Search Results for author:
Found 25 papers, 4 papers with code
MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs
We present MotionVideoGAN, a novel video generator synthesizing videos based on the motion space learned by pre-trained image pair generators.
Gestalt-Guided Image Understanding for Few-Shot Learning
Due to the scarcity of available data, deep learning does not perform well on few-shot learning tasks.
Distribution Aligned Feature Clustering for Zero-Shot Sketch-Based Image Retrieval
Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a challenging cross-modal retrieval task.
Few-shot Image Generation with Diffusion Models
Then we fine-tune DDPMs pre-trained on large source domains to solve the overfitting problem when training data is limited.
Few-shot Image Generation via Masked Discrimination
It strengthens global image discrimination and guides adapted GANs to preserve more information learned from source domains for higher image quality.
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
We motivate our paradigm through a variational perspective, where the learning objective can be decomposed into two terms: task learning on the current task distribution, and curriculum update to a new task distribution.
Video Frame Interpolation via Structure-Motion based Iterative Fusion
Inspired by the observation that audiences have different visual preferences on foreground and background objects, we for the first time propose to use saliency masks in the evaluation processes of the task of video frame interpolation.
Defending Against Universal Adversarial Patches by Clipping Feature Norms
Physical-world adversarial attacks based on universal adversarial patches have been proved to be able to mislead deep convolutional neural networks (CNNs), exposing the vulnerability of real-world visual classification systems based on CNNs.
Unsupervised segmentation via semantic-apparent feature fusion
In order to solve this problem, the research proposes an unsupervised foreground segmentation method based on semantic-apparent feature fusion (SAFF).
Realizing Pixel-Level Semantic Learning in Complex Driving Scenes based on Only One Annotated Pixel per Class
Semantic segmentation tasks based on weakly supervised condition have been put forward to achieve a lightweight labeling process.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning
In this paper, we propose an iterative algorithm to learn such pairwise relations, which consists of two branches, a unary segmentation network which learns the label probabilities for each pixel, and a pairwise affinity network which learns affinity matrix and refines the probability map generated from the unary network.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Semantic Head Enhanced Pedestrian Detection in a Crowd
Pedestrian detection in the crowd is a challenging task because of intra-class occlusion.
Occluded Pedestrian Detection with Visible IoU and Box Sign Predictor
Training a robust classifier and an accurate box regressor are difficult for occluded pedestrian detection.
WSOD with PSNet and Box Regression
To solve this problem, we added the box regression module to the weakly supervised object detection network and proposed a proposal scoring network (PSNet) to supervise it.
Ranked #16 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
Pretrain Soft Q-Learning with Imperfect Demonstrations
Pretraining reinforcement learning methods with demonstrations has been an important concept in the study of reinforcement learning since a large amount of computing power is spent on online simulations with existing reinforcement learning algorithms.
Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features
Then in the top-down step, the refined object regions are used as supervision to train the segmentation network and to predict object masks.
General Classification
Weakly supervised Semantic Segmentation
+1
Driving maneuvers prediction based on cognition-driven and data-driven method
To overcome this challenge, we propose a novel method that combines both the cognition-driven model and the data-driven model.
Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations
We apply our method to two of the typical actor-critic reinforcement learning algorithms, DDPG and ACER, and demonstrate with experiments that our method not only outperforms the RL algorithms without pretraining process, but also is more simulation efficient.
Single Image Action Recognition using Semantic Body Part Actions
Second, for each body parts, a Part Action Res-Net is used to predict semantic body part actions.
Multi-View 3D Object Detection Network for Autonomous Driving
We encode the sparse 3D point cloud with a compact multi-view representation.
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection.
3D Object Proposals using Stereo Imagery for Accurate Object Class Detection
We then exploit a CNN on top of these proposals to perform object detection.
Monocular 3D Object Detection for Autonomous Driving
The focus of this paper is on proposal generation.
Ranked #8 on
Vehicle Pose Estimation
on KITTI Cars Hard
3D Object Proposals for Accurate Object Class Detection
The goal of this paper is to generate high-quality 3D object proposals in the context of autonomous driving.
Ranked #10 on
Vehicle Pose Estimation
on KITTI Cars Hard
Improving Object Proposals With Multi-Thresholding Straddling Expansion
Based on the characteristics of superpixel tightness distribution, we propose an effective method, namely multi-thresholding straddling expansion (MTSE) to reduce localization bias via fast diversification.
