Search Results for author:
Found 27 papers, 14 papers with code
Skeleton Ground Truth Extraction: Methodology, Annotation Tool and Benchmarks
However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards.
Few-shot Action Recognition via Intra- and Inter-Video Information Maximization
The alignment model performs temporal and spatial action alignment sequentially at the feature level, leading to more precise measurements of inter-video similarity.
Spatio-Temporal Point Process for Multiple Object Tracking
As such, we propose a novel framework that can effectively predict and mask-out the noisy and confusing detection results before associating the objects into trajectories.
ERNet: Efficient and Reliable Human-Object Interaction Detection
Human-Object Interaction (HOI) detection recognizes how persons interact with objects, which is advantageous in autonomous systems such as self-driving vehicles and collaborative robots.
Ranked #7 on
Human-Object Interaction Detection
on HICO-DET
Task-adaptive Spatial-Temporal Video Sampler for Few-shot Action Recognition
In this paper, we propose a novel video frame sampler for few-shot action recognition to address this issue, where task-specific spatial-temporal frame sampling is achieved via a temporal selector (TS) and a spatial amplifier (SA).
Controllable Augmentations for Video Representation Learning
The major reason is that the positive pairs, i. e., different clips sampled from the same video, have limited temporal receptive field, and usually share similar background but differ in motions.
Speed Up Object Detection on Gigapixel-Level Images With Patch Arrangement
With the appearance of super high-resolution (e. g., gigapixel-level) images, performing efficient object detection on such images becomes an important issue.
Exploring the Semi-supervised Video Object Segmentation Problem from a Cyclic Perspective
We conduct comprehensive comparison and detailed analysis on challenging benchmarks of DAVIS16, DAVIS17 and Youtube-VOS, demonstrating that the cyclic mechanism is helpful to enhance segmentation quality, improve the robustness of VOS systems, and further provide qualitative comparison and interpretation on how different VOS algorithms work.
Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization
The crux of self-supervised video representation learning is to build general features from unlabeled videos.
TA2N: Two-Stage Action Alignment Network for Few-shot Action Recognition
The first stage locates the action by learning a temporal affine transform, which warps each video feature to its action duration while dismissing the action-irrelevant feature (e. g. background).
Shallow Optical Flow Three-Stream CNN for Macro- and Micro-Expression Spotting from Long Videos
Facial expressions vary from the visible to the subtle.
Variational Pedestrian Detection
Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the current IoU-based ground truth assignment procedure in classical object detection methods.
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
In this paper, we address several inadequacies of current video object segmentation pipelines.
Finding Action Tubes with a Sparse-to-Dense Framework
The task of spatial-temporal action detection has attracted increasing attention among researchers.
Ranked #3 on
Action Detection
on UCF Sports
(Video-mAP 0.2 metric)
CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization
Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization.
AP-Loss for Accurate One-Stage Object Detection
This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem.
PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments
The experimental results show that PIoU loss can dramatically improve the performance of OBB detectors, particularly on objects with high aspect ratios and complex backgrounds.
Dual-stream shallow networks for facial micro-expression recognition
Micro-expressions are spontaneous, brief and subtle facial muscle movements that exposes underlying emotions.
Group Re-Identification with Multi-grained Matching and Integration
The task of re-identifying groups of people underdifferent camera views is an important yet less-studied problem. Group re-identification (Re-ID) is a very challenging task sinceit is not only adversely affected by common issues in traditionalsingle object Re-ID problems such as viewpoint and human posevariations, but it also suffers from changes in group layout andgroup membership.
Towards Accurate One-Stage Object Detection with AP-Loss
For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks.
Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition
In the recent year, state-of-the-art for facial micro-expression recognition have been significantly advanced by deep neural networks.
Micro Expression Recognition
Micro-Expression Recognition
+1
A Survey of Automatic Facial Micro-expression Analysis: Databases, Methods and Challenges
Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems.
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
Facial micro-expression (ME) recognition has posed a huge challenge to researchers for its subtlety in motion and limited databases.
Lost in Time: Temporal Analytics for Long-Term Video Surveillance
Video surveillance is a well researched area of study with substantial work done in the aspects of object detection, tracking and behavior analysis.
Spontaneous Subtle Expression Detection and Recognition based on Facial Strain
In this paper, we present a novel method for detecting and recognizing micro-expressions by utilizing facial optical strain magnitudes to construct optical strain features and optical strain weighted features.
Less is More: Micro-expression Recognition from Video using Apex Frame
The apex frame of a video contains the highest intensity of expression changes among all frames, while the onset is the perfect choice of a reference frame with neutral expression.
Micro Expression Recognition
Micro-Expression Recognition
+1
Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application
Spontaneous subtle emotions are expressed through micro-expressions, which are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great challenge for visual recognition.
