T-Rex-Omni: Integrating Negative Visual Prompt in Generic Object Detection

• URL: http://arxiv.org/abs/2511.08997v1
• Date: Thu, 13 Nov 2025 01:24:57 GMT
• Title: T-Rex-Omni: Integrating Negative Visual Prompt in Generic Object Detection
• Authors: Jiazhou Zhou, Qing Jiang, Kanghao Chen, Lutao Jiang, Yuanhuiyi Lyu, Ying-Cong Chen, Lei Zhang,
• Abstract summary: T-Rex- Omni is a novel framework that incorporates negative visual prompts to negate hard negative distractors.<n>We show remarkable zero-shot detection performance, significantly narrowing the performance gap between visual-prompted and text-prompted methods.<n>This work establishes negative prompts as a crucial new dimension for advancing open-set visual recognition systems.
• Score: 44.49740244062374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object detection methods have evolved from closed-set to open-set paradigms over the years. Current open-set object detectors, however, remain constrained by their exclusive reliance on positive indicators based on given prompts like text descriptions or visual exemplars. This positive-only paradigm experiences consistent vulnerability to visually similar but semantically different distractors. We propose T-Rex-Omni, a novel framework that addresses this limitation by incorporating negative visual prompts to negate hard negative distractors. Specifically, we first introduce a unified visual prompt encoder that jointly processes positive and negative visual prompts. Next, a training-free Negating Negative Computing (NNC) module is proposed to dynamically suppress negative responses during the probability computing stage. To further boost performance through fine-tuning, our Negating Negative Hinge (NNH) loss enforces discriminative margins between positive and negative embeddings. T-Rex-Omni supports flexible deployment in both positive-only and joint positive-negative inference modes, accommodating either user-specified or automatically generated negative examples. Extensive experiments demonstrate remarkable zero-shot detection performance, significantly narrowing the performance gap between visual-prompted and text-prompted methods while showing particular strength in long-tailed scenarios (51.2 AP_r on LVIS-minival). This work establishes negative prompts as a crucial new dimension for advancing open-set visual recognition systems.

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