reviews: 
Comments: This paper proposes an approach to boost open-vocabulary object detector with fine-grained language descriptors of the categories. The motivation is that existing works learn to align region embeddings with category labels only, disregarding the capability of VLM to align with object parts and other fine-grained descriptions. The fine-grained text descriptors are generated with an LLM in an interactive manner. In addition, the paper introduces conditional context prompt to augment region embeddings with contextual cues and make them more image-like for open-vocabulary detection.
Strengths: The two approaches presented by this paper are intuitive and effective. The context prompt makes the region more image-like to aid open-vocabulary recognition and the use of LLM provides additional features for VLM to reliably detect novel classes. The main results in Table 1 and 2 show the effectiveness of proposed approaches on LVIS and COCO benchmarks based on many existing methods. Ablations show that each part is effective on OV-COCO.
Weaknesses: 1. The idea of using LLM to generate more textual description has been explored in recent/concurrent work [2]. It'd be great to see a comparison with the existing ideas. Similarly, the idea of context regional prompt has been explored in recent/concurrent work [1], although the exact instantiation may differ. It'd be helpful to tease apart the contribution of context (which makes the crop more image-like) vs prompting (which adapts the features for detection use cases) through an ablation, and compare/discuss the similarity/differences with existing work. I understand the references listed are very recent/concurrent works, but I think it'd still be valuable to have some comparison with them for scientific understanding.

2. Although DVDet shows gains for all methods in Table 1 and 2. The gains on base categories are non-trivial and even comparable to the novel categories in most cases. For example, DVDet + Detic has +1.8 boost on mAPf and +1.3 on mAPr, and +2.5 on Base AP vs +1.7 on Novel AP. Given the method is based on Detic, I'm wondering what the implication of the gains on base vs novel categories are, since the method is primarily designed for open-vocabulary detection. 

3. Table 3 (row 2) shows that adding fine-grained descriptors by itself without prompting hurts the performance of the model. This seems counter-intuitive to me. It'd be helpful to see if the same observation holds for other OVD models e.g. ViLD or Detic.

4. Table 4 shows transfer detection from COCO to PASCAL and LVIS. It's nice to see a clear boost there. I'd recommend trying out the LVIS-trained model on Objects365 instead since it's a more commonly used transfer detection setting by e.g. ViLD, DetPro, F-VLM. 

5. In Eq (2), the $\textit{m, n}$ are said to be constants. How are they set? I'm wondering if they should be set proportional to the width and height of the ROI. Another option is to use the whole image box as a baseline and see how that performs. Some ablations/analysis on the choice of context would be interesting.

References:
1. CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching (CVPR 2023)
2. Multi-Modal Classifiers for Open-Vocabulary Object Detection (ICML 2023)
Recommendation: 6: marginally above the acceptance threshold
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 3 good
Clarity: 3 good