Image Roi Proposal , Object Proposals( 翻译成什么好 )

最新推荐文章于 2024-04-20 09:39:57 发布
最新推荐文章于 2024-04-20 09:39:57 发布
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物体检测通常涉及先提出大量的Roi以识别物体，以避免遗漏。Selective Search是最具影响力的Object Proposals算法之一，但MCG在Recall上表现最佳，而GoP速度最快，适合实时物体检测。尽管Matlab在该领域的研究中常用，但开源代码稀缺，GoP、MCG和Selective Search的代码要么是二进制的，要么被加密。

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Image Roi Proposal , Object Proposals( 翻译成什么好 )

标签（空格分隔）： vision

物体检测的套路还是需要先得到一些Roi，然后判断这些Roi中是什么物体。
为了防止有漏网之鱼，所以，一般在一幅图中会有上千个Roi。

Object Proposals有影响力的算法当然是Selective Search。但是这个领域进步很快，
从GoP的测试来看，目前Recall最好的应该是MCG（Berkeley的）,但是最快的应该是GoP。

Method prop ABO 70% Recall time
Selective Search 4374 0.735 0.597 2.6s
MCG 5158 0.807 0.772 30s
GOP (200,10)
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proposal embedding
最新发布
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### Proposal Embedding Implementation and Concept In the context of machine learning and natural language processing (NLP), proposal embeddings refer to representations used primarily within object detection frameworks but can also be extended to other domains where proposals or hypotheses need to be evaluated. These embeddings capture the essence of proposed regions or entities by encoding them into dense vectors that facilitate further analysis. #### Conceptual Overview The core idea behind proposal embeddings involves transforming raw data points—such as bounding boxes in computer vision tasks or candidate phrases in text processing—into feature-rich vector spaces. This transformation allows models to better understand spatial relationships between objects or contextual meanings among words/phrases[^1]. For instance, when dealing with images containing multiple potential targets, each region-of-interest (ROI) gets converted into an embedding through convolutional layers followed by pooling operations. Similarly, for textual content, named entity recognition systems might generate embeddings based on syntactic structures surrounding key terms. #### Technical Realization A common approach to implementing proposal embeddings includes: - **Feature Extraction**: Utilize pre-trained networks like ResNet or BERT depending upon whether working with visual or linguistic inputs. For example, using PyTorch's torchvision library: ```python import torch from torchvision import models resnet = models.resnet50(pretrained=True).eval() features_extractor = torch.nn.Sequential(*list(resnet.children())[:-2]) ``` - **Region Proposals Generation**: Apply algorithms such as Selective Search or Region Proposal Network (RPN). - **Embedding Computation**: Pass extracted features corresponding to individual ROIs through fully connected layers resulting in fixed-size output vectors representing those areas. An illustrative code snippet demonstrating this pipeline could look something along these lines: ```python def compute_proposal_embeddings(image_tensor, rois): """ Computes embeddings for given Regions Of Interest (ROIs). Args: image_tensor (torch.Tensor): Input tensor shaped according to model requirements. rois (List[List[int]]): List of ROI coordinates [[x_min, y_min, x_max, y_max], ...]. Returns: torch.Tensor: Tensor holding computed embeddings per ROI. """ # Extract base features from entire image all_features = features_extractor(image_tensor) roi_pooler = RoIPool(output_size=(7, 7)) pooled_regions = roi_pooler(all_features.unsqueeze(0), [rois]) flattened = pooled_regions.view(pooled_regions.size()[0], -1) fc_layer = torch.nn.Linear(flattened.shape[-1], 4096)(flattened) return fc_layer ``` This function takes both an input image represented as a tensor alongside lists specifying rectangular boundaries around interesting parts; it returns tensors filled with learned characteristics describing said segments effectively serving as their "proposals". --related questions-- 1. How do different architectures impact the quality of generated proposal embeddings? 2. What techniques exist for improving computational efficiency during large-scale batched generation processes involving numerous ROIs simultaneously? 3. Can you provide examples illustrating applications beyond standard use cases mentioned here? 4. Are there any specific challenges associated with fine-tuning pretrained models specifically aimed at generating high-quality proposal embeddings?
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