33 citations · 127 across the 18 of their papers we have counts for
18 papers
Squeezing Capacity from Multimodal Large Language Models for Subject-driven Generation
Shuhong Zheng, Aashish Kumar Misraa, Yu-Teng Li +2
Subject-driven image generation aims to synthesize new images that preserve the identity of the given subject while following textual instructions. Existing approaches often encode…
3D-CLFusion: Fast Text-to-3D Rendering with Contrastive Latent Diffusion
Yu-Jhe Li, Tao Xu, Ji Hou +6
We tackle the task of text-to-3D creation with pre-trained latent-based NeRFs (NeRFs that generate 3D objects given input latent code). Recent works such as DreamFusion and Magic3D…
3D-Aware Encoding for Style-based Neural Radiance Fields
Yu-Jhe Li, Tao Xu, Bichen Wu +6
We tackle the task of NeRF inversion for style-based neural radiance fields, (e.g., StyleNeRF). In the task, we aim to learn an inversion function to project an input image to the…
Domain Adaptive Hand Keypoint and Pixel Localization in the Wild
Takehiko Ohkawa, Yu-Jhe Li, Qichen Fu +3
We aim to improve the performance of regressing hand keypoints and segmenting pixel-level hand masks under new imaging conditions (e.g., outdoors) when we only have labeled images…
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…
Wide-Baseline Multi-Camera Calibration using Person Re-Identification
Yan Xu, Yu-Jhe Li, Xinshuo Weng +1
We address the problem of estimating the 3D pose of a network of cameras for large-environment wide-baseline scenarios, e.g., cameras for construction sites, sports stadiums, and p…
Semantics-Guided Representation Learning with Applications to Visual Synthesis
Jia-Wei Yan, Ci-Siang Lin, Fu-En Yang +2
Learning interpretable and interpolatable latent representations has been an emerging research direction, allowing researchers to understand and utilize the derived latent space fo…
Semantics-Guided Clustering with Deep Progressive Learning for Semi-Supervised Person Re-identification
Chih-Ting Liu, Yu-Jhe Li, Shao-Yi Chien +1
Person re-identification (re-ID) requires one to match images of the same person across camera views. As a more challenging task, semi-supervised re-ID tackles the problem that onl…
Transforming Multi-Concept Attention into Video Summarization
Yen-Ting Liu, Yu-Jhe Li, Yu-Chiang Frank Wang
Video summarization is among challenging tasks in computer vision, which aims at identifying highlight frames or shots over a lengthy video input. In this paper, we propose an nove…
Learning Shape Representations for Clothing Variations in Person Re-Identification
Yu-Jhe Li, Zhengyi Luo, Xinshuo Weng +1
Person re-identification (re-ID) aims to recognize instances of the same person contained in multiple images taken across different cameras. Existing methods for re-ID tend to rely…
Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond
Yu-Jhe Li, Yun-Chun Chen, Yen-Yu Lin +1
Person re-identification (re-ID) aims at matching images of the same person across camera views. Due to varying distances between cameras and persons of interest, resolution mismat…
Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation
Yu-Jhe Li, Ci-Siang Lin, Yan-Bo Lin +1
Person re-identification (re-ID) aims at recognizing the same person from images taken across different cameras. To address this challenging task, existing re-ID models typically r…
Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification
Yu-Jhe Li, Yun-Chun Chen, Yen-Yu Lin +2
Person re-identification (re-ID) aims at matching images of the same identity across camera views. Due to varying distances between cameras and persons of interest, resolution mism…
Learning Resolution-Invariant Deep Representations for Person Re-Identification
Yun-Chun Chen, Yu-Jhe Li, Xiaofei Du +1
Person re-identification (re-ID) solves the task of matching images across cameras and is among the research topics in vision community. Since query images in real-world scenarios…
Dual-modality seq2seq network for audio-visual event localization
Yan-Bo Lin, Yu-Jhe Li, Yu-Chiang Frank Wang
Audio-visual event localization requires one to identify theevent which is both visible and audible in a video (eitherat a frame or video level). To address this task, we pro-pose…
Deep Reinforcement Learning for Playing 2.5D Fighting Games
Yu-Jhe Li, Hsin-Yu Chang, Yu-Jing Lin +2
Deep reinforcement learning has shown its success in game playing. However, 2.5D fighting games would be a challenging task to handle due to ambiguity in visual appearances like he…
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…
Deep Learning for Malicious Flow Detection
Yun-Chun Chen, Yu-Jhe Li, Aragorn Tseng +1
Cyber security has grown up to be a hot issue in recent years. How to identify potential malware becomes a challenging task. To tackle this challenge, we adopt deep learning approa…