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Found 22 papers, 7 papers with code
Heterogeneous Generative Knowledge Distillation with Masked Image Modeling
Masked image modeling (MIM) methods achieve great success in various visual tasks but remain largely unexplored in knowledge distillation for heterogeneous deep models.
Simple parameter-free self-attention approximation
The hybrid model of self-attention and convolution is one of the methods to lighten ViT.
Prompt Tuning Inversion for Text-Driven Image Editing Using Diffusion Models
This technique ensures a superior trade-off between editability and high fidelity to the input image of our method.
Language-aware Multiple Datasets Detection Pretraining for DETRs
Pretraining on large-scale datasets can boost the performance of object detectors while the annotated datasets for object detection are hard to scale up due to the high labor cost.
Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning
Based on this observation, we propose a simple strategy, i. e., increasing the number of training shots, to mitigate the loss of intrinsic dimension caused by robustness-promoting regularization.
CAE v2: Context Autoencoder with CLIP Target
That is to say, the smaller the model, the lower the mask ratio needs to be.
MAFormer: A Transformer Network with Multi-scale Attention Fusion for Visual Recognition
We develop a simple but effective module to explore the full potential of transformers for visual representation by learning fine-grained and coarse-grained features at a token level and dynamically fusing them.
Context Autoencoder for Self-Supervised Representation Learning
The pretraining tasks include two tasks: masked representation prediction - predict the representations for the masked patches, and masked patch reconstruction - reconstruct the masked patches.
Representation Disentanglement in Generative Models with Contrastive Learning
Recent works apply the contrastive learning on the discriminator of the Generative Adversarial Networks, and there exists little work on exploring if contrastive learning can be applied on encoders to learn disentangled representations.
Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning
In self-supervised learning frameworks, deep networks are optimized to align different views of an instance that contains the similar visual semantic information.
Layer-Wise Searching for 1-Bit Detectors
At each layer, it exploits a differentiable binarization search (DBS) to minimize the angular error in a student-teacher framework.
Oriented Object Detection with Transformer
Object detection with Transformers (DETR) has achieved a competitive performance over traditional detectors, such as Faster R-CNN.
Dual-stream Network for Visual Recognition
Transformers with remarkable global representation capacities achieve competitive results for visual tasks, but fail to consider high-level local pattern information in input images.
Probabilistic Ranking-Aware Ensembles for Enhanced Object Detections
This leads to a new problem of confidence discrepancy for the detector ensembles.
PAFNet: An Efficient Anchor-Free Object Detector Guidance
Therefore, a trade-off between effectiveness and efficiency is necessary in practical scenarios.
Ranked #1 on
Object Detection
on COCO test-dev
(Hardware Burden metric)
PP-YOLOv2: A Practical Object Detector
To meet these two concerns, we comprehensively evaluate a collection of existing refinements to improve the performance of PP-YOLO while almost keep the infer time unchanged.
Student-Teacher Feature Pyramid Matching for Anomaly Detection
Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies.
Ranked #19 on
Anomaly Detection
on VisA
HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network
Moreover, Hierarchical-Split block is very flexible and efficient, which provides a large space of potential network architectures for different applications.
The 1st Tiny Object Detection Challenge:Methods and Results
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.
PP-YOLO: An Effective and Efficient Implementation of Object Detector
We mainly try to combine various existing tricks that almost not increase the number of model parameters and FLOPs, to achieve the goal of improving the accuracy of detector as much as possible while ensuring that the speed is almost unchanged.
Ranked #137 on
Object Detection
on COCO test-dev
2nd Place Solution in Google AI Open Images Object Detection Track 2019
We present an object detection framework based on PaddlePaddle.
Learning from Large-scale Noisy Web Data with Ubiquitous Reweighting for Image Classification
In this manner, the influence of bias and noise in the web data can be gradually alleviated, leading to the steadily improving performance of URNet.
