Text-based person retrieval faces a critical but under-explored challenge: the inherent uncertainty of query granularity in real-world scenarios. This paper introduces a new paradigm, Text-based Person Retrieval with Any Granularity, and provides a systematic solution.
arXiv:2609.12965v1 Announce Type: new Abstract: Text-based person search (TBPS) aims to retrieve images of a target person from a large image gallery based on a given natural language description. Mo...
arXiv:2504. 20734v5 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries.
arXiv:2608.23503v1 Announce Type: new Abstract: Text-based person anomaly search requires distinguishing individuals based on fine-grained, context-dependent behaviors rather than mere appearance. Ex...
arXiv:2608. 12843v1 Announce Type: cross Abstract: Text-based person anomaly retrieval aims to retrieve pedestrians exhibiting anomalous behaviors from a large image gallery using natural language descriptions.
arXiv:2608. 16161v1 Announce Type: cross Abstract: General-purpose text embedding models are designed to capture semantic similarity but are not optimised for distinguishing entity records that represent the same real-world business or person.
arXiv:2609.24372v1 Announce Type: new Abstract: In-context learning (ICL) based on large language models (LLMs) has shown promising potential in alleviating performance bottlenecks caused by the limi...
Granuscore is a reference‑free metric that measures the granularity of text by exploiting the structure of a hierarchical embedding space. It successfully reproduces known hierarchical orderings on the Granola‑EQ dataset, distinguishes granularity across different discourse contexts, and explains sentence‑specificity variations beyond sentence length. The authors also apply Granuscore to four question‑answering benchmarks, revealing systematic differences in granularity among questions, gold answers, and model outputs, thereby offering a new lens for assessing QA dataset difficulty.
arXiv:2606. 13550v1 Announce Type: new Abstract: Retrieval augmented generation (RAG) depends critically on the quality and granularity of retrieved evidence.
arXiv:2605. 27441v2 Announce Type: replace-cross Abstract: Query understanding in large-scale industrial search systems is typically implemented as a cascade of disparate, task-specific components.
arXiv:2606. 05181v1 Announce Type: cross Abstract: Natural Language Inference (NLI) is a fundamental task in natural language understanding that requires determining the logical relationship between a premise and a hypothesis.
Due to their strong generalizable multimodal processing and reasoning capabilities, Multimodal Large Language Models (MLLMs) have demonstrated significant potential as universal image retrievers, effectively addressing diverse real-world image retrieval tasks. Nevertheless, pioneering studies, while promising, overlook the potential of fine-grained context modeling and disentangled fine-tuning objectives in enhancing MLLMs' retrieval performance, particularly for complex tasks such as long-text-to-image retrieval, visual dialog retrieval, and composed image retrieval (CIR).
Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of documents. While large language models (LLMs) have preliminarily achieved promising performance in part of these tasks individually, their capability in event analysis still lacks comprehensive understanding due to restricted document granularity, task designs, and data source of existing benchmarks.