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Found 14 papers, 7 papers with code
Contextual Explainable Video Representation: Human Perception-based Understanding
We choose video paragraph captioning and temporal action detection to illustrate the effectiveness of human perception based-contextual representation in video understanding.
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection
Video anomaly detection (VAD) -- commonly formulated as a multiple-instance learning problem in a weakly-supervised manner due to its labor-intensive nature -- is a challenging problem in video surveillance where the frames of anomaly need to be localized in an untrimmed video.
VLTinT: Visual-Linguistic Transformer-in-Transformer for Coherent Video Paragraph Captioning
Video paragraph captioning aims to generate a multi-sentence description of an untrimmed video with several temporal event locations in coherent storytelling.
Ranked #2 on
Video Captioning
on ActivityNet Captions
AISFormer: Amodal Instance Segmentation with Transformer
AISFormer explicitly models the complex coherence between occluder, visible, amodal, and invisible masks within an object's regions of interest by treating them as learnable queries.
AOE-Net: Entities Interactions Modeling with Adaptive Attention Mechanism for Temporal Action Proposals Generation
PMR module represents each video snippet by a visual-linguistic feature, in which main actors and surrounding environment are represented by visual information, whereas relevant objects are depicted by linguistic features through an image-text model.
VLCap: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning
In this paper, we leverage the human perceiving process, that involves vision and language interaction, to generate a coherent paragraph description of untrimmed videos.
Ranked #3 on
Video Captioning
on ActivityNet Captions
ABN: Agent-Aware Boundary Networks for Temporal Action Proposal Generation
Temporal action proposal generation (TAPG) aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet plays an important role in many tasks of video analysis and understanding.
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications
To address such limitations, meta-learning has been adopted in the scenarios of few-shot learning and multiple tasks.
AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation
In this paper, we make an attempt to simulate that ability of a human by proposing Actor Environment Interaction (AEI) network to improve the video representation for temporal action proposals generation.
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey
In this work, we provide a detailed review of recent and state-of-the-art research advances of deep reinforcement learning in computer vision.
Agent-Environment Network for Temporal Action Proposal Generation
Temporal action proposal generation is an essential and challenging task that aims at localizing temporal intervals containing human actions in untrimmed videos.
Invertible Residual Network with Regularization for Effective Medical Image Segmentation
Among many successful network architectures, 3D Unet has been established as a standard architecture for volumetric medical segmentation.
Offset Curves Loss for Imbalanced Problem in Medical Segmentation
Our proposed Offset Curves (OsC) loss consists of three main fitting terms.
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation
The first objective is performed by our proposed contextual brain tumor detection network, which plays a role of an attention gate and focuses on the region around brain tumor only while ignoring the far neighbor background which is less correlated to the tumor.
