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Prompt for scoring generations.

Gemini-prompt to generation caption given an image.

Fine-grained action localization in untrimmed sports videos presents a significant challenge due to rapid and subtle motion transitions over short durations. Existing supervised and weakly supervised solutions often rely on extensive annotated datasets and high-capacity models, making them computationally intensive and less adaptable to real-world scenarios. In this work, we introduce a lightweight and unsupervised skeleton-based action localization pipeline that leverages spatio-temporal graph neural representations.

In this work, we present a novel multi-agent framework for generating immersive 3D virtual environments from high-level semantic inputs, powered by large language and vision-language models (LLMs/VLMs). Unlike prior work that focuses primarily on visual output, data-intensive training pipelines, and code generation, our system coordinates a team of specialized agents, each assigned a role such as manager, planner, or expert in visual, audio, or spatial domains, to decompose and execute environment construction tasks within a game engine.

Appendix of our paper: "Rethinking the Backbone in Class Imbalanced Federated Source Free Domain Adaptation: The Utility of Vision Foundation Models" accepted at IEEE ICIP 2025 workshop: Edge Intelligence: Smart, Efficient, and Scalable Solutions for IoT, Wearables, and Embedded Devices (SEEDS)

We address the challenges of local feature matching under large scale and rotation changes by focusing on keypoint positions.
First, we propose a novel module called similarity normalization (SN).
This module normalizes keypoint positions to remove translation, rotation and scale differences between image pairs.
By performing positional encoding on these normalized positions, a network incorporating with SN can effectively avoid encoding largely different positions into descriptors from the two images.

This supplementary material accompanies our paper titled "Texturing Endoscopic 3D Stomach via Neural Radiance Field under Uneven Lighting."

MULTIMAE MEETS EARTH OBSERVATION: PRE-TRAINING MULTI-MODAL MULTI-TASK MASKED AUTOENCODERS FOR EARTH OBSERVATION TASKS (APPENDIX)

O1-mini prompt to expose the seven components of agricultural disease management evaluation framework

Supplementary Material.