24 citations · 100 across the 25 of their papers we have counts for
16 papers · 1 filter
Learning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning
Yuto Shibata, Kashu Yamazaki, Lalit Jayanti +3
Humanoid robotics has strong potential to transform daily service and caregiving applications. Although recent advances in general motion tracking within physics engines (GMT) have…
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model
Khoa Vo, Thinh Phan, Kashu Yamazaki +2
Current video-language models (VLMs) rely extensively on instance-level alignment between video and language modalities, which presents two major limitations: (1) visual reasoning…
-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations
Xiang Li, Kai Qiu, Jinglu Wang +6
Referring perception, which aims at grounding visual objects with multimodal referring guidance, is essential for bridging the gap between humans, who provide instructions, and the…
Open-Fusion: Real-time Open-Vocabulary 3D Mapping and Queryable Scene Representation
Kashu Yamazaki, Taisei Hanyu, Khoa Vo +5
Precise 3D environmental mapping is pivotal in robotics. Existing methods often rely on predefined concepts during training or are time-intensive when generating semantic maps. Thi…
AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation
Kashu Yamazaki, Taisei Hanyu, Minh Tran +7
Aerial Image Segmentation is a top-down perspective semantic segmentation and has several challenging characteristics such as strong imbalance in the foreground-background distribu…
Contextual Explainable Video Representation: Human Perception-based Understanding
Khoa Vo, Kashu Yamazaki, Phong X. Nguyen +3
Video understanding is a growing field and a subject of intense research, which includes many interesting tasks to understanding both spatial and temporal information, e.g., action…
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection
Hyekang Kevin Joo, Khoa Vo, Kashu Yamazaki +1
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 pro…
VLTinT: Visual-Linguistic Transformer-in-Transformer for Coherent Video Paragraph Captioning
Kashu Yamazaki, Khoa Vo, Sang Truong +2
Video paragraph captioning aims to generate a multi-sentence description of an untrimmed video with several temporal event locations in coherent storytelling. Following the human p…
AOE-Net: Entities Interactions Modeling with Adaptive Attention Mechanism for Temporal Action Proposals Generation
Khoa Vo, Sang Truong, Kashu Yamazaki +3
Temporal action proposal generation (TAPG) is a challenging task, which requires localizing action intervals in an untrimmed video. Intuitively, we as humans, perceive an action th…
AISFormer: Amodal Instance Segmentation with Transformer
Minh Tran, Khoa Vo, Kashu Yamazaki +3
Amodal Instance Segmentation (AIS) aims to segment the region of both visible and possible occluded parts of an object instance. While Mask R-CNN-based AIS approaches have shown pr…
VLCap: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning
Kashu Yamazaki, Sang Truong, Khoa Vo +4
In this paper, we leverage the human perceiving process, that involves vision and language interaction, to generate a coherent paragraph description of untrimmed videos. We propose…
ABN: Agent-Aware Boundary Networks for Temporal Action Proposal Generation
Khoa Vo, Kashu Yamazaki, Sang Truong +3
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 v…
AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation
Khoa Vo, Hyekang Joo, Kashu Yamazaki +4
Humans typically perceive the establishment of an action in a video through the interaction between an actor and the surrounding environment. An action only starts when the main ac…
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey
Ngan Le, Vidhiwar Singh Rathour, Kashu Yamazaki +2
Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remar…
Agent-Environment Network for Temporal Action Proposal Generation
Viet-Khoa Vo-Ho, Ngan Le, Kashu Yamazaki +2
Temporal action proposal generation is an essential and challenging task that aims at localizing temporal intervals containing human actions in untrimmed videos. Most of existing a…
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation
Ngan Le, Kashu Yamazaki, Dat Truong +2
In recent years, deep neural networks have achieved state-of-the-art performance in a variety of recognition and segmentation tasks in medical imaging including brain tumor segment…