Search Results for author:
Found 22 papers, 8 papers with code
Adaptive Rotated Convolution for Rotated Object Detection
In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images, and an efficient conditional computation mechanism is introduced to accommodate the large orientation variations of objects within an image.
Ranked #3 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
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.
Unsupervised Learning of Accurate Siamese Tracking
As unlimited self-supervision signals can be obtained by tracking a video along a cycle in time, we investigate evolving a Siamese tracker by tracking videos forward-backward.
ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generation
We present a GAN-based Transformer for general action-conditioned 3D human motion generation, including not only single-person actions but also multi-person interactive actions.
Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking
Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive biases has recently drawn extensive interest.
Learning Video Representations of Human Motion From Synthetic Data
In this paper, we take an early step towards video representation learning of human actions with the help of largescale synthetic videos, particularly for human motion representation enhancement.
Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection
Different from pixel-based anomaly detection methods, pose-based methods utilize highly-structured skeleton data, which decreases the computational burden and also avoids the negative impact of background noise.
Anomaly Detection In Surveillance Videos
Optical Flow Estimation
+1
Transferable Knowledge-Based Multi-Granularity Aggregation Network for Temporal Action Localization: Submission to ActivityNet Challenge 2021
This technical report presents an overview of our solution used in the submission to 2021 HACS Temporal Action Localization Challenge on both Supervised Learning Track and Weakly-Supervised Learning Track.
TSI: Temporal Saliency Integration for Video Action Recognition
Specifically, SME aims to highlight the motion-sensitive area through local-global motion modeling, where the saliency alignment and pyramidal feature difference are conducted successively between neighboring frames to capture motion dynamics with less noises caused by misaligned background.
Temporal Context Aggregation Network for Temporal Action Proposal Refinement
In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement.
Ranked #9 on
Temporal Action Localization
on ActivityNet-1.3
Real-Time Visual Object Tracking via Few-Shot Learning
Visual Object Tracking (VOT) can be seen as an extended task of Few-Shot Learning (FSL).
Higher Performance Visual Tracking with Dual-Modal Localization
Visual Object Tracking (VOT) has synchronous needs for both robustness and accuracy.
Learning Statistical Texture for Semantic Segmentation
In this paper, we fully take advantages of the low-level texture features and propose a novel Statistical Texture Learning Network (STLNet) for semantic segmentation.
Context-Aware Graph Convolution Network for Target Re-identification
Most existing re-identification methods focus on learning robust and discriminative features with deep convolution networks.
SAMOT: Switcher-Aware Multi-Object Tracking and Still Another MOT Measure
Besides, we propose a new MOT evaluation measure, Still Another IDF score (SAIDF), aiming to focus more on identity issues. This new measure may overcome some problems of the previous measures and provide a better insight for identity issues in MOT.
BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation
In this paper, we present BSN++, a new framework which exploits complementary boundary regressor and relation modeling for temporal proposal generation.
Collaborative Distillation in the Parameter and Spectrum Domains for Video Action Recognition
Second, the parameter frequency distribution is further adopted to guide the student network to learn the appearance modeling process from the teacher.
Class-wise Dynamic Graph Convolution for Semantic Segmentation
Specifically, the CDGC module takes the coarse segmentation result as class mask to extract node features for graph construction and performs dynamic graph convolutions on the constructed graph to learn the feature aggregation and weight allocation.
Hierarchical Feature Embedding for Attribute Recognition
Attribute recognition is a crucial but challenging task due to viewpoint changes, illumination variations and appearance diversities, etc.
STM: SpatioTemporal and Motion Encoding for Action Recognition
Spatiotemporal and motion features are two complementary and crucial information for video action recognition.
Ranked #1 on
Action Recognition In Videos
on HMDB-51
Dynamic Curriculum Learning for Imbalanced Data Classification
Human attribute analysis is a challenging task in the field of computer vision, since the data is largely imbalance-distributed.
