Search Results for author:
Found 28 papers, 16 papers with code
Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation
This paper addresses text-supervised semantic segmentation, aiming to learn a model capable of segmenting arbitrary visual concepts within images by using only image-text pairs without dense annotations.
CTGAN: Semantic-guided Conditional Texture Generator for 3D Shapes
The entertainment industry relies on 3D visual content to create immersive experiences, but traditional methods for creating textured 3D models can be time-consuming and subjective.
2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision
Considering the high annotation cost of point clouds, effective 2D and 3D feature fusion based on weakly supervised learning is in great demand.
Robust Dynamic Radiance Fields
Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene.
Physically Plausible Animation of Human Upper Body from a Single Image
We present a new method for generating controllable, dynamically responsive, and photorealistic human animations.
NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets
Our proposed NeighborTrack takes advantage of unoccluded neighbors' information to reconfirm the tracking target and reduces false tracking when the target is occluded.
Ranked #1 on
Visual Object Tracking
on UAV123
SearchTrack: Multiple Object Tracking with Object-Customized Search and Motion-Aware Features
For each object, a customized fully convolutional search engine is created by SearchTrack by learning a set of weights for dynamic convolutions specific to the object.
An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation
We address weakly supervised point cloud segmentation by proposing a new model, MIL-derived transformer, to mine additional supervisory signals.
Hybrid Neural Fusion for Full-frame Video Stabilization
Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views.
Unsupervised Point Cloud Object Co-Segmentation by Co-Contrastive Learning and Mutual Attention Sampling
We formulate this task as an object point sampling problem, and develop two techniques, the mutual attention module and co-contrastive learning, to enable it.
Learning to See Through Obstructions with Layered Decomposition
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions, or adherent raindrops, from a short sequence of images captured by a moving camera.
Domain-Specific Mappings for Generative Adversarial Style Transfer
For addressing this issue, this paper leverages domain-specific mappings for remapping latent features in the shared content space to domain-specific content spaces.
Deep Exposure Fusion with Deghosting via Homography Estimation and Attention Learning
Modern cameras have limited dynamic ranges and often produce images with saturated or dark regions using a single exposure.
Shadow Removal of Text Document Images by Estimating Local and Global Background Colors
Assuming that the document mainly contains texts, our method estimates the global and local background colors using statistical analysis of the whole image and local neighborhoods.
Learning to See Through Obstructions
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera.
Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization.
Attention-based View Selection Networks for Light-field Disparity Estimation
This paper introduces a novel deep network for estimating depth maps from a light field image.
Ranked #1 on
Depth Estimation
on 4D Light Field Dataset
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior
This paper presents a weakly supervised instance segmentation method that consumes training data with tight bounding box annotations.
Box-supervised Instance Segmentation
Multiple Instance Learning
+4
Illumination-Adaptive Person Re-identification
To demonstrate the illumination issue and to evaluate our model, we construct two large-scale simulated datasets with a wide range of illumination variations.
Unsupervised CNN-based Co-Saliency Detection with Graphical Optimization
In this paper, we address co-saliency detection in a set of images jointly covering objects of a specific class by an unsupervised convolutional neural network (CNN).
Deep Photo Enhancer: Unpaired Learning for Image Enhancement From Photographs With GANs
First, we augment the U-Net with global features and show that it is more effective.
Learning Deep Convolutional Networks for Demosaicing
The proposed models and the thorough experiments together demonstrate that CNN is an effective and versatile tool for solving the demosaicing problem.
DeepCD: Learning Deep Complementary Descriptors for Patch Representations
This paper presents the DeepCD framework which learns a pair of complementary descriptors jointly for a patch by employing deep learning techniques.
Deep Co-Occurrence Feature Learning for Visual Object Recognition
We tackle the three issues by introducing a new network layer, called co-occurrence layer.
Accumulated Stability Voting: A Robust Descriptor From Descriptors of Multiple Scales
Experiments on popular benchmarks demonstrate the effectiveness of our descriptors and their superiority to the state-of-the-art descriptors.
Blur Kernel Estimation Using Normalized Color-Line Prior
The intermediate patches can then guide the estimation of the blur kernel.
Robust Image Alignment With Multiple Feature Descriptors and Matching-Guided Neighborhoods
First, the performance of descriptor-based approaches to image alignment relies on the chosen descriptor, but the optimal descriptor typically varies from image to image, or even pixel to pixel.
Shape-Preserving Half-Projective Warps for Image Stitching
It provides good alignment accuracy as projective warps while preserving the perspective of individual image as similarity warps.
