Search Results for author:
Found 31 papers, 15 papers with code
Mining Inter-Video Proposal Relations for Video Object Detection
Recent studies have shown that, context aggregating information from proposals in different frames can clearly enhance the performance of video object detection.
Ranked #1 on
Video Object Detection
on ImageNet VID
Cross Domain Object Detection by Target-Perceived Dual Branch Distillation
It can adaptively enhance source detector to perceive objects in a target image, by leveraging target proposal contexts from iterative cross-attention.
Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection
Specifically, TRKP adopts the teacher-student framework, where the multi-head teacher network is built to extract knowledge from labeled source domains and guide the student network to learn detectors in unlabeled target domain.
Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition
Learning spatial-temporal relation among multiple actors is crucial for group activity recognition.
UniFormer: Unifying Convolution and Self-attention for Visual Recognition
Different from the typical transformer blocks, the relation aggregators in our UniFormer block are equipped with local and global token affinity respectively in shallow and deep layers, allowing to tackle both redundancy and dependency for efficient and effective representation learning.
Ranked #72 on
Image Classification
on ImageNet
CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning
To fill this gap, we propose a generic Contour-Perturbed Reconstruction Network (CP-Net), which can effectively guide self-supervised reconstruction to learn semantic content in the point cloud, and thus promote discriminative power of point cloud representation.
UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning
For Something-Something V1 and V2, our UniFormer achieves new state-of-the-art performances of 60. 9% and 71. 2% top-1 accuracy respectively.
Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results
Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration.
MorphMLP: A Self-Attention Free, MLP-Like Backbone for Image and Video
Self-attention has become an integral component of the recent network architectures, e. g., Transformer, that dominate major image and video benchmarks.
Ranked #11 on
Action Recognition
on Something-Something V2
(using extra training data)
Self-slimmed Vision Transformer
Vision transformers (ViTs) have become the popular structures and outperformed convolutional neural networks (CNNs) on various vision tasks.
Self-Slimming Vision Transformer
Specifically, we first design a novel Token Slimming Module (TSM), which can boost the inference efficiency of ViTs by dynamic token aggregation.
UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning
For Something-Something V1 and V2, our UniFormer achieves new state-of-the-art performances of 60. 8% and 71. 4% top-1 accuracy respectively.
Ranked #1 on
Action Recognition
on Something-Something V1
Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos
Graph Convolution Network (GCN) has been successfully used for 3D human pose estimation in videos.
Digging into Uncertainty in Self-supervised Multi-view Stereo
Specially, the limitations can be categorized into two types: ambiguious supervision in foreground and invalid supervision in background.
TE-YOLOF: Tiny and efficient YOLOF for blood cell detection
In this work, an object detector based on YOLOF has been proposed to detect blood cell objects such as red blood cells, white blood cells and platelets.
CT-Net: Channel Tensorization Network for Video Classification
It can learn to exploit spatial, temporal and channel attention in a high-dimensional manner, to improve the cooperative power of all the feature dimensions in our CT-Module.
Ranked #6 on
Action Recognition
on Something-Something V1
FineAction: A Fine-Grained Video Dataset for Temporal Action Localization
Second, the coarse action classes often lead to the ambiguous annotations of temporal boundaries, which are inappropriate for temporal action localization.
PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos
By coupling advanced 3D pose estimators and HMR in a serial or parallel manner, these two frameworks can effectively correct human mesh with guidance of a concise pose calibration module.
Automatic Preference Based Multi-objective Evolutionary Algorithm on Vehicle Fleet Maintenance Scheduling Optimization
A preference based multi-objective evolutionary algorithm is proposed for generating solutions in an automatically detected knee point region.
SmallBigNet: Integrating Core and Contextual Views for Video Classification
Our SmallBig network outperforms a number of recent state-of-the-art approaches, in terms of accuracy and/or efficiency.
Improving Many-Objective Evolutionary Algorithms by Means of Edge-Rotated Cones
Given a point in $m$-dimensional objective space, any $\varepsilon$-ball of a point can be partitioned into the incomparable, the dominated and dominating region.
A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling
A customized multi-objective evolutionary algorithm (MOEA) is proposed for the multi-objective flexible job shop scheduling problem (FJSP).
Context-Transformer: Tackling Object Confusion for Few-Shot Detection
Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors.
Learning Attentive Pairwise Interaction for Fine-Grained Classification
These distinct gate vectors inherit mutual context on semantic differences, which allow API-Net to attentively capture contrastive clues by pairwise interaction between two images.
Ranked #3 on
Fine-Grained Image Classification
on Stanford Dogs
Progressive Object Transfer Detection
Inspired by this procedure of learning to detect, we propose a novel Progressive Object Transfer Detection (POTD) framework.
Clustering Bioactive Molecules in 3D Chemical Space with Unsupervised Deep Learning
We ex-plored the superior learning capability of deep autoencoders for unsupervised clustering of 1. 39 mil-lion bioactive molecules into band-clusters in a 3-dimensional latent chemical space.
Temporal Hallucinating for Action Recognition With Few Still Images
To mimic this capacity, we propose a novel Hybrid Video Memory (HVM) machine, which can hallucinate temporal features of still images from video memory, in order to boost action recognition with few still images.
LSTD: A Low-Shot Transfer Detector for Object Detection
Second, we introduce a novel regularized transfer learning framework for low-shot detection, where the transfer knowledge (TK) and background depression (BD) regularizations are proposed to leverage object knowledge respectively from source and target domains, in order to further enhance fine-tuning with a few target images.
Ranked #14 on
Few-Shot Object Detection
on MS-COCO (30-shot)
RPAN: An End-to-End Recurrent Pose-Attention Network for Action Recognition in Videos
Firstly, unlike previous works on pose-related action recognition, our RPAN is an end-to-end recurrent network which can exploit important spatial-temporal evolutions of human pose to assist action recognition in a unified framework.
Ranked #5 on
Skeleton Based Action Recognition
on J-HMDB
Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition
In this paper, we propose a hybrid representation, which leverages the discriminative capacity of CNNs and the simplicity of descriptor encoding schema for image recognition, with a focus on scene recognition.
A Marginalized Particle Gaussian Process Regression
We present a novel marginalized particle Gaussian process (MPGP) regression, which provides a fast, accurate online Bayesian filtering framework to model the latent function.
