Open AccessProceedings Article
Deformable DETR: Deformable Transformers for End-to-End Object Detection
Reads0
Chats0
TLDR
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.read more
Citations
More filters
Posted Content
Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World
TL;DR: Trans4Trans as mentioned in this paper is a wearable system with a dual-head Transformer for Transparency model, which is capable of segmenting general and transparent objects and performing real-time wayfinding to assist people walking alone more safely.
Posted Content
RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition
TL;DR: RAMS-Trans as discussed by the authors uses the self-attention to recursively learn discriminative region attention in a multi-scale manner, which can be easily trained end-to-end.
Posted Content
FQ-ViT: Fully Quantized Vision Transformer without Retraining
TL;DR: FQ-ViT as mentioned in this paper proposes Log-Int-Softmax (LIS) to sustain the extreme non-uniform distribution of the attention maps while simplifying inference by using 4-bit quantization and the BitShift operator.
Posted Content
Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity
TL;DR: In this article, the authors proposed Sparse DETR that selectively updates only the tokens expected to be referenced by the decoder, thus improving the performance of the model while minimizing computational overhead.
Posted Content
K-Net: Towards Unified Image Segmentation
TL;DR: K-Net as discussed by the authors segment both instances and semantic categories consistently by a group of learnable kernels, where each kernel is responsible for generating a mask for either a potential instance or a stuff class.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;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.
Proceedings Article
Attention is All you Need
Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N. Gomez,Lukasz Kaiser,Illia Polosukhin +7 more
TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
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.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
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.