Search Results for author:
Found 16 papers, 3 papers with code
Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model
In this paper, we introduce a novel method, detection prompt (DetPro), to learn continuous prompt representations for open-vocabulary object detection based on the pre-trained vision-language model.
MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning
Last, to enhance the embedding space learning, an additional pixel-wise metric learning module is introduced with triplet loss formulated on the pixel-level embedding of the input image.
Learning to Recommend Items to Wikidata Editors
The system uses a hybrid of content-based and collaborative filtering techniques to rank items for editors relying on both item features and item-editor previous interaction.
Detecting Human-Object Interaction with Mixed Supervision
Human object interaction (HOI) detection is an important task in image understanding and reasoning.
Fast Fourier Intrinsic Network
We address the problem of decomposing an image into albedo and shading.
Restoring Negative Information in Few-Shot Object Detection
In this paper, we restore the negative information in few-shot object detection by introducing a new negative- and positive-representative based metric learning framework and a new inference scheme with negative and positive representatives.
Defending Adversarial Examples via DNN Bottleneck Reinforcement
This paper presents a DNN bottleneck reinforcement scheme to alleviate the vulnerability of Deep Neural Networks (DNN) against adversarial attacks.
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer
We formulate the mutual transformations between the outputs of regression- and detection-based models as two scene-agnostic transformers which enable knowledge distillation between the two models.
Active Crowd Counting with Limited Supervision
To learn a reliable people counter from crowd images, head center annotations are normally required.
Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images
Weakly-supervised object detection attempts to limit the amount of supervision by dispensing the need for bounding boxes, but still assumes image-level labels on the entire training set.
Ranked #21 on
Weakly Supervised Object Detection
on PASCAL VOC 2012 test
(using extra training data)
Point in, Box out: Beyond Counting Persons in Crowds
In the end, we propose a curriculum learning strategy to train the network from images of relatively accurate and easy pseudo ground truth first.
Revisiting Perspective Information for Efficient Crowd Counting
Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions.
Crowd counting via scale-adaptive convolutional neural network
The task of crowd counting is to automatically estimate the pedestrian number in crowd images.
Weakly Supervised Object Localization Using Things and Stuff Transfer
We propose to help weakly supervised object localization for classes where location annotations are not available, by transferring things and stuff knowledge from a source set with available annotations.
Multiple Instance Learning
Weakly-Supervised Object Localization
Weakly Supervised Object Localization Using Size Estimates
We present a technique for weakly supervised object localization (WSOL), building on the observation that WSOL algorithms usually work better on images with bigger objects.
Early Burst Detection for Memory-Efficient Image Retrieval
Then, we show the interest of using this strategy in an asymmetrical manner, with only the database features being aggregated but not those of the query.
