1.4k citations · 3.6k across the 125 of their papers we have counts for
36 papers · 1 filter
Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint
Borui Zhang, Wenzhao Zheng, Jie Zhou +1
Deep learning has revolutionized human society, yet the black-box nature of deep neural networks hinders further application to reliability-demanded industries. In the attempt to u…
FLAG3D: A 3D Fitness Activity Dataset with Language Instruction
Yansong Tang, Jinpeng Liu, Aoyang Liu +6
With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision. While a variety of new tasks and alg…
Diffusion-SDF: Text-to-Shape via Voxelized Diffusion
Muheng Li, Yueqi Duan, Jie Zhou +1
With the rising industrial attention to 3D virtual modeling technology, generating novel 3D content based on specified conditions (e.g. text) has become a hot issue. In this paper,…
Token-Label Alignment for Vision Transformers
Han Xiao, Wenzhao Zheng, Zheng Zhu +2
Data mixing strategies (e.g., CutMix) have shown the ability to greatly improve the performance of convolutional neural networks (CNNs). They mix two images as inputs for training…
OPERA: Omni-Supervised Representation Learning with Hierarchical Supervisions
Chengkun Wang, Wenzhao Zheng, Zheng Zhu +2
The pretrain-finetune paradigm in modern computer vision facilitates the success of self-supervised learning, which tends to achieve better transferability than supervised learning…
Planning Irregular Object Packing via Hierarchical Reinforcement Learning
Sichao Huang, Ziwei Wang, Jie Zhou +1
Object packing by autonomous robots is an im-portant challenge in warehouses and logistics industry. Most conventional data-driven packing planning approaches focus on regular cubo…
Probabilistic Deep Metric Learning for Hyperspectral Image Classification
Chengkun Wang, Wenzhao Zheng, Xian Sun +2
This paper proposes a probabilistic deep metric learning (PDML) framework for hyperspectral image classification, which aims to predict the category of each pixel for an image capt…
Dynamics-aware Adversarial Attack of Adaptive Neural Networks
An Tao, Yueqi Duan, Yingqi Wang +2
In this paper, we investigate the dynamics-aware adversarial attack problem of adaptive neural networks. Most existing adversarial attack algorithms are designed under a basic assu…
A Simple Baseline for Multi-Camera 3D Object Detection
Yunpeng Zhang, Wenzhao Zheng, Zheng Zhu +3
3D object detection with surrounding cameras has been a promising direction for autonomous driving. In this paper, we present SimMOD, a Simple baseline for Multi-camera Object Dete…
Shap-CAM: Visual Explanations for Convolutional Neural Networks based on Shapley Value
Quan Zheng, Ziwei Wang, Jie Zhou +1
Explaining deep convolutional neural networks has been recently drawing increasing attention since it helps to understand the networks' internal operations and why they make certai…
P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting
Ziyi Wang, Xumin Yu, Yongming Rao +2
Nowadays, pre-training big models on large-scale datasets has become a crucial topic in deep learning. The pre-trained models with high representation ability and transferability a…
GE-Grasp: Efficient Target-Oriented Grasping in Dense Clutter
Zhan Liu, Ziwei Wang, Sichao Huang +2
Grasping in dense clutter is a fundamental skill for autonomous robots. However, the crowdedness and occlusions in the cluttered scenario cause significant difficulties to generate…
Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis
Shuai Shen, Wanhua Li, Zheng Zhu +3
Talking head synthesis is an emerging technology with wide applications in film dubbing, virtual avatars and online education. Recent NeRF-based methods generate more natural talki…
Label2Label: A Language Modeling Framework for Multi-Attribute Learning
Wanhua Li, Zhexuan Cao, Jianjiang Feng +2
Objects are usually associated with multiple attributes, and these attributes often exhibit high correlations. Modeling complex relationships between attributes poses a great chall…
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition
Yansong Tang, Xingyu Liu, Xumin Yu +3
Rapid progress and superior performance have been achieved for skeleton-based action recognition recently. In this article, we investigate this problem under a cross-dataset settin…
MetaAge: Meta-Learning Personalized Age Estimators
Wanhua Li, Jiwen Lu, Abudukelimu Wuerkaixi +2
Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personaliz…
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
Yongming Rao, Wenliang Zhao, Yansong Tang +3
Recent progress in vision Transformers exhibits great success in various tasks driven by the new spatial modeling mechanism based on dot-product self-attention. In this paper, we s…
Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution
Cheng Ma, Jingyi Zhang, Jie Zhou +1
Lookup table (LUT) has shown its efficacy in low-level vision tasks due to the valuable characteristics of low computational cost and hardware independence. However, recent attempt…
Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks
Yongming Rao, Zuyan Liu, Wenliang Zhao +2
In this paper, we present a new approach for model acceleration by exploiting spatial sparsity in visual data. We observe that the final prediction in vision Transformers is only b…
Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search
Han Xiao, Ziwei Wang, Zheng Zhu +2
In this paper, we propose a Shapley value based method to evaluate operation contribution (Shapley-NAS) for neural architecture search. Differentiable architecture search (DARTS) a…
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
Wanhua Li, Xiaoke Huang, Zheng Zhu +4
This paper presents a language-powered paradigm for ordinal regression. Existing methods usually treat each rank as a category and employ a set of weights to learn these concepts.…
SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation
Ziyi Wang, Yongming Rao, Xumin Yu +2
Conventional point cloud semantic segmentation methods usually employ an encoder-decoder architecture, where mid-level features are locally aggregated to extract geometric informat…
BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving
Yunpeng Zhang, Zheng Zhu, Wenzhao Zheng +4
In this paper, we present BEVerse, a unified framework for 3D perception and prediction based on multi-camera systems. Unlike existing studies focusing on the improvement of single…
Introspective Deep Metric Learning for Image Retrieval
Wenzhao Zheng, Chengkun Wang, Jie Zhou +1
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods produce confident…