Search Results for author:
Found 16 papers, 6 papers with code
Mutual Exclusive Modulator for Long-Tailed Recognition
In this paper, we try to look into the root cause of the LTR task, i. e., training samples for each class are greatly imbalanced, and propose a straightforward solution.
Deep Dynamic Scene Deblurring from Optical Flow
In this paper, we explore optical flow to remove dynamic scene blur by using the multi-scale spatially variant recurrent neural network (RNN).
SparseMAE: Sparse Training Meets Masked Autoencoders
In this paper, we aim to reduce model complexity from large vision transformers pretrained by MAE with assistant of sparse training.
Fraud Dataset Benchmark and Applications
Standardized datasets and benchmarks have spurred innovations in computer vision, natural language processing, multi-modal and tabular settings.
Pyramid Fusion Transformer for Semantic Segmentation
The recently proposed MaskFormer gives a refreshed perspective on the task of semantic segmentation: it shifts from the popular pixel-level classification paradigm to a mask-level classification method.
Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation
To this end, we propose Multi-level Attention Encoder-Decoder Network (MAED), including a Spatial-Temporal Encoder (STE) and a Kinematic Topology Decoder (KTD) to model multi-level attentions in a unified framework.
Ranked #40 on
3D Human Pose Estimation
on MPI-INF-3DHP
HAN: An Efficient Hierarchical Self-Attention Network for Skeleton-Based Gesture Recognition
Previous methods for skeleton-based gesture recognition mostly arrange the skeleton sequence into a pseudo picture or spatial-temporal graph and apply deep Convolutional Neural Network (CNN) or Graph Convolutional Network (GCN) for feature extraction.
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch
In this paper, we are the first to study training from scratch an N:M fine-grained structured sparse network, which can maintain the advantages of both unstructured fine-grained sparsity and structured coarse-grained sparsity simultaneously on specifically designed GPUs.
A Holistically-Guided Decoder for Deep Representation Learning with Applications to Semantic Segmentation and Object Detection
With the proposed holistically-guided decoder, we implement the EfficientFCN architecture for semantic segmentation and HGD-FPN for object detection and instance segmentation.
EfficientFCN: Holistically-guided Decoding for Semantic Segmentation
State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN), which adopt dilated convolutions in the backbone networks to extract high-resolution feature maps for achieving high-performance segmentation performance.
Decoupled Representation Learning for Skeleton-Based Gesture Recognition
In this paper, we propose to decouple the gesture into hand posture variations and hand movements, which are then modeled separately.
Learning to Predict Context-adaptive Convolution for Semantic Segmentation
Long-range contextual information is essential for achieving high-performance semantic segmentation.
Learning Selfie-Friendly Abstraction from Artistic Style Images
Artistic style transfer can be thought as a process to generate different versions of abstraction of the original image.
LSTM Pose Machines
Such suboptimal results are mainly attributed to the inability of imposing sequential geometric consistency, handling severe image quality degradation (e. g. motion blur and occlusion) as well as the inability of capturing the temporal correlation among video frames.
Ranked #3 on
Pose Estimation
on J-HMDB
Robust Tracking Using Region Proposal Networks
Recent advances in visual tracking showed that deep Convolutional Neural Networks (CNN) trained for image classification can be strong feature extractors for discriminative trackers.
Accurate Single Stage Detector Using Recurrent Rolling Convolution
In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation.
