activity
20242026
collaborators

17 papers

cs.AI2026

Learning Intrinsic Dynamics in Latent World Models

Jiamin Shi, Yuanhong Zhang, Zhiyang Liu +2

Learning intrinsic dynamics---the relationship between actions and their consequences---is essential for world models. However, visual variations unrelated to the underlying contro…

cs.LG2026

An AI4AI Framework for Visual Token Pruning

Zhen Liu, Wenli Huang, Wei Song +3

Visual-token pruning can substantially reduce the inference cost of multimodal large language models (MLLMs), yet existing methods largely rely on fixed, handcrafted heuristics and…

cs.AI2026

Improving Auto-Design of Neural PDE Solvers with a Domain-Specific Language

Shengxin Kong, Liwen Xu, Jingwen Fu

Neural PDE solver auto-design is fundamentally a search-space representation problem. In the space of unrestricted Python programs, valid solvers form an extremely sparse subset: m…

cs.CV2026

Overcoming Statistical Bias in Action-Controllable World Models

Yuhong Shi, Zhenhao Chu, Jie Wei +3

Action-conditioned world models aim to predict how visual environments evolve under an agent's actions. Yet future frames are often highly predictable from visual inertia and recur…

cs.LG2026

GraphIR: Architecture-Level Search States for LLM-Guided Neural Architecture Evolution

Zhen Liu, Wanqi Zhou, Shuanghao Bai +3

Large language models (LLMs) enable neural architecture search (NAS) directly over executable neural network programs. However, code-level flexibility does not provide the architec…

cs.LG2026

Overcoming the Weakest-Link Effect in LLM-Driven Program Optimization via Heterogeneous Edit Recombination

Jingwen Fu, Zhen Liu, Yuhan Liu +2

Large language models (LLMs) are increasingly used to solve complex problems by searching over program space, offering a general paradigm for scientific problems that can be natura…

cs.IR2026

ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval

Yuhan Liu, Pei Fu, Hang Li +8

Leveraging Multimodal Large Language Models (MLLMs) via contrastive learning has become a mainstream paradigm for improving the performance of Universal Multimodal Retrieval (UMR).…

cs.LG2026

Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search

Zhen Liu, Yuhan Liu, Jinjun Wang +3

This paper focuses on a key challenge in Neural Architecture Search (NAS): integrating established architectural knowledge while exploring new designs under expensive evaluations.…

cs.AI2026

Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding

Zhiqin Yang, Yuhan Liu, Jingwen Fu +4

Although natural language is the default medium for Large Language Models (LLMs), its limited expressive capacity creates a profound bottleneck for complex problem-solving. While r…

cs.CV2026

The Essence of Balance for Self-Improving Agents in Vision-and-Language Navigation

Zhen Liu, Yuhan Liu, Jinjun Wang +3

In vision-and-language navigation (VLN), self-improvement from policy-induced experience, using only standard VLN action supervision, critically depends on balancing behavioral div…

cs.CV2026

Instruction-as-State: Environment-Guided and State-Conditioned Semantic Understanding for Embodied Navigation

Zhen Liu, Yuhan Liu, Jinjun Wang +3

Vision-and-Language Navigation requires agents to follow natural-language instructions in visually changing environments. A central challenge is the dynamic entanglement between la…

cs.CV2025

DAMap: Distance-aware MapNet for High Quality HD Map Construction

Jinpeng Dong, Chen Li, Yutong Lin +3

Predicting High-definition (HD) map elements with high quality (high classification and localization scores) is crucial to the safety of autonomous driving vehicles. However, curre…

cs.MM2025

CEM-Net: Cross-Emotion Memory Network for Emotional Talking Face Generation

Kangyi Wu, Pengna Li, Jingwen Fu +4

Emotional talking face generation aims to animate a human face in given reference images and generate a talking video that matches the content and emotion of driving audio. However…

cs.CV2025

StructVPR++: Distill Structural and Semantic Knowledge with Weighting Samples for Visual Place Recognition

Yanqing Shen, Sanping Zhou, Jingwen Fu +3

Visual place recognition is a challenging task for autonomous driving and robotics, which is usually considered as an image retrieval problem. A commonly used two-stage strategy in…

cs.CV2025

REGNav: Room Expert Guided Image-Goal Navigation

Pengna Li, Kangyi Wu, Jingwen Fu +1

Image-goal navigation aims to steer an agent towards the goal location specified by an image. Most prior methods tackle this task by learning a navigation policy, which extracts vi…

cs.CV2024

PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization

Haoran Zhang, Shuanghao Bai, Wanqi Zhou +2

Source-free domain generalization (SFDG) tackles the challenge of adapting models to unseen target domains without access to source domain data. To deal with this challenging task,…

cs.IR2024

Semantic-aware Representation Learning for Homography Estimation

Yuhan Liu, Qianxin Huang, Siqi Hui +5

Homography estimation is the task of determining the transformation from an image pair. Our approach focuses on employing detector-free feature matching methods to address this iss…