665 citations · 4.5k across the 236 of their papers we have counts for
14 papers · 1 filter
Understanding Divergent Framing of the Supreme Court Controversies: Social Media vs. News Outlets
Jinsheng Pan, Zichen Wang, Weihong Qi +2
Understanding the framing of political issues is of paramount importance as it significantly shapes how individuals perceive, interpret, and engage with these matters. While prior…
Jurassic World Remake: Bringing Ancient Fossils Back to Life via Zero-Shot Long Image-to-Image Translation
Alexander Martin, Haitian Zheng, Jie An +1
With a strong understanding of the target domain from natural language, we produce promising results in translating across large domain gaps and bringing skeletons back to life. In…
User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations
Juntao Tan, Yingqiang Ge, Yan Zhu +4
Modern recommender systems utilize users' historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminish…
SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation
Wenxi Yue, Jing Zhang, Kun Hu +3
The Segment Anything Model (SAM) is a powerful foundation model that has revolutionised image segmentation. To apply SAM to surgical instrument segmentation, a common approach is t…
MobileVidFactory: Automatic Diffusion-Based Social Media Video Generation for Mobile Devices from Text
Junchen Zhu, Huan Yang, Wenjing Wang +8
Videos for mobile devices become the most popular access to share and acquire information recently. For the convenience of users' creation, in this paper, we present a system, name…
Domain-Scalable Unpaired Image Translation via Latent Space Anchoring
Siyu Huang, Jie An, Donglai Wei +3
Unpaired image-to-image translation (UNIT) aims to map images between two visual domains without paired training data. However, given a UNIT model trained on certain domains, it is…
Learning to Evaluate the Artness of AI-generated Images
Junyu Chen, Jie An, Hanjia Lyu +2
Assessing the artness of AI-generated images continues to be a challenge within the realm of image generation. Most existing metrics cannot be used to perform instance-level and re…
Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation
Jie An, Songyang Zhang, Harry Yang +4
We propose Latent-Shift -- an efficient text-to-video generation method based on a pretrained text-to-image generation model that consists of an autoencoder and a U-Net diffusion m…
Meta-causal Learning for Single Domain Generalization
Jin Chen, Zhi Gao, Xinxiao Wu +1
Single domain generalization aims to learn a model from a single training domain (source domain) and apply it to multiple unseen test domains (target domains). Existing methods foc…
SegPrompt: Using Segmentation Map as a Better Prompt to Finetune Deep Models for Kidney Stone Classification
Wei Zhu, Runtao Zhou, Yao Yuan +3
Recently, deep learning has produced encouraging results for kidney stone classification using endoscope images. However, the shortage of annotated training data poses a severe pro…
VideoXum: Cross-modal Visual and Textural Summarization of Videos
Jingyang Lin, Hang Hua, Ming Chen +4
Video summarization aims to distill the most important information from a source video to produce either an abridged clip or a textual narrative. Traditionally, different methods h…
Spatial-Aware Token for Weakly Supervised Object Localization
Pingyu Wu, Wei Zhai, Yang Cao +2
Weakly supervised object localization (WSOL) is a challenging task aiming to localize objects with only image-level supervision. Recent works apply visual transformer to WSOL and a…
Grounding 3D Object Affordance from 2D Interactions in Images
Yuhang Yang, Wei Zhai, Hongchen Luo +3
Grounding 3D object affordance seeks to locate objects' ''action possibilities'' regions in the 3D space, which serves as a link between perception and operation for embodied agent…
Adaptive Siamese Tracking with a Compact Latent Network
Xingping Dong, Jianbing Shen, Fatih Porikli +2
In this paper, we provide an intuitive viewing to simplify the Siamese-based trackers by converting the tracking task to a classification. Under this viewing, we perform an in-dept…