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Learning Object-Language Alignments for Open-Vocabulary Object Detection

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arxiv 2211.14843 v1 pith:BSCS5NNL submitted 2022-11-27 cs.CV

Chuang Lin , Peize Sun , Yi Jiang , Ping Luo , Lizhen Qu , Gholamreza Haffari , Zehuan Yuan , Jianfei Cai This is my paper
classification cs.CV
keywords objectdetectionopen-vocabularyimage-textlearningnovelobject-languagealignments
verification ladder T0 review T1 audit T2 compute T3 formal
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Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding box annotations. Natural language supervision is an attractive alternative for its annotation-free attributes and broader object concepts. However, learning open-vocabulary object detection from language is challenging since image-text pairs do not contain fine-grained object-language alignments. Previous solutions rely on either expensive grounding annotations or distilling classification-oriented vision models. In this paper, we propose a novel open-vocabulary object detection framework directly learning from image-text pair data. We formulate object-language alignment as a set matching problem between a set of image region features and a set of word embeddings. It enables us to train an open-vocabulary object detector on image-text pairs in a much simple and effective way. Extensive experiments on two benchmark datasets, COCO and LVIS, demonstrate our superior performance over the competing approaches on novel categories, e.g. achieving 32.0% mAP on COCO and 21.7% mask mAP on LVIS. Code is available at: https://github.com/clin1223/VLDet.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Open-Vocabulary Gaze Object Prediction: Benchmark and Method

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A COCO+GazeFollow-derived benchmark (86 categories) plus a Grounding DINO + gaze-selection pipeline with selective tuning improves open-vocabulary gaze object prediction over existing closed-vocabulary methods.

  2. Fine-grained CLIP fine-tuning with self-annotated region alignment

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Fine-tuning CLIP with self-generated region-phrase pairs from caption heatmaps improves dense region understanding without any region annotations, while preserving global retrieval.

  3. Unveiling the Unknown: Open Vocabulary Object Detection with Scene Graphs

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    A new framework improves open-vocabulary object detection on novel categories by using scene graphs for relational modeling and distilling knowledge from image captions.

  4. DeCo-DETR: Decoupled Cognition DETR for efficient Open-Vocabulary Object Detection

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    DeCo-DETR builds hierarchical semantic prototypes offline and uses decoupled training streams to deliver competitive zero-shot open-vocabulary detection with improved inference speed.

  5. Grad-ECLIP: Gradient-based Visual and Textual Explanations for CLIP

    cs.CV 2025-02 conditional novelty 6.0 of 10

    Grad-ECLIP produces gradient-based visual and textual explanation heatmaps for CLIP by applying channel and spatial weights to token features instead of relying on sparse self-attention maps.

  6. DeCo-DETR: Decoupled Cognition DETR for efficient Open-Vocabulary Object Detection

    cs.CV 2026-04 unverdicted novelty 5.0 of 10

    DeCo-DETR constructs a hierarchical semantic prototype space from LVLM-generated descriptions aligned via CLIP and uses decoupled training streams to separate semantic reasoning from detection, yielding efficient open...

  7. Group Relative Augmentation for Data Efficient Action Detection

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A LoRA plus FiLM feature-augmentation method with a group-weighted loss reports modest few-shot action detection gains on AVA and MOMA, but the evidence for the weighting component is weak.

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