Qi Zeng

AAAI Conference 2026 Conference Paper

Explicit Temporal-Semantic Modeling for Dense Video Captioning via Context-Aware Cross-Modal Interaction

• Mingda Jia
• Weiliang Meng
• Zenghuang Fu
• Yiheng Li
• Qi Zeng
• Yifan Zhang
• Ju Xin
• Rongtao Xu

Dense video captioning jointly localizes and captions salient events in untrimmed videos. Recent methods primarily focus on leveraging additional prior knowledge and advanced multi-task architectures to achieve competitive performance. However, these pipelines rely on implicit modeling that uses frame-level or fragmented video features, failing to capture the temporal coherence across event sequences and comprehensive semantics within visual contexts. To address this, we propose an explicit temporal-semantic modeling framework called Context-Aware Cross-Modal Interaction (CACMI), which leverages both latent temporal characteristics within videos and linguistic semantics from text corpus. Specifically, our model consists of two core components: Cross-modal Frame Aggregation aggregates relevant frames to extract temporally coherent, event-aligned textual features through cross-modal retrieval; and Context-aware Feature Enhancement utilizes query-guided attention to integrate visual dynamics with pseudo-event semantics. Extensive experiments on the ActivityNet Captions and YouCook2 datasets demonstrate that CACMI achieves the state-of-the-art performance on dense video captioning task.

YNIMG Journal 2025 Journal Article

FetDTIAlign: A deep learning framework for affine and deformable registration of fetal brain dMRI

• Bo Li
• Qi Zeng
• Simon K. Warfield
• Davood Karimi

YNIMG Journal 2025 Journal Article

Towards automatic US-MR fetal brain image registration with learning-based methods

• Qi Zeng
• Weide Liu
• Bo Li
• Ryne Didier
• P. Ellen Grant
• Davood Karimi

TMLR Journal 2025 Journal Article

Two-Step Offline Preference-Based Reinforcement Learning on Explicitly Constrained Policies

• Yinglun Xu
• Tarun Suresh
• Rohan Gumaste
• David Zhu
• Ruirui Li
• Zhengyang Wang
• Haoming Jiang
• Xianfeng Tang

Preference-based reinforcement learning (PBRL) in the offline setting has succeeded greatly in industrial applications such as chatbots. A two-step learning framework that learns a reward model from an offline dataset first and then optimizes a policy over the learned reward model through online reinforcement learning has been widely adopted. However, such a method faces challenges from the risk of reward hacking and the complexity of reinforcement learning. To overcome the challenge, our insight is that both challenges come from the state-actions not supported in the dataset. Such state actions are unreliable and increase the complexity of the reinforcement learning problem. Based on the insight, we develop a novel two-step learning method called PRC: preference-based reinforcement learning on explicitly constrained policies. The high-level idea is to limit the reinforcement learning agent to optimize over policies supported on an explicitly constrained action space that excludes the out-of-distribution state-actions. We empirically verify that our method has high learning efficiency on various datasets in robotic control environments.

ICLR Conference 2023 Conference Paper

Competitive Physics Informed Networks

• Qi Zeng
• Yash Kothari
• Spencer H. Bryngelson
• Florian Tobias Schäfer

Neural networks can be trained to solve partial differential equations (PDEs) by using the PDE residual as the loss function. This strategy is called "physics-informed neural networks" (PINNs), but it currently cannot produce high-accuracy solutions, typically attaining about $0.1\%$ relative error. We present an adversarial approach that overcomes this limitation, which we call competitive PINNs (CPINNs). CPINNs train a discriminator that is rewarded for predicting mistakes the PINN makes. The discriminator and PINN participate in a zero-sum game with the exact PDE solution as an optimal strategy. This approach avoids squaring the large condition numbers of PDE discretizations, which is the likely reason for failures of previous attempts to decrease PINN errors even on benign problems. Numerical experiments on a Poisson problem show that CPINNs achieve errors four orders of magnitude smaller than the best-performing PINN. We observe relative errors on the order of single-precision accuracy, consistently decreasing with each epoch. To the authors' knowledge, this is the first time this level of accuracy and convergence behavior has been achieved. Additional experiments on the nonlinear Schr{\"o}dinger, Burgers', and Allen--Cahn equation show that the benefits of CPINNs are not limited to linear problems.

TMLR Journal 2023 Journal Article

Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning

• Yinglun Xu
• Qi Zeng
• Gagandeep Singh

We study reward poisoning attacks on online deep reinforcement learning (DRL), where the attacker is oblivious to the learning algorithm used by the agent and the dynamics of the environment. We demonstrate the intrinsic vulnerability of state-of-the-art DRL algorithms by designing a general, black-box reward poisoning framework called adversarial MDP attacks. We instantiate our framework to construct two new attacks which only corrupt the rewards for a small fraction of the total training timesteps and make the agent learn a low-performing policy. We provide a theoretical analysis of the efficiency of our attack and perform an extensive empirical evaluation. Our results show that our attacks efficiently poison agents learning in several popular classical control and MuJoCo environments with a variety of state-of-the-art DRL algorithms, such as DQN, PPO, SAC, etc.

NeurIPS Conference 2021 Conference Paper

Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method

• Yifan Chen
• Qi Zeng
• Heng Ji
• Yun Yang

Transformers are expensive to train due to the quadratic time and space complexity in the self-attention mechanism. On the other hand, although kernel machines suffer from the same computation bottleneck in pairwise dot products, several approximation schemes have been successfully incorporated to considerably reduce their computational cost without sacrificing too much accuracy. In this work, we leverage the computation methods for kernel machines to alleviate the high computational cost and introduce Skyformer, which replaces the softmax structure with a Gaussian kernel to stabilize the model training and adapts the Nyström method to a non-positive semidefinite matrix to accelerate the computation. We further conduct theoretical analysis by showing that the matrix approximation error of our proposed method is small in the spectral norm. Experiments on Long Range Arena benchmark show that the proposed method is sufficient in getting comparable or even better performance than the full self-attention while requiring fewer computation resources.

AAAI Conference 2019 Conference Paper

Learning Personalized End-to-End Goal-Oriented Dialog

• Liangchen Luo
• Wenhao Huang
• Qi Zeng
• Zaiqing Nie
• Xu Sun

Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first introduce a PROFILE MODEL which encodes user profiles into distributed embeddings and refers to conversation history from other similar users. Then a PREFERENCE MODEL captures user preferences over knowledge base entities to handle the ambiguity in user requests. The two models are combined into the PERSONALIZED MEMN2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.

AAAI Conference 2019 Conference Paper

Text Assisted Insight Ranking Using Context-Aware Memory Network

• Qi Zeng
• Liangchen Luo
• Wenhao Huang
• Yang Tang

Extracting valuable facts or informative summaries from multi-dimensional tables, i. e. insight mining, is an important task in data analysis and business intelligence. However, ranking the importance of insights remains a challenging and unexplored task. The main challenge is that explicitly scoring an insight or giving it a rank requires a thorough understanding of the tables and costs a lot of manual efforts, which leads to the lack of available training data for the insight ranking problem. In this paper, we propose an insight ranking model that consists of two parts: A neural ranking model explores the data characteristics, such as the header semantics and the data statistical features, and a memory network model introduces table structure and context information into the ranking process. We also build a dataset with text assistance. Experimental results show that our approach largely improves the ranking precision as reported in multi evaluation metrics.