355 citations · 457 across the 26 of their papers we have counts for
13 papers · 1 filter
LISA: Reasoning Segmentation via Large Language Model
Xin Lai, Zhuotao Tian, Yukang Chen +4
Although perception systems have made remarkable advancements in recent years, they still rely on explicit human instruction or pre-defined categories to identify the target object…
Not All Steps are Created Equal: Selective Diffusion Distillation for Image Manipulation
Luozhou Wang, Shuai Yang, Shu Liu +1
Conditional diffusion models have demonstrated impressive performance in image manipulation tasks. The general pipeline involves adding noise to the image and then denoising it. Ho…
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstract
Bohao Peng, Zhuotao Tian, Xiaoyang Wu +4
Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations. Previous methods limited to the semantic featu…
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation
Bohao Peng, Zhuotao Tian, Xiaoyang Wu +4
Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations. Previous methods limited to the semantic featu…
Learning Context-aware Classifier for Semantic Segmentation
Zhuotao Tian, Jiequan Cui, Li Jiang +5
Semantic segmentation is still a challenging task for parsing diverse contexts in different scenes, thus the fixed classifier might not be able to well address varying feature dist…
Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Reflection
Wenhang Ge, Tao Hu, Haoyu Zhao +2
Neural implicit surface learning has shown significant progress in multi-view 3D reconstruction, where an object is represented by multilayer perceptrons that provide continuous im…
Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
Jingyao Li, Pengguang Chen, Shaozuo Yu +3
The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID) representation, which is distinguishable from OOD samples. Previous work applied recognition-ba…
Lightweight Facial Attractiveness Prediction Using Dual Label Distribution
Shu Liu, Enquan Huang, Ziyu Zhou +4
Facial attractiveness prediction (FAP) aims to assess facial attractiveness automatically based on human aesthetic perception. Previous methods using deep convolutional neural netw…
Generalized Parametric Contrastive Learning
Jiequan Cui, Zhisheng Zhong, Zhuotao Tian +3
In this paper, we propose the Generalized Parametric Contrastive Learning (GPaCo/PaCo) which works well on both imbalanced and balanced data. Based on theoretical analysis, we obse…
DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation
Xin Lai, Zhuotao Tian, Xiaogang Xu +5
Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations. It leverages a labeled source domain dataset…
Sequential Context Encoding for Duplicate Removal
Lu Qi, Shu Liu, Jianping Shi +1
Duplicate removal is a critical step to accomplish a reasonable amount of predictions in prevalent proposal-based object detection frameworks. Albeit simple and effective, most pre…
Path Aggregation Network for Instance Segmentation
Shu Liu, Lu Qi, Haifang Qin +2
The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in pro…
Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture
Guang Chen, Shu Liu, Kejia Ren +4
The rapid growth of IoT era is shaping the future of mobile services. Advanced communication technology enables a heterogeneous connectivity where mobile devices broadcast informat…