When Do Language-Grounded Explanations Help? A Graph-Bottleneck for Farm Monitoring Interpretable Sheep Facial Pain

arXiv:2609.20427 · cs.CV, cs.AI · Submitted 2026-09-17 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-17

Updated: 2026-09-17

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 44× in this paper · explained in 1 episode
• Ablation — 13× in this paper · explained in 1 episode
• Adaptation — 13× in this paper · explained in 1 episode
• Embeddings — 10× in this paper · explained in 2 episodes
• Interpretability — 10× in this paper · explained in 15 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Sheep Pain Facial Expression Scale — 2× in this paper
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Affective Computing — 1× in this paper · explained in 1 episode
• Anti-correlation — 1× in this paper · explained in 3 episodes
• Conference on Automatic Face and Gesture Recognition — 1× in this paper
• Explainability — 1× in this paper · explained in 3 episodes
• Generalization Bounds — 1× in this paper · explained in 2 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Semi-Supervised Classification with Graph Convolutional Networks
• Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization

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