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From the 2 of 7 linked papers with an AI index.

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7 papers

cs.CV2026

MedXplore: Towards Reliable and Unbiased Generalized Category Discovery in Medical Imaging

Jianwei He, Kailin Lyu, Junhao Dong +6

The paper presents MedXplore, a unified framework for generalized category discovery in medical imaging that leverages frequency-domain adaptive attention and an adaptive cosine-an…

cs.CV2026

VQ-Touch: A Data-Efficient Tactile Generation Framework Across Sensors and Scenarios

Kailin Lyu, Long Xiao, Jianing Zeng +3

The paper presents VQ-Touch, a framework that efficiently generates high‑fidelity tactile images across different sensors and scenarios using a VQ‑GAN based representation and a di…

cs.AI2026

TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation

Kailin Lyu, Di Wu, Pengwei Zhang +12

Touch is a key modality for embodied agents to understand the physical world. Although recent work has incorporated tactile signals into language systems for tactile commonsense re…

cs.AI2026

TacReasoner: A Dynamic Tactile-Language Framework for Interactive Reasoning in Real-World Scenarios

Kailin Lyu, Di Wu, Long Xiao +7

Among the five primary human senses, tactile is arguably the most fundamental to survival, as it enables the perception of physical contact and interaction in real-world environmen…

cs.CV2026

HiMemVLN: Enhancing Reliability of Open-Source Zero-Shot Vision-and-Language Navigation with Hierarchical Memory System

Kailin Lyu, Kangyi Wu, Pengna Li +9

LLM-based agents have demonstrated impressive zero-shot performance in vision-language navigation (VLN) tasks. However, most zero-shot methods primarily rely on closed-source LLMs…

cs.CV2025

ClearGCD: Mitigating Shortcut Learning For Robust Generalized Category Discovery

Kailin Lyu, Jianwei He, Long Xiao +4

In open-world scenarios, Generalized Category Discovery (GCD) requires identifying both known and novel categories within unlabeled data. However, existing methods often suffer fro…