Proceedings Article•
Deformable DETR: Deformable Transformers for End-to-End Object Detection
03 May 2021-
TL;DR: Deformable DETR as discussed by the authors proposes to only attend to a small set of key sampling points around a reference, which can achieve better performance than DETR with 10× less training epochs.
Abstract: DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10× less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach. Code shall be released.
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TL;DR: Zhang et al. as discussed by the authors proposed a plug-in head, named PSS head, which can be attached to any one-stage detectors to make them NMS-free.
Abstract: It is valuable and promising to remove post-processing non-maximum suppression (NMS) for object detectors, making detectors simpler and purely end-to-end. Removing NMS is possible if the object detector can identify only one positive sample for prediction for each ground-truth object instance in an image. In this work, we propose a compact and plug-in head, named PSS head, which can be attached to any one-stage detectors to make them NMS-free. Specifically, the PSS head works by automatically selecting a positive sample for each instance to be detected, so that the detectors with our PSS head can directly remove NMS. The success of our PSS head lies in three aspects, namely one-to-one label assignment, stop-gradient operation for eliminating optimization conflicts, and the pss loss and ranking loss specifically designed for the PSS head. Experiments on the COCO dataset demonstrate the effectiveness of our method. In particular, when compared with stage-of-the-art NMS-free methods, our (attaching PSS head to VFNET) achieves 44.0% mAP, which exceeds the 41.5% mAP of DeFCN with a large margin. When taking Res2Net-101-DCN as backbone network, our achieves 50.3% mAP on the COCO test set, which is a promising performance even among NMS-based methods.
Posted Content•
TL;DR: In this paper, a segmentation-based bounding box generation method for omnidirectional pedestrian detection is proposed, which enables detectors to tightly fit bounding boxes to pedestrians without omnidirectal images for training.
Abstract: We propose a segmentation-based bounding box generation method for omnidirectional pedestrian detection, which enables detectors to tightly fit bounding boxes to pedestrians without omnidirectional images for training. Because the appearance of pedestrians in omnidirectional images may be rotated to any angle, the performance of common pedestrian detectors is likely to be substantially degraded. Existing methods mitigate this issue by transforming images during inference or training detectors with omnidirectional images. However, the first approach substantially degrades the inference speed, and the second approach requires laborious annotations. To overcome these drawbacks, we leverage an existing large-scale dataset, whose segmentation annotations can be utilized, to generate tightly fitted bounding box annotations. We also develop a pseudo-fisheye distortion augmentation method, which further enhances the performance. Extensive analysis shows that our detector successfully fits bounding boxes to pedestrians and demonstrates substantial performance improvement.
Posted Content•
TL;DR: TxT as mentioned in this paper is a transformer-based cross-modal pipeline that enables fine-tuning both language and visual components on the downstream task in a fully end-to-end manner.
Abstract: Reasoning over multiple modalities, e.g. in Visual Question Answering (VQA), requires an alignment of semantic concepts across domains. Despite the widespread success of end-to-end learning, today's multimodal pipelines by and large leverage pre-extracted, fixed features from object detectors, typically Faster R-CNN, as representations of the visual world. The obvious downside is that the visual representation is not specifically tuned to the multimodal task at hand. At the same time, while transformer-based object detectors have gained popularity, they have not been employed in today's multimodal pipelines. We address both shortcomings with TxT, a transformer-based crossmodal pipeline that enables fine-tuning both language and visual components on the downstream task in a fully end-to-end manner. We overcome existing limitations of transformer-based detectors for multimodal reasoning regarding the integration of global context and their scalability. Our transformer-based multimodal model achieves considerable gains from end-to-end learning for multimodal question answering.
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TL;DR: Wang et al. as discussed by the authors proposed a softmax-free transformer (SOFT) to remove softmax in self-attention, which enables a full selfattention matrix to be approximated via a low-rank matrix decomposition.
Abstract: Vision transformers (ViTs) have pushed the state-of-the-art for various visual recognition tasks by patch-wise image tokenization followed by self-attention. However, the employment of self-attention modules results in a quadratic complexity in both computation and memory usage. Various attempts on approximating the self-attention computation with linear complexity have been made in Natural Language Processing. However, an in-depth analysis in this work shows that they are either theoretically flawed or empirically ineffective for visual recognition. We further identify that their limitations are rooted in keeping the softmax self-attention during approximations. Specifically, conventional self-attention is computed by normalizing the scaled dot-product between token feature vectors. Keeping this softmax operation challenges any subsequent linearization efforts. Based on this insight, for the first time, a softmax-free transformer or SOFT is proposed. To remove softmax in self-attention, Gaussian kernel function is used to replace the dot-product similarity without further normalization. This enables a full self-attention matrix to be approximated via a low-rank matrix decomposition. The robustness of the approximation is achieved by calculating its Moore-Penrose inverse using a Newton-Raphson method. Extensive experiments on ImageNet show that our SOFT significantly improves the computational efficiency of existing ViT variants. Crucially, with a linear complexity, much longer token sequences are permitted in SOFT, resulting in superior trade-off between accuracy and complexity.
Posted Content•
TL;DR: In this article, an iterative and progressive sampling strategy is proposed to locate discriminative regions, where the current sampling step is fed into a transformer encoder layer, and a group of sampling offsets is predicted to update the sampling locations for the next step.
Abstract: Transformers with powerful global relation modeling abilities have been introduced to fundamental computer vision tasks recently. As a typical example, the Vision Transformer (ViT) directly applies a pure transformer architecture on image classification, by simply splitting images into tokens with a fixed length, and employing transformers to learn relations between these tokens. However, such naive tokenization could destruct object structures, assign grids to uninterested regions such as background, and introduce interference signals. To mitigate the above issues, in this paper, we propose an iterative and progressive sampling strategy to locate discriminative regions. At each iteration, embeddings of the current sampling step are fed into a transformer encoder layer, and a group of sampling offsets is predicted to update the sampling locations for the next step. The progressive sampling is differentiable. When combined with the Vision Transformer, the obtained PS-ViT network can adaptively learn where to look. The proposed PS-ViT is both effective and efficient. When trained from scratch on ImageNet, PS-ViT performs 3.8% higher than the vanilla ViT in terms of top-1 accuracy with about $4\times$ fewer parameters and $10\times$ fewer FLOPs. Code is available at this https URL.
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123,388 citations
Proceedings Article•
01 Jan 2015TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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52,856 citations
20 Jun 2009
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Abstract: The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.
49,639 citations
06 Sep 2014
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
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30,462 citations