134 citations · 324 across the 14 of their papers we have counts for
14 papers
Cross-Domain Adaptive Teacher for Object Detection
Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma +6
We address the task of domain adaptation in object detection, where there is a domain gap between a domain with annotations (source) and a domain of interest without annotations (t…
Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation
Muhammad Zubair Irshad, Chih-Yao Ma, Zsolt Kira
Deep Learning has revolutionized our ability to solve complex problems such as Vision-and-Language Navigation (VLN). This task requires the agent to navigate to a goal purely based…
Unbiased Teacher for Semi-Supervised Object Detection
Yen-Cheng Liu, Chih-Yao Ma, Zijian He +6
Semi-supervised learning, i.e., training networks with both labeled and unlabeled data, has made significant progress recently. However, existing works have primarily focused on im…
FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning
Chia-Wen Kuo, Chih-Yao Ma, Jia-Bin Huang +1
Recent state-of-the-art semi-supervised learning (SSL) methods use a combination of image-based transformations and consistency regularization as core components. Such methods, how…
Frustratingly Simple Domain Generalization via Image Stylization
Nathan Somavarapu, Chih-Yao Ma, Zsolt Kira
Convolutional Neural Networks (CNNs) show impressive performance in the standard classification setting where training and testing data are drawn i.i.d. from a given domain. Howeve…
Who2com: Collaborative Perception via Learnable Handshake Communication
Yen-Cheng Liu, Junjiao Tian, Chih-Yao Ma +3
In this paper, we propose the problem of collaborative perception, where robots can combine their local observations with those of neighboring agents in a learnable way to improve…
Manifold Graph with Learned Prototypes for Semi-Supervised Image Classification
Chia-Wen Kuo, Chih-Yao Ma, Jia-Bin Huang +1
Recent advances in semi-supervised learning methods rely on estimating the categories of unlabeled data using a model trained on the labeled data (pseudo-labeling) and using the un…
Learning to Generate Grounded Visual Captions without Localization Supervision
Chih-Yao Ma, Yannis Kalantidis, Ghassan AlRegib +3
When automatically generating a sentence description for an image or video, it often remains unclear how well the generated caption is grounded, that is whether the model uses the…
The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation
Chih-Yao Ma, Zuxuan Wu, Ghassan AlRegib +2
As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making. Specifically, the Visi…
Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
Chih-Yao Ma, Jiasen Lu, Zuxuan Wu +4
The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments. This challenging task demands that the ag…
AdaFrame: Adaptive Frame Selection for Fast Video Recognition
Zuxuan Wu, Caiming Xiong, Chih-Yao Ma +2
We present AdaFrame, a framework that adaptively selects relevant frames on a per-input basis for fast video recognition. AdaFrame contains a Long Short-Term Memory network augment…
Grounded Objects and Interactions for Video Captioning
Chih-Yao Ma, Asim Kadav, Iain Melvin +3
We address the problem of video captioning by grounding language generation on object interactions in the video. Existing work mostly focuses on overall scene understanding with of…
Attend and Interact: Higher-Order Object Interactions for Video Understanding
Chih-Yao Ma, Asim Kadav, Iain Melvin +3
Human actions often involve complex interactions across several inter-related objects in the scene. However, existing approaches to fine-grained video understanding or visual relat…
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
Chih-Yao Ma, Min-Hung Chen, Zsolt Kira +1
Recent two-stream deep Convolutional Neural Networks (ConvNets) have made significant progress in recognizing human actions in videos. Despite their success, methods extending the…