1 citations · 1 across the 15 of their papers we have counts for
16 papers
RemoteZero: Geospatial Reasoning with Zero Labels
Liang Yao, Fan Liu, Shengxiang Xu +4
Geospatial reasoning requires models to identify image regions that satisfy complex and often implicit user intents. Recent reinforcement learning approaches improve reasoning with…
Evaluating Remote Sensing Image Captions Beyond Metric Biases
Ziyun Chen, Fan Liu, Liang Yao +3
The core objective of image captioning is to achieve lossless semantic compression from visual signals into textual modalities. However, the reliance on manually curated reference…
RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation
Rui Min, Liang Yao, Shiyu Miao +5
A robust Multimodal Large Language Model (MLLM) for Earth Observation should maintain consistent interpretation and reasoning under realistic input variations. However, current Rem…
RemoteAgent: Bridging Vague Human Intents and Earth Observation with RL-based Agentic MLLMs
Liang Yao, Shengxiang Xu, Fan Liu +7
Earth Observation (EO) systems are essentially designed to support domain experts who often express their requirements through vague natural language rather than precise, machine-f…
Disentangle Object and Non-object Infrared Features via Language Guidance
Fan Liu, Ting Wu, Chuanyi Zhang +3
Infrared object detection focuses on identifying and locating objects in complex environments (\eg, dark, snow, and rain) where visible imaging cameras are disabled by poor illumin…
Label-Noise Resistant Learning via Optimal Brain Damage Masking
Xinlei Zhang, Fan Liu, Chuanyi Zhang +4
Noisy labels are inevitable in real-world multimedia applications. Due to the strong memorization capacity of deep neural networks, these noisy labels cause significant performance…
RemoteReasoner: Towards Unifying Geospatial Reasoning Workflow
Liang Yao, Fan Liu, Hongbo Lu +5
Remote sensing imagery presents vast, inherently unstructured spatial data, necessitating sophisticated reasoning to interpret complex user intents and contextual relationships bey…
RemoteSAM: Towards Segment Anything for Earth Observation
Liang Yao, Fan Liu, Delong Chen +6
We aim to develop a robust yet flexible visual foundation model for Earth observation. It should possess strong capabilities in recognizing and localizing diverse visual targets wh…
Chain-of-Talkers (CoTalk): Fast Human Annotation of Dense Image Captions
Yijun Shen, Delong Chen, Fan Liu +4
While densely annotated image captions significantly facilitate the learning of robust vision-language alignment, methodologies for systematically optimizing human annotation effor…
RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image Classification
Guangwenjie Zou, Liang Yao, Fan Liu +5
Since high resolution remote sensing image classification often requires a relatively high computation complexity, lightweight models tend to be practical and efficient. Model prun…
Robust Noisy Correspondence Learning via Self-Drop and Dual-Weight
Fan Liu, Chenwei Dong, Chuanyi Zhang +2
Many researchers collect data from the internet through crowd-sourcing or web crawling to alleviate the data-hungry challenge associated with cross-modal matching. Although such pr…
Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing Images
Shiyu Miao, Delong Chen, Fan Liu +4
The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where…
Domain-invariant Progressive Knowledge Distillation for UAV-based Object Detection
Liang Yao, Fan Liu, Chuanyi Zhang +2
Knowledge distillation (KD) is an effective method for compressing models in object detection tasks. Due to limited computational capability, UAV-based object detection (UAV-OD) wi…
Making Large Vision Language Models to be Good Few-shot Learners
Fan Liu, Wenwen Cai, Jian Huo +3
Few-shot classification (FSC) is a fundamental yet challenging task in computer vision that involves recognizing novel classes from limited data. While previous methods have focuse…
UEMM-Air: Make Unmanned Aerial Vehicles Perform More Multi-modal Tasks
Liang Yao, Fan Liu, Shengxiang Xu +6
The development of multi-modal learning for Unmanned Aerial Vehicles (UAVs) typically relies on a large amount of pixel-aligned multi-modal image data. However, existing datasets f…
Boost UAV-based Ojbect Detection via Scale-Invariant Feature Disentanglement and Adversarial Learning
Fan Liu, Liang Yao, Chuanyi Zhang +4
Detecting objects from Unmanned Aerial Vehicles (UAV) is often hindered by a large number of small objects, resulting in low detection accuracy. To address this issue, mainstream a…