arXiv AI

THPL: A Vision-to-Language Decision Support Framework for Rainbow Trout Feeding Management in RAS

The paper introduces THPL, a vision-to-language framework for precision feeding of rainbow trout in recirculating aquaculture systems. It uses Fishsort to extract feeding trajectories, a Hierarchical Behavior Encoder to model individual and collective dynamics, and integrates these with environmental data and expert rules to fine‑tune a large language model via LoRA and counterfactual multimodal Direct Preference Optimization. Experimental results show strong correlation between the Activity Coefficient and expert feeding intensity, and significant improvements in decision accuracy and language metrics when using dual‑evidence tokens and counterfactual optimization.

arXiv Machine Learning

Sep 24

Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inference

The paper discusses how active learning (AL) can alleviate the expert annotation bottleneck in biodiversity monitoring by selecting the most informative samples under a fixed budget. It highlights that while AL reduces labeling effort, its non-random sample selection complicates model validation, calibration, and ecological inference, issues often overlooked in current studies. The authors review existing AL research across acoustic and image data, identify gaps such as limited species coverage and lack of real-world deployments, and propose a tutorial framework and roadmap for developing AL methods that support efficient training, reliable validation, and trustworthy ecological conclusions.

By Ben McEwen, Shiqi Zhang, Dan Stowell

multimodal benchmarks

Hugging Face Trending Papers

Aug 19

Teach a Molmo2Fish: Towards interactive fish tracking with natural language guidance

The paper introduces Molmo2Fish, an interactive tool that uses a multimodal large language model to correct imperfect fish tracking predictions in sonar datasets. It demonstrates that the system can effectively improve multi‑object tracking performance through natural language guidance, though further enhancements are needed. The authors provide open‑source code and data for replication.

llms computer-vision multimodal

arXiv Computer Vision

Aug 24

WildFin: An In-the-Wild Dataset for Fish Behavioral Recognition

arXiv:2608.21281v1 Announce Type: new Abstract: Recent advances in field technology have led to a massive influx of in-the-wild video data for ecological science. The primary bottleneck in leveraging...

By Abigail G. Grassick, Jerome Tze-Hou Hsu, Ethan Lin, Ziang Liu, Max Whitton, Madelyn Hair, Liam Gutierrez, Haozheng Yu, Kristin Branson, Vivek Jayaraman, Michael A. Gil, Andrew M. Hein, Jennifer J. Sun

computer-vision benchmarks

arXiv Machine Learning

Jul 28

HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows

arXiv:2607. 23983v1 Announce Type: cross Abstract: Operational flood forecasting depends on tacit forecaster expertise that is difficult to formalize, audit, and transfer.

By Qingyi Yang, Siqian Qiu, Bing Li, Xu Shan, Jia Feng, Shunan Zhou, Xudong Zhou, Tiantian Xing, Jiale Guo, Xiaoyi Dong, Gaoyu Liu, Xiaohuan Liu, Haiqing Pu, Qingwen Deng, Xun Zhang, Zhongrun Xiang, Haiyang Qian, Ying Yan, Yongkang Xu, Nuo Lei, Tianlong Jia, Baoying Shan, Carlo De Michele

llms agents benchmarks

arXiv Computer Vision

Aug 25

WADE: A Reasoning-Annotated Benchmark for Multi-Instance Floating-Waste Grounding with Compact Vision-Language Models

arXiv:2608.22950v1 Announce Type: new Abstract: Floating waste in inland waterways threatens aquatic ecosystems and requires timely monitoring under cluttered, multi-object conditions. Existing aquat...

By Md. Asaduzzaman Shuvo, Ahsan Farabi, Md. Abdul Ahad Minhaz, Mahedi Hasan, Israt Khandaker, Ibrahim Khalil Shanto, Muhammad Nomani Kabir

llms fine-tuning multimodal benchmarks safety

Hugging Face Trending Papers

Aug 24

WADE: A Reasoning-Annotated Benchmark for Multi-Instance Floating-Waste Grounding with Compact Vision-Language Models

Floating waste in inland waterways threatens aquatic ecosystems and requires timely monitoring under cluttered, multi-object conditions. Existing aquatic-waste datasets provide limited geographic cove...

llms fine-tuning multimodal benchmarks safety

arXiv AI

Jul 13

Parameter-Efficient Vision-Language Adaptation with Continuous Metadata Conditioning for Animal Re-Identification

arXiv:2607. 09443v1 Announce Type: cross Abstract: Long-term animal re-identification (ReID) must remain robust to gradual morphological evolution and seasonal appearance shifts.

By Anil Osman Tur, Tonje Knutsen Sordalen, Kim Tallaksen Halvorsen, Cigdem Beyan

llms rag multimodal benchmarks safety

arXiv Computer Vision

Sep 11

Does YOLO26 Truly Offer Advantages Over Its Predecessors for Edge Deployment? A Benchmark Study in Aquaculture

The paper evaluates the new YOLO26 architecture, which offers NMS-free end-to-end inference and is tailored for CPU-based edge devices, against three earlier Ultralytics models (YOLOv5u, YOLOv8, and YOLO11) in aquaculture fish mortality detection. Across nano, small, and medium scales, all models achieved similar detection accuracy on a full dataset, but differences emerged in data efficiency and deployment performance: YOLOv8 reached 90% mAP50 with only 400 images, while YOLO26 variants needed 1,000 images; YOLO26n was fastest on a Raspberry Pi 5 (7.51 FPS), whereas YOLOv5mu led on CPU-based hardware. The study concludes that architectural novelty alone does not dictate suitability for edge AI in aquaculture; training data size, target hardware, and inference needs must be jointly considered.

By Rakesh Ranjan, Gajanan S. Kothawade, Kata Sharrer, Scott Tsukuda, Christopher Good

computer-vision efficiency benchmarks

arXiv AI

Jul 21

PGN: Design and Implementation of a Vision-Language Navigation System Based on Pangu Multimodal Foundation Model

arXiv:2607. 17806v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) requires an embodied agent to interpret a natural-language instruction and predict actions from temporally ordered visual observations.

By Li Xian, Mingxi Li, Yizheng Wang, Yiming Shen, Qi Chen, Zhuoling Xiao

llms rag agents robotics fine-tuning multimodal safety

arXiv AI

Jul 10

OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice

arXiv:2607. 08423v1 Announce Type: new Abstract: The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary management.

By Qian Jiang, Zhecheng Shi, Jingpu Yang, Zirui Song, Miao Fang

llms agents multimodal benchmarks safety

arXiv Machine Learning

Jun 26

MORL-A2C: Multi-Objective Reinforcement Learning Reranker for Optimizing Healthiness in MOPI-HFRS

arXiv:2606. 23603v2 Announce Type: replace Abstract: Unhealthy dietary behavior continues to be a persistent public health issue in the United States, exacerbated by recommendation systems that prioritize user preference without considering nutritional health.

By Aarya Vasantlal, Joshua Zolla, Chuxu Zhang

rag reinforcement-learning benchmarks safety

arXiv AI

Sep 16

Neuro-Symbolic Hierarchical Intention Anticipation in Human Behavior

arXiv:2609.17064v1 Announce Type: new Abstract: Assistive autonomous systems must anticipate human goals before an observed behavior is complete. This article formulates anticipation as goal inferenc...

By Farnaz Soleimani (LISSI), Abdelghani Chibani (LISSI), Yacine Amirat (LISSI), Ghazaleh Khodabandelou (LISSI)

agents multimodal benchmarks

Related stories