{
  "annotation_id": "6e6a3e4c-5634-4b13-8420-47813440b85d",
  "date_created": "2026-02-17T05:53:15.844000Z",
  "date_modified": "2026-02-17T05:53:15.844000Z",
  "file_hash": "d41b44b0993712e38f01ff26f051d6d7c3b73c7e995f7bf61dbd51c7b714629e",
  "private": false,
  "record": {
    "abstract": "Reliable zero-shot detection of out-of-distribution (OOD) inputs is critical for deploying vision-language models in open-world settings. However, the lack of labeled negatives in zero-shot OOD detection necessitates proxy signals that remain effective under distribution shift. Existing negative-label methods rely on a fixed set of textual proxies, which (i) sparsely sample the semantic space beyond in-distribution (ID) classes and (ii) remain static while only visual features drift, leading to cross-modal misalignment and unstable predictions. In this paper, we propose CoEvo, a training- and annotation-free test-time framework that performs bidirectional, sample-conditioned adaptation of both textual and visual proxies. Specifically, CoEvo introduces a proxy-aligned co-evolution mechanism to maintain two evolving proxy caches, which dynamically mines contextual textual negatives guided by test images and iteratively refines visual proxies, progressively realigning cross-modal similarities and enlarging local OOD margins. Finally, we dynamically re-weight the contributions of dual-modal proxies to obtain a calibrated OOD score that is robust to distribution shift. Extensive experiments on standard benchmarks demonstrate that CoEvo achieves state-of-the-art performance, improving AUROC by 1.33% and reducing FPR95 by 45.98% on ImageNet-1K compared to strong negative-label baselines.",
    "arxiv_id": "2601.08476",
    "authors": [
      "Hao Tang",
      "Yu Liu",
      "Shuanglin Yan",
      "Fei Shen",
      "Shengfeng He",
      "Jing Qin"
    ],
    "categories": [
      "cs.CV",
      "cs.MM"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Cross-modal Proxy Evolving for OOD Detection with Vision-Language Models",
    "url": "https://arxiv.org/abs/2601.08476",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "2bb73f2f-2ced-4c15-bbc1-e29e0cd71b05",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}