6 citations · 19 across the 12 of their papers we have counts for
15 papers
Search2Motion: Training-Free Object-Level Motion Control via Attention-Consensus Search
Sainan Liu, Tz-Ying Wu, Hector A Valdez +1
We present Search2Motion, a training-free framework for object-level motion editing in image-to-video generation. Unlike prior methods requiring trajectories, bounding boxes, masks…
VC-Inspector: Advancing Reference-free Evaluation of Video Captions with Factual Analysis
Shubhashis Roy Dipta, Tz-Ying Wu, Subarna Tripathi
We propose VC-Inspector, a lightweight, open-source large multimodal model (LMM) for reference-free evaluation of video captions, with a focus on factual accuracy. Unlike existing…
Harnessing Object Grounding for Time-Sensitive Video Understanding
Tz-Ying Wu, Sharath Nittur Sridhar, Subarna Tripathi
We propose to improve the time-sensitive video understanding (TSV) capability of video large language models (Video-LLMs) with grounded objects (GO). We hypothesize that TSV tasks…
Toward Scalable Video Narration: A Training-free Approach Using Multimodal Large Language Models
Tz-Ying Wu, Tahani Trigui, Sharath Nittur Sridhar +2
In this paper, we introduce VideoNarrator, a novel training-free pipeline designed to generate dense video captions that offer a structured snapshot of video content. These caption…
EASG-Bench: Video Q&A Benchmark with Egocentric Action Scene Graphs
Ivan Rodin, Tz-Ying Wu, Kyle Min +4
We introduce EASG-Bench, a question-answering benchmark for egocentric videos where the question-answering pairs are created from spatio-temporally grounded dynamic scene graphs ca…
Ego-VPA: Egocentric Video Understanding with Parameter-efficient Adaptation
Tz-Ying Wu, Kyle Min, Subarna Tripathi +1
Video understanding typically requires fine-tuning the large backbone when adapting to new domains. In this paper, we leverage the egocentric video foundation models (Ego-VFMs) bas…
Single-Stage Visual Relationship Learning using Conditional Queries
Alakh Desai, Tz-Ying Wu, Subarna Tripathi +1
Research in scene graph generation (SGG) usually considers two-stage models, that is, detecting a set of entities, followed by combining them and labeling all possible relationship…
ProTeCt: Prompt Tuning for Taxonomic Open Set Classification
Tz-Ying Wu, Chih-Hui Ho, Nuno Vasconcelos
Visual-language foundation models, like CLIP, learn generalized representations that enable zero-shot open-set classification. Few-shot adaptation methods, based on prompt tuning,…
Class-Incremental Learning with Strong Pre-trained Models
Tz-Ying Wu, Gurumurthy Swaminathan, Zhizhong Li +4
Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world…
Learning of Visual Relations: The Devil is in the Tails
Alakh Desai, Tz-Ying Wu, Subarna Tripathi +1
Significant effort has been recently devoted to modeling visual relations. This has mostly addressed the design of architectures, typically by adding parameters and increasing mode…
Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier
Tz-Ying Wu, Pedro Morgado, Pei Wang +2
Long-tail recognition tackles the natural non-uniformly distributed data in real-world scenarios. While modern classifiers perform well on populated classes, its performance degrad…
Exploit Clues from Views: Self-Supervised and Regularized Learning for Multiview Object Recognition
Chih-Hui Ho, Bo Liu, Tz-Ying Wu +1
Multiview recognition has been well studied in the literature and achieves decent performance in object recognition and retrieval task. However, most previous works rely on supervi…
Explainable Object-induced Action Decision for Autonomous Vehicles
Yiran Xu, Xiaoyin Yang, Lihang Gong +4
A new paradigm is proposed for autonomous driving. The new paradigm lies between the end-to-end and pipelined approaches, and is inspired by how humans solve the problem. While it…
Liquid Pouring Monitoring via Rich Sensory Inputs
Tz-Ying Wu, Juan-Ting Lin, Tsun-Hsuang Wang +3
Humans have the amazing ability to perform very subtle manipulation task using a closed-loop control system with imprecise mechanics (i.e., our body parts) but rich sensory informa…
Anticipating Daily Intention using On-Wrist Motion Triggered Sensing
Tz-Ying Wu, Ting-An Chien, Cheng-Sheng Chan +2
Anticipating human intention by observing one's actions has many applications. For instance, picking up a cellphone, then a charger (actions) implies that one wants to charge the c…