3 citations · 13 across the 18 of their papers we have counts for
19 papers
APGNet: Adaptive Prior-Guided for Underwater Camouflaged Object Detection
Xinxin Huang, Han Sun, Junmin Cai +2
Detecting camouflaged objects in underwater environments is crucial for marine ecological research and resource exploration. However, existing methods face two key challenges: unde…
SLENet: A Guidance-Enhanced Network for Underwater Camouflaged Object Detection
Xinxin Huang, Han Sun, Ningzhong Liu +2
Underwater Camouflaged Object Detection (UCOD) aims to identify objects that blend seamlessly into underwater environments. This task is critically important to marine ecology. How…
SliceSemOcc: Vertical Slice Based Multimodal 3D Semantic Occupancy Representation
Han Huang, Han Sun, Ningzhong Liu +2
Driven by autonomous driving's demands for precise 3D perception, 3D semantic occupancy prediction has become a pivotal research topic. Unlike bird's-eye-view (BEV) methods, which…
DCFS: Continual Test-Time Adaptation via Dual Consistency of Feature and Sample
Wenting Yin, Han Sun, Xinru Meng +2
Continual test-time adaptation aims to continuously adapt a pre-trained model to a stream of target domain data without accessing source data. Without access to source domain data,…
Energy-Based Pseudo-Label Refining for Source-free Domain Adaptation
Xinru Meng, Han Sun, Jiamei Liu +2
Source-free domain adaptation (SFDA), which involves adapting models without access to source data, is both demanding and challenging. Existing SFDA techniques typically rely on ps…
SPARNet: Continual Test-Time Adaptation via Sample Partitioning Strategy and Anti-Forgetting Regularization
Xinru Meng, Han Sun, Jiamei Liu +2
Test-time Adaptation (TTA) aims to improve model performance when the model encounters domain changes after deployment. The standard TTA mainly considers the case where the target…
B2Net: Camouflaged Object Detection via Boundary Aware and Boundary Fusion
Junmin Cai, Han Sun, Ningzhong Liu
Camouflaged object detection (COD) aims to identify objects in images that are well hidden in the environment due to their high similarity to the background in terms of texture and…
DecoratingFusion: A LiDAR-Camera Fusion Network with the Combination of Point-level and Feature-level Fusion
Zixuan Yin, Han Sun, Ningzhong Liu +2
Lidars and cameras play essential roles in autonomous driving, offering complementary information for 3D detection. The state-of-the-art fusion methods integrate them at the featur…
SIDE: Self-supervised Intermediate Domain Exploration for Source-free Domain Adaptation
Jiamei Liu, Han Sun, Yizhen Jia +3
Domain adaptation aims to alleviate the domain shift when transferring the knowledge learned from the source domain to the target domain. Due to privacy issues, source-free domain…
FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object Detection
Zixuan Yin, Han Sun, Ningzhong Liu +2
Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. While most prevalent methods progressively downscale the 3D p…
A Task-aware Dual Similarity Network for Fine-grained Few-shot Learning
Yan Qi, Han Sun, Ningzhong Liu +1
The goal of fine-grained few-shot learning is to recognize sub-categories under the same super-category by learning few labeled samples. Most of the recent approaches adopt a singl…
Polycentric Clustering and Structural Regularization for Source-free Unsupervised Domain Adaptation
Xinyu Guan, Han Sun, Ningzhong Liu +1
Source-Free Domain Adaptation (SFDA) aims to solve the domain adaptation problem by transferring the knowledge learned from a pre-trained source model to an unseen target domain. M…
Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images
Yuhan Lin, Han Sun, Ningzhong Liu +3
Due to the extreme complexity of scale and shape as well as the uncertainty of the predicted location, salient object detection in optical remote sensing images (RSI-SOD) is a very…
A lightweight multi-scale context network for salient object detection in optical remote sensing images
Yuhan Lin, Han Sun, Ningzhong Liu +3
Due to the more dramatic multi-scale variations and more complicated foregrounds and backgrounds in optical remote sensing images (RSIs), the salient object detection (SOD) for opt…
Robust Ensembling Network for Unsupervised Domain Adaptation
Han Sun, Lei Lin, Ningzhong Liu +1
Recently, in order to address the unsupervised domain adaptation (UDA) problem, extensive studies have been proposed to achieve transferrable models. Among them, the most prevalent…
Multi-scale Edge-based U-shape Network for Salient Object Detection
Han Sun, Yetong Bian, Ningzhong Liu +1
Deep-learning based salient object detection methods achieve great improvements. However, there are still problems existing in the predictions, such as blurry boundary and inaccura…
MPI: Multi-receptive and Parallel Integration for Salient Object Detection
Han Sun, Jun Cen, Ningzhong Liu +2
The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantl…
Learning Calibrated-Guidance for Object Detection in Aerial Images
Zongqi Wei, Dong Liang, Dong Zhang +4
Object detection is one of the most fundamental yet challenging research topics in the domain of computer vision. Recently, the study on this topic in aerial images has made tremen…
Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition
Lin Xu, Han Sun, Yuai Liu
Deep metric learning is essential for visual recognition. The widely used pair-wise (or triplet) based loss objectives cannot make full use of semantical information in training sa…