2 citations · 4 across the 20 of their papers we have counts for
22 papers
STRAND: Benchmarking and Improving Object-Centric Spatio-Temporal Monitoring in Video Large Language Models
Thong Nguyen, Tri Cao, Khoi Le +4
While multimodal large language models (MLLMs) have advanced video understanding, they remain highly prone to hallucinations in dynamic scenes. We argue this stems from a failure i…
MatReplace: A Reference-Free, Conditioning-Aligned Benchmark for Material Replacement in Interior Scenes
Mingzhe Du, Thong Thanh Nguyen, Nguyen Tran Cong Duy +2
Material replacement is a common interior-design operation: changing the material of a selected surface while preserving its geometry, surroundings, and illumination. Despite its c…
DynaPix: Can Vision-Language Models Identify the Exact Future?
Thong Nguyen, Vinh-Hien Do, Quynh Vo +2
Acting in a physical scene requires knowing its real later state, not a plausible one. Current evaluations often accept words or a realistic-looking image, so the predicted state i…
When In-Distribution Gains Fail: Evaluating Weak-to-Strong Reward Models under Preference Shift
Khoi Le, Tri Cao, Phong Nguyen +5
Weak-to-strong (W2S) generalization is a promising framework for scalable oversight, yet existing evaluations often test students under matched train-test distributions. Therefore,…
Perception Without Engagement: Dissecting the Causal Discovery Deficit in LMMs
Jiafeng Liang, Zhihao Zhu, Zihan Zhang +7
Although Large Multimodal Models (LMMs) have achieved strong performance on general video understanding, their susceptibility to textual prior shortcuts during causal discovery has…
Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning
Lin Li, Jiawei Huang, Qihao Quan +7
In this paper, we propose the first VL\underline{\textbf{M}} \underline{\textbf{a}}gentic \underline{\textbf{r}}easoning framework for few-\underline{\textbf{s}}hot multimodal \und…
Tracking the Truth: Object-Centric Spatio-Temporal Monitoring for Video Large Language Models
Tri Cao, Khoi Le, Thong Nguyen +7
While multimodal large language models (MLLMs) have advanced video understanding, they remain highly prone to hallucinations in dynamic scenes. We argue this stems from a failure i…
Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning
Zhiyuan Hu, Yunhai Hu, Juncheng Liu +9
Multi-agent systems have evolved into practical LLM-driven collaborators for many applications, gaining robustness from diversity and cross-checking. However, multi-agent RL (MARL)…
Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs
Zhiyuan Hu, Yucheng Wang, Yufei He +7
Reinforcement learning (RL) has become a central paradigm for post-training large language models (LLMs), particularly for complex reasoning tasks, yet it often suffers from explor…
EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code
Yuhao Qing, Boyu Zhu, Mingzhe Du +9
Existing code generation benchmarks primarily evaluate functional correctness, with limited focus on code efficiency and often restricted to a single language like Python. To addre…
Temporal-Oriented Recipe for Transferring Large Vision-Language Model to Video Understanding
Thong Nguyen, Zhiyuan Hu, Xu Lin +3
Recent years have witnessed outstanding advances of large vision-language models (LVLMs). In order to tackle video understanding, most of them depend upon their implicit temporal u…
Guiding VLM Agents with Process Rewards at Inference Time for GUI Navigation
Zhiyuan Hu, Shiyun Xiong, Yifan Zhang +5
Recent advancements in visual language models (VLMs) have notably enhanced their capabilities in handling complex Graphical User Interface (GUI) interaction tasks. Despite these im…
CodeArena: A Collective Evaluation Platform for LLM Code Generation
Mingzhe Du, Anh Tuan Luu, Bin Ji +5
Large Language Models (LLMs) have reshaped code generation by synergizing their exceptional comprehension of natural language and programming syntax, thereby substantially boosting…
Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation
Thong Thanh Nguyen, Xiaobao Wu, Yi Bin +3
To equip artificial intelligence with a comprehensive understanding towards a temporal world, video and 4D panoptic scene graph generation abstracts visual data into nodes to repre…
LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models
Zhiyuan Hu, Yuliang Liu, Jinman Zhao +8
Large language models (LLMs) face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts t…
MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning
Thong Nguyen, Yi Bin, Xiaobao Wu +6
Data quality stands at the forefront of deciding the effectiveness of video-language representation learning. However, video-text pairs in previous data typically do not align perf…
Encoding and Controlling Global Semantics for Long-form Video Question Answering
Thong Thanh Nguyen, Zhiyuan Hu, Xiaobao Wu +3
Seeking answers effectively for long videos is essential to build video question answering (videoQA) systems. Previous methods adaptively select frames and regions from long videos…
SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks
Brian Formento, Wenjie Feng, Chuan Sheng Foo +2
Language models (LMs) are indispensable tools for natural language processing tasks, but their vulnerability to adversarial attacks remains a concern. While current research has ex…
READ: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language Modeling
Thong Nguyen, Xiaobao Wu, Xinshuai Dong +5
Fully fine-tuning pretrained large-scale transformer models has become a popular paradigm for video-language modeling tasks, such as temporal language grounding and video-language…
DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding
Thong Nguyen, Xiaobao Wu, Xinshuai Dong +3
Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query. Recent advances employ the attention mechanism to learn the re…
MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration
Lin Xu, Zhiyuan Hu, Daquan Zhou +5
Large Language Models (LLMs) have significantly advanced natural language processing, demonstrating exceptional reasoning, tool usage, and memory capabilities. As their application…
Vision-and-Language Pretraining
Thong Nguyen, Cong-Duy Nguyen, Xiaobao Wu +2
With the burgeoning amount of data of image-text pairs and diversity of Vision-and-Language (V\&L) tasks, scholars have introduced an abundance of deep learning models in this rese…