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Found 9 papers, 3 papers with code
Online Convolutional Re-parameterization
Structural re-parameterization has drawn increasing attention in various computer vision tasks.
Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-identification
Assisted with the camera-aware proxies, we design two proxy-level contrastive learning losses that are, respectively, based on offline and online association results.
Camera-aware Proxies for Unsupervised Person Re-Identification
These camera-aware proxies enable us to deal with large intra-ID variance and generate more reliable pseudo labels for learning.
Learning to Generate Content-Aware Dynamic Detectors
To the best of our knowledge, our CADDet is the first work to introduce dynamic routing mechanism in object detection.
Towards Precise Intra-camera Supervised Person Re-identification
Our approach performs even comparable to state-of-the-art fully supervised methods in two of the datasets.
Deep Active Learning for Video-based Person Re-identification
With the gained annotations of the actively selected candidates, the tracklets' pesudo labels are updated by label merging and further used to re-train our re-ID model.
Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream.
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
To address this issue, this paper integrates saliency into a deep architecture, in which the location in- formation is explored both explicitly and implicitly.
Saliency Guided Dictionary Learning for Weakly-Supervised Image Parsing
In this paper, we propose a novel method to perform weakly-supervised image parsing based on the dictionary learning framework.
