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

Source: [T-Rex-Omni: Integrating Negative Visual Prompt in Generic Object Detection](<https://www.sota2.com/research/paper/691565e50052cac1dc65d6c6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-14T05:02:00.116Z

Authors: Jiazhou Zhou, Qing Jiang, Kanghao Chen, Lutao Jiang, Yuanhuiyi Lyu, Ying-Cong Chen, Lei Zhang

Published: 2025-11-12T05:38:56.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2511.08997>)

[PDF](<https://arxiv.org/pdf/2511.08997.pdf>)