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Found 9 papers, 2 papers with code
Customizing Segmentation Foundation Model via Prompt Learning for Instance Segmentation
Recently, foundation models trained on massive datasets to adapt to a wide range of domains have attracted considerable attention and are actively being explored within the computer vision community.
Face Shape-Guided Deep Feature Alignment for Face Recognition Robust to Face Misalignment
This is mainly attributed to the mismatch between training and testing sets.
Position-aware Location Regression Network for Temporal Video Grounding
The key to successful grounding for video surveillance is to understand a semantic phrase corresponding to important actors and objects.
Robust Pedestrian Attribute Recognition Using Group Sparsity for Occlusion Videos
To solve the uncorrelated attention issue, we also propose a novel group sparsity-based temporal attention module.
Diverse Temporal Aggregation and Depthwise Spatiotemporal Factorization for Efficient Video Classification
By using the proposed temporal modeling method (T-OSA), and the efficient factorized component (D(2+1)D), we construct two types of VoV3D networks, VoV3D-M and VoV3D-L.
Ranked #29 on
Action Recognition
on Something-Something V1
(using extra training data)
Localization Uncertainty Estimation for Anchor-Free Object Detection
To tackle these limitations, we propose a new localization uncertainty estimation method called UAD for anchor-free object detection.
Ranked #116 on
Object Detection
on COCO test-dev
Skeleton-based Action Recognition of People Handling Objects
In this paper, to address this problem, we propose a new framework for recognizing object-related human actions by graph convolutional networks using human and object poses.
Ranked #1 on
Action Recognition
on IRD
Action-Decision Networks for Visual Tracking With Deep Reinforcement Learning
In contrast to the existing trackers using deep networks, the proposed tracker is designed to achieve a light computation as well as satisfactory tracking accuracy in both location and scale.
Visual Path Prediction in Complex Scenes With Crowded Moving Objects
In this paper, we consider moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects.
