From Imitation to Intuition: Intrinsic Reasoning for Open-Instance Video Classification

TL;DR

DeepIntuit introduces an intrinsic reasoning framework that evolves open-instance video classification from imitation to intuition, followed by refinement using Group Relative Policy Optimization (GRPO) to enhance reasoning coherence through reinforcement learning.

Abstract

Conventional video classification models, acting as effective imitators, excel in scenarios with homogeneous data distributions. However, real-world applications often present an open-instance challenge, where intra-class variations are vast and complex, beyond existing benchmarks. While traditional video encoder models struggle to fit these diverse distributions, vision-language models (VLMs) offer superior generalization but have not fully leveraged their reasoning capabilities (intuition) for such tasks. In this paper, we bridge this gap with an intrinsic reasoning framework that evolves open-instance video classification from imitation to intuition. Our approach, namely DeepIntuit, begins with a cold-start supervised alignment to initialize reasoning capability, followed by refinement using Group Relative Policy Optimization (GRPO) to enhance reasoning coherence through reinforcement learning. Crucially, to translate this reasoning into accurate classification, DeepIntuit then introduces an intuitive calibration stage. In this stage, a classifier is trained on this intrinsic reasoning traces generated by the refined VLM, ensuring stable knowledge transfer without distribution mismatch. Extensive experiments demonstrate that for open-instance video classification, DeepIntuit benefits significantly from transcending simple feature imitation and evolving toward intrinsic reasoning. Our project is available at https://bwgzk-keke.github.io/DeepIntuit/.

Figures (7)

• Figure 1: Overview of DeepIntuit. Unlike conventional classifiers that rely on direct input-to-label mapping, DeepIntuit evolves open-instance video classification from imitation to intuition. Through staged training, it develops intrinsic reasoning that enables stable and calibrated decisions.
• Figure 2: Close-instance vs. open-instance video classification. (a) Close-instance benchmarks have relatively homogeneous intra-class distributions. (b) Open-instance settings exhibit broader, open-ended intra-class variation that better reflects real-world data. (c) Consequently, conventional video encoders fit close-instance data well but struggle to generalize, whereas VLMs with stronger semantic priors are more robust in the open-instance regime.
• Figure 3: Pipeline of DeepIntuit. The framework follows three stages: (1) cold-start supervised alignment for initializing reasoning capability, (2) GRPO-based reinforcement learning to refine intrinsic reasoning, and (3) intuitive calibration that translates intrinsic reasoning into stable and calibrated final decisions.
• Figure 4: Effect of calibration and reasoning model choice.Left: Initializing Stage-3 from the Stage-2 model yields a $>10\%$ F1 improvement compared with using an external VLM model. Right: DeepIntuit-S$_2$, trained with cold-start supervised alignment and GRPO, consistently outperforms the baseline reasoning model (e.g., Qwen2.5-VL) across categories.
• Figure 5: Qualitative examples on open-instance videos. The refined model generates structured intrinsic reasoning (e.g., observations and context) before predicting the final label. The examples show accurate classification of both normal and abnormal events, illustrating robust open-instance generalization in real-world scenarios.