--- license: cc-by-4.0 pipeline_tag: image-text-to-text library_name: transformers --- Welcome to **EmoCaliber**, an MLLM for reliable visual emotion comprehension. **Paper:** [EmoCaliber: Advancing Reliable Visual Emotion Comprehension via Confidence Verbalization and Calibration](https://huggingface.co/papers/2512.15528) **Code / Project Page:** [https://github.com/wdqqdw/EmoCaliber](https://github.com/wdqqdw/EmoCaliber) Given an image, EmoCaliber is trained to produce structured affective reasoning following this pipeline: (1) identifying prominent visual elements in the image; (2) providing detailed descriptions of human subjects, if present; (3) describing contextual elements beyond the subjects; (4) discussing how these elements interact; and (5) deriving an emotional conclusion based on the preceding observations. The final emotion prediction integrates these visual cues. After outputting the prediction, EmoCaliber also emits a confidence score wrapped in a \ tag, which reflects the model’s self-assessed certainty about its answer. EmoCaliber is implemented based on Qwen2.5-VL-7B and can perform both inference and training in an identical manner. Standard prompt templates: **For emotion recognition**: ```json { "conversations": [ { "role": "user", "content": [ {"type": "image", "image": "IMAGE_PATH"}, { "type": "text", "text": "Which emotion might this image evoke? Choose the most likely one from ['EMOTION_CATEGORIES']. Think step by step. Respond in the format: {your reasoning}{your final answer}." } ] } ] } ``` **For sentiment analysis**: ```json { "conversations": [ { "role": "user", "content": [ {"type": "image", "image": "IMAGE_PATH"}, { "type": "text", "text": "What sentiment might this image evoke? Choose the most likely one from ['positive', 'negative']. Think step by step. Respond in the format: {your reasoning}{your final answer}." } ] } ] } ```