Neural radiance fields (NeRF) have shown great potentials in representing 3D scenes and synthesizing novel views, but the computational overhead of NeRF at the inference stage is still heavy.
Semantic-Aware Generation for Self-Supervised Visual Representation Learning
In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by requiring the target network to recover the original image based on the mid-level features.
Ranked #63 on
Semantic Segmentation
on Cityscapes test
Hierarchical Aggregation for 3D Instance Segmentation
Instance segmentation on point clouds is a fundamental task in 3D scene perception.
Ranked #4 on
3D Instance Segmentation
on S3DIS
(mCov metric, using extra
training data)
Bag of Instances Aggregation Boosts Self-supervised Distillation
Here bag of instances indicates a set of similar samples constructed by the teacher and are grouped within a bag, and the goal of distillation is to aggregate compact representations over the student with respect to instances in a bag.
You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection
Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure?
Ranked #30 on
Object Detection
on COCO-O
MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens
Transformers have offered a new methodology of designing neural networks for visual recognition.
ResizeMix: Mixing Data with Preserved Object Information and True Labels
Especially, CutMix uses a simple but effective method to improve the classifiers by randomly cropping a patch from one image and pasting it on another image.
EfficientPose: Efficient Human Pose Estimation with Neural Architecture Search
Human pose estimation from image and video is a vital task in many multimedia applications.
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search
In this paper, we propose a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network (e. g. an ImageNet pre-trained network) to become a network with different depths, widths, or kernel sizes via a parameter remapping technique, making it possible to use NAS for segmentation and detection tasks a lot more efficiently.
Fast Neural Network Adaptation via Parameter Remapping and Architecture Search
In our experiments, we conduct FNA on MobileNetV2 to obtain new networks for both segmentation and detection that clearly out-perform existing networks designed both manually and by NAS.
Densely Connected Search Space for More Flexible Neural Architecture Search
We revisit the search space design in most previous NAS methods and find the number and widths of blocks are set manually.
Ranked #91 on
Neural Architecture Search
on ImageNet
EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search
In our implementations, architectures are first searched on a small dataset, e. g., CIFAR-10.
