Search Results for author:
Found 14 papers, 3 papers with code
Large Batch Optimization for Object Detection: Training COCO in 12 Minutes
Most of existing object detectors usually adopt a small training batch size ( ~16), which severely hinders the whole community from exploring large-scale datasets due to the extremely long training procedure.
An Asymmetric Modeling for Action Assessment
However, existing methods for action assessment are mostly limited to individual actions, especially lacking modeling of the asymmetric relations among agents (e. g., between persons and objects); and this limitation undermines their ability to assess actions containing asymmetrically interactive motion patterns, since there always exists subordination between agents in many interactive actions.
Hybrid Dynamic-static Context-aware Attention Network for Action Assessment in Long Videos
However, most existing works focus only on video dynamic information (i. e., motion information) but ignore the specific postures that an athlete is performing in a video, which is important for action assessment in long videos.
Ranked #2 on
Action Quality Assessment
on Rhythmic Gymnastic
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing
Inspired by such capability of humans, to imitate humans' ability of learning visual primitives and composing primitives to recognize novel classes, we propose an approach to FSL to learn a feature representation composed of important primitives, which is jointly trained with two parts, i. e. primitive discovery and primitive enhancing.
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning
By solving the problem with its closed-form solution on the fly with the setup of transduction, our approach efficiently tailors an episodic-wise metric for each task to adapt all features from a shared task-agnostic embedding space into a more discriminative task-specific metric space.
P-ODN: Prototype based Open Deep Network for Open Set Recognition
Predictors for new categories are added to the classification layer to "open" the deep neural networks to incorporate new categories dynamically.
ODN: Opening the Deep Network for Open-set Action Recognition
Most of the existing action recognition works hold the \textit{closed-set} assumption that all action categories are known beforehand while deep networks can be well trained for these categories.
Exploiting Multi-Grain Ranking Constraints for Precisely Searching Visually-Similar Vehicles
In this paper, we model the relationship of vehicle images as multiple grains.
A Network Framework for Noisy Label Aggregation in Social Media
This paper focuses on the task of noisy label aggregation in social media, where users with different social or culture backgrounds may annotate invalid or malicious tags for documents.
Cultural Vocal Bursts Intensity Prediction
Image Classification
+2
Deep Transfer Learning for Person Re-identification
Second, a two-stepped fine-tuning strategy is developed to transfer knowledge from auxiliary datasets.
Learning long-term dependencies for action recognition with a biologically-inspired deep network
Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task.
Joint Network based Attention for Action Recognition
We also introduce an attention mechanism on the temporal domain to capture the long-term dependence meanwhile finding the salient portions.
Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN
Nevertheless, most of the existing features or descriptors cannot capture motion information effectively, especially for long-term motion.
Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification
Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training.
