The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding

• URL: http://arxiv.org/abs/2311.17518v2
• Date: Fri, 5 Apr 2024 19:18:30 GMT
• Title: The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding
• Authors: Lorenzo Bianchi, Fabio Carrara, Nicola Messina, Claudio Gennaro, Fabrizio Falchi,
• Abstract summary: We introduce an evaluation protocol based on dynamic vocabulary generation to test whether models detect, discern, and assign the correct fine-grained description to objects. We further enhance our investigation by evaluating several state-of-the-art open-vocabulary object detectors using the proposed protocol.
• Score: 8.448399308205266
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in large vision-language models enabled visual object detection in open-vocabulary scenarios, where object classes are defined in free-text formats during inference. In this paper, we aim to probe the state-of-the-art methods for open-vocabulary object detection to determine to what extent they understand fine-grained properties of objects and their parts. To this end, we introduce an evaluation protocol based on dynamic vocabulary generation to test whether models detect, discern, and assign the correct fine-grained description to objects in the presence of hard-negative classes. We contribute with a benchmark suite of increasing difficulty and probing different properties like color, pattern, and material. We further enhance our investigation by evaluating several state-of-the-art open-vocabulary object detectors using the proposed protocol and find that most existing solutions, which shine in standard open-vocabulary benchmarks, struggle to accurately capture and distinguish finer object details. We conclude the paper by highlighting the limitations of current methodologies and exploring promising research directions to overcome the discovered drawbacks. Data and code are available at https://lorebianchi98.github.io/FG-OVD/.

Related papers

• Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection [101.15777242546649]
  Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowledge distillation to transfer insightful knowledge from pre-trained large-scale vision-language models to the task of object detection. We present a novel OVD framework termed LBP to propose learning background prompts to harness explored implicit background knowledge.
  arXiv  Detail & Related papers  (2024-06-01T17:32:26Z)
• Generative Region-Language Pretraining for Open-Ended Object Detection [55.42484781608621]
  We propose a framework named GenerateU, which can detect dense objects and generate their names in a free-form way. Our framework achieves comparable results to the open-vocabulary object detection method GLIP.
  arXiv  Detail & Related papers  (2024-03-15T10:52:39Z)
• LLMs Meet VLMs: Boost Open Vocabulary Object Detection with Fine-grained Descriptors [58.75140338866403]
  DVDet is a Descriptor-Enhanced Open Vocabulary Detector. It transforms regional embeddings into image-like representations that can be directly integrated into general open vocabulary detection training. Extensive experiments over multiple large-scale benchmarks show that DVDet outperforms the state-of-the-art consistently by large margins.
  arXiv  Detail & Related papers  (2024-02-07T07:26:49Z)
• Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning [13.667326007851674]
  We propose CastDet, a CLIP-activated student-teacher open-vocabulary object detection framework. Our approach boosts not only novel object proposals but also classification. Experimental results demonstrate our CastDet achieving superior open-vocabulary detection performance.
  arXiv  Detail & Related papers  (2023-11-20T10:26:04Z)
• Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey [10.665235711722076]
  Oriented object detection is one of the most fundamental and challenging tasks in remote sensing. Recent years have witnessed remarkable progress in oriented object detection using deep learning techniques.
  arXiv  Detail & Related papers  (2023-02-21T06:31:53Z)
• Learning Object-Language Alignments for Open-Vocabulary Object Detection [83.09560814244524]
  We propose a novel open-vocabulary object detection framework directly learning from image-text pair data. It enables us to train an open-vocabulary object detector on image-text pairs in a much simple and effective way.
  arXiv  Detail & Related papers  (2022-11-27T14:47:31Z)
• Exploiting Unlabeled Data with Vision and Language Models for Object Detection [64.94365501586118]
  Building robust and generic object detection frameworks requires scaling to larger label spaces and bigger training datasets. We propose a novel method that leverages the rich semantics available in recent vision and language models to localize and classify objects in unlabeled images. We demonstrate the value of the generated pseudo labels in two specific tasks, open-vocabulary detection and semi-supervised object detection.
  arXiv  Detail & Related papers  (2022-07-18T21:47:15Z)
• Learning Object Detection from Captions via Textual Scene Attributes [70.90708863394902]
  We argue that captions contain much richer information about the image, including attributes of objects and their relations. We present a method that uses the attributes in this "textual scene graph" to train object detectors. We empirically demonstrate that the resulting model achieves state-of-the-art results on several challenging object detection datasets.
  arXiv  Detail & Related papers  (2020-09-30T10:59:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.