Search Results for author:
Found 49 papers, 20 papers with code
Inverse Rendering of Glossy Objects via the Neural Plenoptic Function and Radiance Fields
Our method has two stages: the geometry of the target object and the pre-filtered environmental radiance fields are reconstructed in the first stage, and materials of the target object are estimated in the second stage with the proposed NeP and material-aware cone sampling strategy.
ThemeStation: Generating Theme-Aware 3D Assets from Few Exemplars
To this end, we design a two-stage framework that draws a concept image first, followed by a reference-informed 3D modeling stage.
Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks
Diffusion models trained on large-scale datasets have achieved remarkable progress in image synthesis.
RelayAttention for Efficient Large Language Model Serving with Long System Prompts
Practical large language model (LLM) services may involve a long system prompt, which specifies the instructions, examples, and knowledge documents of the task and is reused across numerous requests.
Delving into Dark Regions for Robust Shadow Detection
Our key insight to this problem is that existing methods typically learn discriminative shadow features from the whole image globally, covering the full range of intensity values, and may not learn the subtle differences between shadow and non-shadow pixels in dark regions.
Recasting Regional Lighting for Shadow Removal
Based on this observation, we propose to condition the restoration of attenuated textures on the corrected local lighting in the shadow region.
DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior
The priors are then regarded as input conditions to maintain reasonable geometries, in which conditional LoRA and weighted score are further proposed to optimize detailed textures.
TextField3D: Towards Enhancing Open-Vocabulary 3D Generation with Noisy Text Fields
To this end, an NTFGen module is proposed to model general text latent code in noisy fields.
Language-based Photo Color Adjustment for Graphic Designs
We also introduce an approach for generating a synthetic graphic design dataset with instructions to enable model training.
Neural Preset for Color Style Transfer
In this paper, we present a Neural Preset technique to address the limitations of existing color style transfer methods, including visual artifacts, vast memory requirement, and slow style switching speed.
Structure-Informed Shadow Removal Networks
Hence, in this paper, we propose to remove shadows at the image structure level.
Efficient Mirror Detection via Multi-level Heterogeneous Learning
We present HetNet (Multi-level \textbf{Het}erogeneous \textbf{Net}work), a highly efficient mirror detection network.
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training
To address this issue, we propose CLIP2Point, an image-depth pre-training method by contrastive learning to transfer CLIP to the 3D domain, and adapt it to point cloud classification.
Ranked #3 on
Training-free 3D Point Cloud Classification
on ScanObjectNN
(using extra training data)
Rain Removal from Light Field Images with 4D Convolution and Multi-scale Gaussian Process
However, making full use of the abundant information available from LFIs, such as 2D array of sub-views and the disparity map of each sub-view, for effective rain removal is still a challenging problem.
Weakly-Supervised Camouflaged Object Detection with Scribble Annotations
To achieve this, we first relabel 4, 040 images in existing camouflaged object datasets with scribbles, which takes ~10s to label one image.
Symmetry-Aware Transformer-based Mirror Detection
Mirror detection aims to identify the mirror regions in the given input image.
Harmonizer: Learning to Perform White-Box Image and Video Harmonization
Unlike prior methods that are based on black-box autoencoders, Harmonizer contains a neural network for filter argument prediction and several white-box filters (based on the predicted arguments) for image harmonization.
Ranked #7 on
Image Harmonization
on iHarmony4
Depth-aware Glass Surface Detection with Cross-modal Context Mining
This however poses substantial challenges on the operations of autonomous systems such as robots, self-driving cars and drones, as the glass panels can become transparent obstacles to the navigation. Existing works attempt to exploit various cues, including glass boundary context or reflections, as a prior.
Bi-directional Object-context Prioritization Learning for Saliency Ranking
We observe that spatial attention works concurrently with object-based attention in the human visual recognition system.
Geometry-aware Two-scale PIFu Representation for Human Reconstruction
Our key idea is to exploit the complementary properties of depth denoising and 3D reconstruction, for learning a two-scale PIFu representation to reconstruct high-frequency facial details and consistent bodies separately.
Learning to Detect Instance-level Salient Objects Using Complementary Image Labels
However, it is non-trivial to use only class labels to learn instance-aware saliency information, as salient instances with high semantic affinities may not be easily separated by the labels.
MODNet-V: Improving Portrait Video Matting via Background Restoration
In this work, we observe that instead of asking the user to explicitly provide a background image, we may recover it from the input video itself.
Smart Scribbles for Image Mating
Image matting is an ill-posed problem that usually requires additional user input, such as trimaps or scribbles.
Automatic Comic Generation with Stylistic Multi-page Layouts and Emotion-driven Text Balloon Generation
Finally, as opposed to using the same type of balloon as in previous works, we propose an emotion-aware balloon generation method to create different types of word balloons by analyzing the emotion of subtitles and audios.
Weakly-Supervised Saliency Detection via Salient Object Subitizing
This allows the supervision to be aligned with the property of saliency detection, where the salient objects of an image could be from more than one class.
Location-aware Single Image Reflection Removal
It is beneficial to strong reflection detection and substantially improves the quality of reflection removal results.
MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition
MODNet is easy to be trained in an end-to-end manner.
Ranked #1 on
Image Matting
on PPM-100
Weakly-supervised Salient Instance Detection
Inspired by this insight, we propose to use class and subitizing labels as weak supervision for the SID problem.
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning
Although SSL methods have achieved impressive results in image classification, the performances of applying them to pixel-wise tasks are unsatisfactory due to their need for dense outputs.
HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions
To address these two problems, we propose in this paper a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images.
What makes instance discrimination good for transfer learning?
Contrastive visual pretraining based on the instance discrimination pretext task has made significant progress.
Distilling Localization for Self-Supervised Representation Learning
To address this problem, we propose a data-driven approach for learning invariance to backgrounds.
Night-time Scene Parsing with a Large Real Dataset
Although huge progress has been made on scene analysis in recent years, most existing works assume the input images to be in day-time with good lighting conditions.
Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning
In this work, we show that the coupled EMA teacher causes a performance bottleneck.
Semi-Supervised Image Classification
Unsupervised Domain Adaptation
Where Is My Mirror?
To the best of our knowledge, this is the first work to address the mirror segmentation problem with a computational approach.
Deformable Object Tracking with Gated Fusion
In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance.
Task-driven Webpage Saliency
Inspired by the observation that given a specific task, human attention is strongly correlated with certain semantic components on a webpage (e. g., images, buttons and input boxes), our network explicitly disentangles saliency prediction into two independent sub-tasks: task-specific attention shift prediction and task-free saliency prediction.
Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks
The proposed network is composed of three deep convolutional neural networks (CNNs) and a recurrent neural network (RNN).
Ranked #10 on
Deblurring
on RealBlur-R (trained on GoPro)
(SSIM (sRGB) metric)
Egocentric Hand Detection Via Dynamic Region Growing
Egocentric videos, which mainly record the activities carried out by the users of the wearable cameras, have drawn much research attentions in recent years.
Delving Into Salient Object Subitizing and Detection
Experiments show that the proposed multi-task network outperforms existing multi-task architectures, and the auxiliary subitizing network provides strong guidance to salient object detection by reducing false positives and producing coherent saliency maps.
DeshadowNet: A Multi-Context Embedding Deep Network for Shadow Removal
Two levels of features are derived from the global network and transferred to two parallel networks.
Exemplar-Driven Top-Down Saliency Detection via Deep Association
To address it, we design a two-stage deep model to learn the intra-class association between the exemplars and query objects.
Exemplar-AMMs: Recognizing Crowd Movements from Pedestrian Trajectories
Specifically, we propose an optimization framework that filters out the unknown noise in the crowd trajectories and measures their similarity to the exemplar-AMMs to produce a crowd motion feature.
Oriented Object Proposals
In this paper, we propose a new approach to generate oriented object proposals (OOPs) to reduce the detection error caused by various orientations of the object.
Leveraging Long-Term Predictions and Online-Learning in Agent-based Multiple Person Tracking
We present a multiple-person tracking algorithm, based on combining particle filters and RVO, an agent-based crowd model that infers collision-free velocities so as to predict pedestrian's motion.
A Robust Panel Extraction Method for Manga
Automatically extracting frames/panels from digital comic pages is crucial for techniques that facilitate comic reading on mobile devices with limited display areas.
Visual Tracking via Locality Sensitive Histograms
A robust tracking framework based on the locality sensitive histograms is proposed, which consists of two main components: a new feature for tracking that is robust to illumination changes and a novel multi-region tracking algorithm that runs in realtime even with hundreds of regions.
