Search Results for author:
Found 17 papers, 5 papers with code
SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation
The 2D heatmap-based approaches have dominated Human Pose Estimation (HPE) for years due to high performance.
TokenPose: Learning Keypoint Tokens for Human Pose Estimation
Most existing CNN-based methods do well in visual representation, however, lacking in the ability to explicitly learn the constraint relationships between keypoints.
Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware Association
This paper presents a new method to solve keypoint detection and instance association by using Transformer.
Ranked #10 on
Multi-Person Pose Estimation
on MS COCO
Generating Unrestricted 3D Adversarial Point Clouds
The unrestricted adversarial attack loss is incorporated in the special adversarial training of GAN, which enables the generator to generate the adversarial examples to spoof the target network.
Improved phase-unwrapping method using geometric constraints
In this paper, we proposed an improved method, which eliminates the system calibration and determination in Zhang's method, meanwhile does not need to use the low frequency fringe pattern.
Enhanced high dynamic range 3D shape measurement based on generalized phase-shifting algorithm
We proposed a method for enhanced high dynamic range 3D shape measurement based on generalized phase-shifting algorithm, which combines the complementary technique of inverted and regular fringe patterns with generalized phase-shifting algorithm.
Hidden Backdoor Attack against Semantic Segmentation Models
Deep neural networks (DNNs) are vulnerable to the \emph{backdoor attack}, which intends to embed hidden backdoors in DNNs by poisoning training data.
Physical-World Optical Adversarial Attacks on 3D Face Recognition
2D face recognition has been proven insecure for physical adversarial attacks.
Generating Transferable and Stealthy Adversarial Patch via Attention-guided Adversarial Inpainting
In the first stage, we extract multi-scale style embeddings by a pyramid-like network and identity embeddings by a pretrained FR model and propose a novel Attention-guided Adaptive Instance Normalization layer (AAIN) to merge them via background-patch cross-attention maps.
A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data
Instead of searching for expressions within a large search space, we explore DySymNet with various structures and optimize them to identify expressions that better-fitting the data.
A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attacks
Lots of papers have emerged to investigate the robustness and safety of deep learning models against adversarial attacks.
MetaSymNet: A Dynamic Symbolic Regression Network Capable of Evolving into Arbitrary Formulations
To address these issues, we propose MetaSymNet, a novel neural network that dynamically adjusts its structure in real-time, allowing for both expansion and contraction.
A Novel Paradigm for Neural Computation: X-Net with Learnable Neurons and Adaptable Structure
1, The type of activation function is single and relatively fixed, which leads to poor "unit representation ability" of the network, and it is often used to solve simple problems with very complex networks; 2, the network structure is not adaptive, it is easy to cause network structure redundant or insufficient.
Discovering Mathematical Formulas from Data via GPT-guided Monte Carlo Tree Search
To optimize the trade-off between efficiency and versatility, we introduce SR-GPT, a novel algorithm for symbolic regression that integrates Monte Carlo Tree Search (MCTS) with a Generative Pre-Trained Transformer (GPT).
PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regression
Therefore, a greedy pruning algorithm is proposed to prune the network into a subnetwork while ensuring the accuracy of data fitting.
MMSR: Symbolic Regression is a Multimodal Task
The SR problem is solved as a pure multimodal problem, and contrastive learning is also introduced in the training process for modal alignment to facilitate later modal feature fusion.
Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning
However, its performance is very dependent on the training data and performs poorly on data outside the training set, which leads to poor noise robustness and Versatility of such methods.
