Search Results for author:
Found 28 papers, 13 papers with code
Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model
However, a large portion of videos in real-world applications are edited videos, \textit{e. g.}, users usually cut and add effects/modifications to the raw video before publishing it on social media platforms.
Open Set Action Recognition via Multi-Label Evidential Learning
Existing methods for open-set action recognition focus on novelty detection that assumes video clips show a single action, which is unrealistic in the real world.
Human Activity Recognition in an Open World
Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples.
Reconstructing Humpty Dumpty: Multi-feature Graph Autoencoder for Open Set Action Recognition
Our approach uses the reconstruction error to determine the novelty of the video since unknown classes are harder to put back together and thus have a higher reconstruction error than videos from known classes.
MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification
In this paper, we present the Multi-view Extended Videos with Identities (MEVID) dataset for large-scale, video person re-identification (ReID) in the wild.
Unbiased Multi-Modality Guidance for Image Inpainting
Image inpainting is an ill-posed problem to recover missing or damaged image content based on incomplete images with masks.
Multi-Granularity Alignment Domain Adaptation for Object Detection
Domain adaptive object detection is challenging due to distinctive data distribution between source domain and target domain.
Cascade Transformers for End-to-End Person Search
In this paper, we propose the Cascade Occluded Attention Transformer (COAT) for end-to-end person search.
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.
Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark
To promote the developments of object detection, tracking and counting algorithms in drone-captured videos, we construct a benchmark with a new drone-captured largescale dataset, named as DroneCrowd, formed by 112 video clips with 33, 600 HD frames in various scenarios.
Spatial Attention Pyramid Network for Unsupervised Domain Adaptation
Specifically, we first build the spatial pyramid representation to capture context information of objects at different scales.
Rethinking Object Detection in Retail Stores
In this paper, we propose a new task, ie, simultaneously object localization and counting, abbreviated as Locount, which requires algorithms to localize groups of objects of interest with the number of instances.
Multi-Drone based Single Object Tracking with Agent Sharing Network
Moreover, an agent sharing network (ASNet) is proposed by self-supervised template sharing and view-aware fusion of the target from multiple drones, which can improve the tracking accuracy significantly compared with single drone tracking.
Detection and Tracking Meet Drones Challenge
We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i. e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking.
Learning Semantic Neural Tree for Human Parsing
In this paper, we design a novel semantic neural tree for human parsing, which uses a tree architecture to encode physiological structure of human body, and designs a coarse to fine process in a cascade manner to generate accurate results.
SiamMan: Siamese Motion-aware Network for Visual Tracking
To reduce the impact of manually designed anchor boxes to adapt to different target motion patterns, we design the localization branch, which aims to coarsely localize the target to help the regression branch to generate accurate results.
Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network
This paper proposes a space-time multi-scale attention network (STANet) to solve density map estimation, localization and tracking in dense crowds of video clips captured by drones with arbitrary crowd density, perspective, and flight altitude.
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
Guided Attention Network for Object Detection and Counting on Drones
Object detection and counting are related but challenging problems, especially for drone based scenes with small objects and cluttered background.
Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization
Specifically, we incorporate convolutional operations along edges of the tree structure, and use the routing functions in each node to determine the root-to-leaf computational paths within the tree.
Ranked #35 on
Fine-Grained Image Classification
on Stanford Cars
Fine-Grained Image Classification
Fine-Grained Visual Categorization
Multi-view Deep Subspace Clustering Networks
Dnet learns view-specific self-representation matrices, whereas Unet learns a common self-representation matrix for all views.
Ranked #1 on
Multi-view Subspace Clustering
on ORL
Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently
Existing hand detection methods usually follow the pipeline of multiple stages with high computation cost, i. e., feature extraction, region proposal, bounding box regression, and additional layers for rotated region detection.
Data Priming Network for Automatic Check-Out
In this paper, we propose a new data priming method to solve the domain adaptation problem.
Learning Non-Uniform Hypergraph for Multi-Object Tracking
The majority of Multi-Object Tracking (MOT) algorithms based on the tracking-by-detection scheme do not use higher order dependencies among objects or tracklets, which makes them less effective in handling complex scenarios.
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
Selected from 10 hours raw videos, about 80, 000 representative frames are fully annotated with bounding boxes as well as up to 14 kinds of attributes (e. g., weather condition, flying altitude, camera view, vehicle category, and occlusion) for three fundamental computer vision tasks: object detection, single object tracking, and multiple object tracking.
Ranked #5 on
Object Detection
on UAVDT
Geometric Hypergraph Learning for Visual Tracking
Graph based representation is widely used in visual tracking field by finding correct correspondences between target parts in consecutive frames.
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset.
JOTS: Joint Online Tracking and Segmentation
We present a novel Joint Online Tracking and Segmentation (JOTS) algorithm which integrates the multi-part tracking and segmentation into a unified energy optimization framework to handle the video segmentation task.
