編織視覺敘事：超越原子級視覺匹配的代理式影像組合

摘要

影像檢索傳統上被表述為一個逐點匹配問題，其中每個候選影像皆被獨立評分。然而，這種原子化範式無法捕捉個人照片收藏中人類搜尋意圖的複雜性——使用者往往追尋的是由結構性關係串聯而成的緊湊視覺故事，而非孤立的單張快照。為了解決此限制，我們提出了**影像束組合（IBC）**，一個新穎的範式，將目標從對個別影像進行排名，轉變為從大量非結構化照片池中動態組合成具有凝聚力的影像束。由於目標影像束並非預先定義，IBC 帶來了嚴重的組合爆炸挑戰，並要求對不可分解的聯合相關性進行建模。為了確立此範式，我們建構了 **IBCBench**，這是第一個 IBC 基準資料集，包含 109,467 張影像與 667 個經驗證的查詢，並透過半自動化驗證流程建立。此外，我們提出了 **BundleWeaver**，一個代理式框架，將 IBC 重新表述為查詢條件下的增量式超邊發現。透過運用大型語言模型自適應地搜尋缺失的關係角色，並利用視覺語言模型進行整體影像束驗證，BundleWeaver 得以有效地導航組合空間。大量實驗表明，儘管最先進的嵌入模型與靜態分解再排序範式存在關係盲點，BundleWeaver 仍取得了顯著的效能提升，凸顯了從原子化評分轉向動態關係組合的必要性。我們的資料集與程式碼均已公開。

English

Image retrieval has traditionally been formulated as a point-wise matching problem, where each candidate image is scored in isolation. However, this atomic paradigm fails to capture the complexity of human search intent within personal photo collections, where users often seek compact visual stories bound by structural relations rather than isolated snapshots. To address this limitation, we introduce **Image Bundle Composition (IBC)**, a novel paradigm that shifts the objective from ranking individual images to dynamically composing cohesive image bundles from a massive, unstructured photo pool. Since target bundles are not predefined, IBC presents a severe combinatorial explosion challenge and demands modeling non-decomposable joint relevance. To establish this paradigm, we construct **IBCBench**, the first IBC benchmark dataset containing 109,467 images and 667 verified queries, built via a semi-automated verification pipeline. Furthermore, we propose **BundleWeaver**, an agentic framework that reformulates IBC as query-conditioned incremental hyperedge discovery. By employing a Large Language Model to adaptively search for missing relational roles and utilizing a Vision-Language Model for whole-bundle verification, BundleWeaver effectively navigates the combinatorial space. Extensive experiments demonstrate that while state-of-the-art embedding models and static decompose-and-rerank paradigms suffer from relational blindness, BundleWeaver achieves substantial performance gains, highlighting the necessity of shifting from atomic scoring to dynamic relational composition. Our dataset and code are available.