164 citations · 488 across the 38 of their papers we have counts for
40 papers
VLX-Go: Robust Embodied Tracking through Multi-Stage Learning and On-Policy Refinement
Kyusong Lee, Heting Ying, Tiancheng Zhao
Embodied visual tracking requires a robot to identify a designated target from visual observations and control its own motion to follow that target through an environment. Followin…
FIDES: Faithful Inference via Deep Evidence Signals for Retrieval-Memory Conflict in RAG
Zhe Yu, Wenpeng Xing, Tiancheng Zhao +3
When retrieved evidence contradicts parametric memory, language models frequently ignore context and default to memorized priors -- a failure that undermines the core purpose of re…
Geo-R1: Improving Few-Shot Geospatial Referring Expression Understanding with Reinforcement Fine-Tuning
Zilun Zhang, Zian Guan, Tiancheng Zhao +7
Referring expression understanding in remote sensing poses unique challenges, as it requires reasoning over complex object-context relationships. While supervised fine-tuning (SFT)…
Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research
Qianqian Zhang, Jiajia Liao, Heting Ying +9
Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks. However, developing robu…
VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
Haozhan Shen, Peng Liu, Jingcheng Li +9
Recently DeepSeek R1 has shown that reinforcement learning (RL) can substantially improve the reasoning capabilities of Large Language Models (LLMs) through a simple yet effective…
The Self-Improvement Paradox: Can Language Models Bootstrap Reasoning Capabilities without External Scaffolding?
Yutao Sun, Mingshuai Chen, Tiancheng Zhao +3
Self-improving large language models (LLMs) -- i.e., to improve the performance of an LLM by fine-tuning it with synthetic data generated by itself -- is a promising way to advance…
ImageRAG: Enhancing Ultra High Resolution Remote Sensing Imagery Analysis with ImageRAG
Zilun Zhang, Haozhan Shen, Tiancheng Zhao +7
Ultra High Resolution (UHR) remote sensing imagery (RSI) (e.g. 100,000 100,000 pixels or more) poses a significant challenge for current Remote Sensing Multimodal Large La…
OmChat: A Recipe to Train Multimodal Language Models with Strong Long Context and Video Understanding
Tiancheng Zhao, Qianqian Zhang, Kyusong Lee +7
We introduce OmChat, a model designed to excel in handling long contexts and video understanding tasks. OmChat's new architecture standardizes how different visual inputs are proce…
OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer
Lu Zhang, Tiancheng Zhao, Heting Ying +2
Recent advancements in Large Language Models (LLMs) have expanded their capabilities to multimodal contexts, including comprehensive video understanding. However, processing extens…
Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression
Zilun Zhang, Yutao Sun, Tiancheng Zhao +4
Humans can retain old knowledge while learning new information, but Large Language Models (LLMs) often suffer from catastrophic forgetting when post-pretrained or supervised fine-t…
HORAE: A Domain-Agnostic Language for Automated Service Regulation
Yutao Sun, Mingshuai Chen, Tiancheng Zhao +8
Artificial intelligence is rapidly encroaching on the field of service regulation. However, existing AI-based regulation techniques are often tailored to specific application domai…
Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head
Tiancheng Zhao, Peng Liu, Xuan He +2
End-to-end transformer-based detectors (DETRs) have shown exceptional performance in both closed-set and open-vocabulary object detection (OVD) tasks through the integration of lan…
GroundVLP: Harnessing Zero-shot Visual Grounding from Vision-Language Pre-training and Open-Vocabulary Object Detection
Haozhan Shen, Tiancheng Zhao, Mingwei Zhu +1
Visual grounding, a crucial vision-language task involving the understanding of the visual context based on the query expression, necessitates the model to capture the interactions…
Benchmarking Sequential Visual Input Reasoning and Prediction in Multimodal Large Language Models
Mingwei Zhu, Leigang Sha, Yu Shu +3
Multimodal large language models (MLLMs) have shown great potential in perception and interpretation tasks, but their capabilities in predictive reasoning remain under-explored. To…
How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection
Yiyang Yao, Peng Liu, Tiancheng Zhao +5
Object detection (OD) in computer vision has made significant progress in recent years, transitioning from closed-set labels to open-vocabulary detection (OVD) based on large-scale…
OmDet: Large-scale vision-language multi-dataset pre-training with multimodal detection network
Tiancheng Zhao, Peng Liu, Kyusong Lee
The advancement of object detection (OD) in open-vocabulary and open-world scenarios is a critical challenge in computer vision. This work introduces OmDet, a novel language-aware…
VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations
Tiancheng Zhao, Tianqi Zhang, Mingwei Zhu +4
Vision-Language Pretraining (VLP) models have recently successfully facilitated many cross-modal downstream tasks. Most existing works evaluated their systems by comparing the fine…
SF-QA: Simple and Fair Evaluation Library for Open-domain Question Answering
Xiaopeng Lu, Kyusong Lee, Tiancheng Zhao
Although open-domain question answering (QA) draws great attention in recent years, it requires large amounts of resources for building the full system and is often difficult to re…
VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words
Xiaopeng Lu, Tiancheng Zhao, Kyusong Lee
Text-to-image retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant images from a large and unlabelled dataset given textual queries. In th…
SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval
Tiancheng Zhao, Xiaopeng Lu, Kyusong Lee
We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering. Unlike many n…
Report from the NSF Future Directions Workshop, Toward User-Oriented Agents: Research Directions and Challenges
Maxine Eskenazi, Tiancheng Zhao
This USER Workshop was convened with the goal of defining future research directions for the burgeoning intelligent agent research community and to communicate them to the National…
Talk to Papers: Bringing Neural Question Answering to Academic Search
Tianchang Zhao, Kyusong Lee
We introduce Talk to Papers, which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search. It's designed to enable…
"None of the Above":Measure Uncertainty in Dialog Response Retrieval
Yulan Feng, Shikib Mehri, Maxine Eskenazi +1
This paper discusses the importance of uncovering uncertainty in end-to-end dialog tasks, and presents our experimental results on uncertainty classification on the Ubuntu Dialog C…
Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References
Prakhar Gupta, Shikib Mehri, Tiancheng Zhao +3
The aim of this paper is to mitigate the shortcomings of automatic evaluation of open-domain dialog systems through multi-reference evaluation. Existing metrics have been shown to…