arXiv Computer Vision
MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis
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arXiv Computer Vision
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Sparse-View Interpretable 3D Animal Behavior Representations for Neural Encoding and Decoding
arXiv:2609.36217v1 Announce Type: new Abstract: A deeper understanding of brain function requires a precise, structured characterization of behavior.Yet, extracting behavioral representations from vi...
arXiv AI
Jul 14
Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data
arXiv:2607. 09876v1 Announce Type: cross Abstract: Automatically retrieving videos from large camera-trap datasets remains challenging.
arXiv AI
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SERUM: State Extraction and Refinement for User Modeling
arXiv:2607. 29181v1 Announce Type: cross Abstract: Agentic assistants capable of proactive, personalized interactions require structured models of user intent and workflow.
arXiv AI
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K9-Bench: Evaluating Multimodal LLMs on Canine-Centric Videos
arXiv:2607. 02680v1 Announce Type: cross Abstract: MLLMs have shown strong zero-shot capabilities across diverse inputs such as across images, video, audio, and text.
arXiv Machine Learning
Jul 23
Agent-Centric Animal Pose Forecasting
arXiv:2607. 19548v1 Announce Type: new Abstract: Understanding animal behavior at an algorithmic level -- what animals attend to, how they form internal models and plans, and how this maps to action -- remains a central challenge in neuroscience and ethology.
arXiv Computer Vision
Sep 15
PhysBrain 1.5: From Vision-Language Models to Physical Foundation Models
PhysBrain 1.5 is a unified vision‑language model that learns to understand physical environments, generate actions, and predict future states by encoding language, end‑effector motion, and dense visual targets as discrete sequences and training them with autoregressive next‑token prediction. The model is pre‑trained on human interaction videos and fine‑tuned on human demonstrations, robot trajectories, and simulated experience, achieving an average score of 72.5 across 28 embodied understanding benchmarks and outperforming other open‑source models on 14 of them. It also demonstrates the ability to produce end‑effector trajectories and predict future scenes with spatially aligned RGB, depth, and robot‑mask outputs.