4 citations · 13 across the 38 of their papers we have counts for
42 papers
RLHND: Video Foundation Models as Physically Grounded Hand Trackers for Robot Learning
Seungjun Moon, Subin Jeon, Sangwoo Kim +2
Recently, approaches that leverage human video datasets for robot policy training have become increasingly prevalent. However, most existing hand trackers regress pose from cropped…
STORM-Bench: Evaluating Online Video QA under Evolving and Incomplete Evidence
Siru Zhong, Shenghan Tan, Rihong Yan +6
Reliable online video question answering requires tracking state transitions while selectively abstaining when visual evidence is insufficient. Existing benchmarks focus on static…
CompKV: Compensation-Aware KV Selection for Long-Context LLM Inference
Zhen Huang, Ruizhe Yao, Danyi Liu +8
Despite their strong performance, large language models (LLMs) are bottlenecked by KV cache memory traffic during long-context inference. Sparse attention is widely used to acceler…
PREM: Prefix-Steered Recurrent Memory for Long-Video Understanding
Siru Zhong, Qiongyan Wang, Xiaohui Lv +5
Long-video understanding must capture transient visual evidence under strict token budgets, yet existing methods compress frames, append memory tokens, or alter internal key-value…
Topology-Aware Neighborhood Learning for Source-Free Cross-Scene Hyperspectral Image Classification
Qingmei Li, Juepeng Zheng, Jiarui Zhang +2
Domain adaptation has advanced cross-scene hyperspectral image classification, significantly improving discriminative capability in complex scenarios. However, privacy rules or sto…
Ground, Cover, and Refine: Evidence-Centric Frame Selection for Long-Video Question Answering
Fan Wei, Siru Zhong, Runmin Dong +3
Long-video question answering requires identifying sparse yet critical evidence from videos containing thousands of frames under a constrained visual-token budget. Existing methods…
Attend, Transform, or Silence: Operator-Level Visual Skipping for Efficient Multimodal LLM Inference
Zhaoyang Luo, Runmin Dong, Miao Yang +4
Multimodal large language models (MLLMs) increasingly process long visual-token sequences, increasing the overall inference computation. Existing acceleration methods usually remov…
Exascale Hybrid Numerical-AI Ensembles for Operational Flood-Season Forecasting in East Asia: 15-km Decadal Hindcasts and 1-km High-Resolution Capability
Mengxuan Chen, Yunpu Xu, Qiuyan Sun +19
Seasonal forecasting of summer rainfall in East Asia remains a grand challenge, as predictability at 3 to 6 month lead times is constrained by the spring predictability barrier, we…
Learning to Balance: Decoupled Siamese Diffusion Transformer for Reference-Based Remote Sensing Image Super-Resolution
Bin Luo, Runmin Dong, Zhaoyang Luo +4
Diffusion-based methods demonstrate significant potential for remote sensing image super-resolution at large scaling factors, particularly in reference-based super-resolution (RefS…
AgroTools: A Benchmark for Tool-Augmented Multimodal Agents in Agriculture
Zi Ye, Yibin Wen, Xiaoya Fan +10
Agricultural decision-making increasingly requires multimodal systems that can transform visual observations into reliable, executable actions. However, existing agricultural multi…
AgroVG: A Large-Scale Multi-Source Benchmark for Agricultural Visual Grounding
Haocheng Li, Juepeng Zheng, Zenghao Yang +5
Visual grounding, the task of localizing objects described by natural-language expressions, is a foundational capability for agricultural AI systems, enabling applications such as…
From Noisy Historical Maps to Time-Series Oil Palm Mapping Without Annotation in Malaysia and Indonesia (2020-2024)
Nuttaset Kuapanich, Juepeng Zheng, Bohan Shi +6
Accurate monitoring of oil palm plantations is critical for balancing economic development with environmental conservation in Southeast Asia. However, existing plantation maps ofte…
AIM: Asymmetric Information Masking for Visual Question Answering Continual Learning
Peifeng Zhang, Zice Qiu, Donghua Yu +4
In continual visual question answering (VQA), existing Continual Learning (CL) methods are mostly built for symmetric, unimodal architectures. However, modern Vision-Language Model…
GTPBD-MM: A Global Terraced Parcel and Boundary Dataset with Multi-Modality
Zhiwei Zhang, Xingyuan Zeng, Xinkai Kong +7
Agricultural parcel extraction plays an important role in remote sensing-based agricultural monitoring, supporting parcel surveying, precision management, and ecological assessment…
Beyond RGB: Benchmarking and Enhancing MLLMs for Hyperspectral Image Understanding via Training-Free Reasoning Framework
Xinyu Zhang, Zurong Mai, Qingmei Li +13
Multimodal Large Language Models (MLLMs) have achieved strong performance on RGB image understanding, yet their ability to use spectral evidence beyond the visible range remains la…
Parameter-Efficient Modality-Balanced Symmetric Fusion for Multimodal Remote Sensing Semantic Segmentation
Haocheng Li, Juepeng Zheng, Shuangxi Miao +4
Multimodal remote sensing semantic segmentation enhances scene interpretation by exploiting complementary physical cues from heterogeneous data. Although pretrained Vision Foundati…
AGCD: Agent-Guided Cross-Modal Decoding for Weather Forecasting
Jing Wu, Yang Liu, Lin Zhang +13
Accurate weather forecasting is more than grid-wise regression: it must preserve coherent synoptic structures and physical consistency of meteorological fields, especially under au…
AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning
Jiarui Zhang, Junqi Hu, Zurong Mai +10
Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial…
DiT-HC: Enabling Efficient Training of Visual Generation Model DiT on HPC-oriented CPU Cluster
Jinxiao Zhang, Yunpu Xu, Xiyong Wu +7
Generative foundation models have become an important tool for data reconstruction and simulation in scientific computing, showing a tight integration with traditional numerical si…
RS-Prune: Training-Free Data Pruning at High Ratios for Efficient Remote Sensing Diffusion Foundation Models
Fan Wei, Runmin Dong, Yushan Lai +8
Diffusion-based remote sensing (RS) generative foundation models are cruial for downstream tasks. However, these models rely on large amounts of globally representative data, which…
Task-Oriented Data Synthesis and Control-Rectify Sampling for Remote Sensing Semantic Segmentation
Yunkai Yang, Yudong Zhang, Kunquan Zhang +4
With the rapid progress of controllable generation, training data synthesis has become a promising way to expand labeled datasets and alleviate manual annotation in remote sensing…
SAGE: Style-Adaptive Generalization for Privacy-Constrained Semantic Segmentation Across Domains
Qingmei Li, Yang Zhang, Peifeng Zhang +2
Domain generalization for semantic segmentation aims to mitigate the degradation in model performance caused by domain shifts. However, in many real-world scenarios, we are unable…
AgroCoT: A Chain-of-Thought Benchmark for Evaluating Reasoning in Vision-Language Models for Agriculture
Yibin Wen, Qingmei Li, Zi Ye +14
Recent advancements in Vision-Language Models (VLMs) have significantly impacted various industries. In agriculture, these multimodal capabilities hold great promise for applicatio…
CrossEarth-Gate: Fisher-Guided Adaptive Tuning Engine for Efficient Adaptation of Cross-Domain Remote Sensing Semantic Segmentation
Shilei Cao, Ziyang Gong, Hehai Lin +10
In Remote Sensing (RS), Parameter-Efficient Fine-Tuning (PEFT) has emerged as a key approach to activate the generalizable representation ability of foundation models for downstrea…