Dynamic Multi-Attention Dehazing Network with Adaptive Feature Fusion

Gated Fusion Network for Single Image Dehazing

In this paper, we propose an efficient algorithm to directly restore a clear image from a hazy input. The proposed algorithm hinges on an end-to-end trainable neural network that consists of an encoder and a decoder. The encoder is exploited to capture the context of the derived input images, while the decoder is employed to estimate the contribution of each input to the final dehazed result us...

Single Scale image Dehazing by Multi Scale Fusion

-Because of the strong and successfulness, tampering of digital images depends upon the flexible architecture for a derivative estimation has become an important method to a spectacle for a large number of approaches. Regulated on an inherent range, the tampering of digital image procedure is varied and carried out. The beginning of a bridging origination which dilutes into a simple procedure. ...

Multi-view Feature Fusion Network for Vehicle Re- Identification

Identifying whether two vehicles in different images are same or not is called vehicle reidentification. In cities, there are lots of cameras, but cameras cannot cover all the areas. If we can re-identify a car disappearing from one camera and appearing in another in two adjacent regions, we can easily track the vehicle and use the information help with traffic management. In this paper, we pro...

Adaptive Multi-Sensor Fusion for Awareness in Dynamic Environments

A Cascaded Inception of Inception Network with Attention Modulated Feature Fusion for Human Pose Estimation

Accurate keypoint localization of human pose needs diversified features: the high level for contextual dependencies and low level for detailed refinement of joints. However, the importance of the two factors varies from case to case, but how to efficiently use the features is still an open problem. Existing methods have limitations in preserving low level features, adaptively adjusting the impo...