200 citations · 600 across the 23 of their papers we have counts for
15 papers · 1 filter
Fast Trainable Projection for Robust Fine-Tuning
Junjiao Tian, Yen-Cheng Liu, James Seale Smith +1
Robust fine-tuning aims to achieve competitive in-distribution (ID) performance while maintaining the out-of-distribution (OOD) robustness of a pre-trained model when transferring…
Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation
Harsh Maheshwari, Yen-Cheng Liu, Zsolt Kira
Using multiple spatial modalities has been proven helpful in improving semantic segmentation performance. However, there are several real-world challenges that have yet to be addre…
Trainable Projected Gradient Method for Robust Fine-tuning
Junjiao Tian, Xiaoliang Dai, Chih-Yao Ma +3
Recent studies on transfer learning have shown that selectively fine-tuning a subset of layers or customizing different learning rates for each layer can greatly improve robustness…
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks
Yen-Cheng Liu, Chih-Yao Ma, Junjiao Tian +2
Adapting large-scale pretrained models to various downstream tasks via fine-tuning is a standard method in machine learning. Recently, parameter-efficient fine-tuning methods show…
Open-Set Semi-Supervised Object Detection
Yen-Cheng Liu, Chih-Yao Ma, Xiaoliang Dai +4
Recent developments for Semi-Supervised Object Detection (SSOD) have shown the promise of leveraging unlabeled data to improve an object detector. However, thus far these methods h…
Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors
Yen-Cheng Liu, Chih-Yao Ma, Zsolt Kira
With the recent development of Semi-Supervised Object Detection (SS-OD) techniques, object detectors can be improved by using a limited amount of labeled data and abundant unlabele…
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…
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…
When2com: Multi-Agent Perception via Communication Graph Grouping
Yen-Cheng Liu, Junjiao Tian, Nathaniel Glaser +1
While significant advances have been made for single-agent perception, many applications require multiple sensing agents and cross-agent communication due to benefits such as cover…
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…
UNO: Uncertainty-aware Noisy-Or Multimodal Fusion for Unanticipated Input Degradation
Junjiao Tian, Wesley Cheung, Nathan Glaser +2
The fusion of multiple sensor modalities, especially through deep learning architectures, has been an active area of study. However, an under-explored aspect of such work is whethe…
A Closer Look at Few-shot Classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira +2
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing…
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
Alexander H. Liu, Yen-Cheng Liu, Yu-Ying Yeh +1
We present a novel and unified deep learning framework which is capable of learning domain-invariant representation from data across multiple domains. Realized by adversarial train…
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification
Yu-Jhe Li, Fu-En Yang, Yen-Cheng Liu +3
Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras. To address this task, one typically requires a large amount labeled…
Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
Yen-Cheng Liu, Yu-Ying Yeh, Tzu-Chien Fu +3
While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular i…