From Hindsight to Foresight: Self-Encouraged Hindsight Distillation for Knowledge-based Visual Question Answering
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911559183237120 |
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| author | Zhao, Yu Zhang, Ying Sui, Xuhui Zhou, Baohang Shen, Li Tao, Dacheng |
| author_facet | Zhao, Yu Zhang, Ying Sui, Xuhui Zhou, Baohang Shen, Li Tao, Dacheng |
| contents | Knowledge-based Visual Question Answering (KBVQA) necessitates external knowledge incorporation beyond cross-modal understanding. Existing KBVQA methods either utilize implicit knowledge in multimodal large language models (MLLMs) via in-context learning or explicit knowledge via retrieval augmented generation. However, their reasoning processes remain implicit, without explicit multi-step trajectories from MLLMs. To address this gap, we provide a Hindsight Distilled Reasoning (HinD) framework with Knowledge Encouragement Preference Optimization, aiming at self-encouraging the knowledge reasoning ability inside the MLLM. First, we construct the Hindsight Teacher by prompting the MLLM to complete the reasoning process with knowing the right answer, obtaining Hindsight-Zero training data. Then, the Foresight Student, without knowing the answer, learns the golden trajectories from Hindsight: (1) Hindsight Distillation Fine-Tuning (HDFT) to self-distill the Hindsight-Zero into a modularized Chain-of-Thought (CoT) Generator and a Knowledge Generator for sequential steps and discrete facts generation, respectively; (2) Knowledge Encouragement Preference Optimization (KEPO) to encourage the under-confident but relevant knowledge inside the MLLM and suppress the over-confident but irrelevant one. Experiments on OK-VQA and A-OKVQA validate the effectiveness of HinD, showing that HinD with 7-8B MLLM achieves superior performance without commercial model APIs or retrieved knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11132 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Hindsight to Foresight: Self-Encouraged Hindsight Distillation for Knowledge-based Visual Question Answering Zhao, Yu Zhang, Ying Sui, Xuhui Zhou, Baohang Shen, Li Tao, Dacheng Computer Vision and Pattern Recognition Knowledge-based Visual Question Answering (KBVQA) necessitates external knowledge incorporation beyond cross-modal understanding. Existing KBVQA methods either utilize implicit knowledge in multimodal large language models (MLLMs) via in-context learning or explicit knowledge via retrieval augmented generation. However, their reasoning processes remain implicit, without explicit multi-step trajectories from MLLMs. To address this gap, we provide a Hindsight Distilled Reasoning (HinD) framework with Knowledge Encouragement Preference Optimization, aiming at self-encouraging the knowledge reasoning ability inside the MLLM. First, we construct the Hindsight Teacher by prompting the MLLM to complete the reasoning process with knowing the right answer, obtaining Hindsight-Zero training data. Then, the Foresight Student, without knowing the answer, learns the golden trajectories from Hindsight: (1) Hindsight Distillation Fine-Tuning (HDFT) to self-distill the Hindsight-Zero into a modularized Chain-of-Thought (CoT) Generator and a Knowledge Generator for sequential steps and discrete facts generation, respectively; (2) Knowledge Encouragement Preference Optimization (KEPO) to encourage the under-confident but relevant knowledge inside the MLLM and suppress the over-confident but irrelevant one. Experiments on OK-VQA and A-OKVQA validate the effectiveness of HinD, showing that HinD with 7-8B MLLM achieves superior performance without commercial model APIs or retrieved knowledge. |
| title | From Hindsight to Foresight: Self-Encouraged Hindsight Distillation for Knowledge-based Visual Question Answering |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.11132 |