The model is finetuned in two steps to optimize for the text embedding and the UNet that reconstruct the input image and its inverse perspective mapping respectively.
Our algorithm utilizes the motion bias within aerial videos, which enables the selection of motion-salient frames.
Ranked #1 on Action Recognition on UAV-Human
Aerial Diffusion leverages a pretrained text-image diffusion model for prior knowledge.
We build a differentiable static-dynamic frequency mask prior to model the salient static and dynamic pixels in the video, crucial for the underlying task of action recognition.
We compare our method, which we call PAAK, with prior approaches, including POSA, PROX ground truth, and a motion synthesis method, and highlight the benefits of our method with a perceptual study.
SALAD has three key benefits: (i) it is task-agnostic, and can be applied across various visual tasks such as classification, segmentation and detection; (ii) it can handle shifts in output label space from the pre-trained source network to the target domain; (iii) it does not require access to source data for adaptation.
Our formulation uses a novel Fourier object disentanglement method to innately separate out the human agent (which is typically small) from the background.
Ranked #1 on Action Recognition on UAV Human
We interface GANav with a deep reinforcement learning-based navigation algorithm and highlight its benefits in terms of navigation in real-world unstructured terrains.
Ranked #1 on Semantic Segmentation on RUGD
We present a novel approach for unsupervised road segmentation in adverse weather conditions such as rain or fog.
Practical autonomous driving systems face two crucial challenges: memory constraints and domain gap issues.
We present an unsupervised adaptation approach for visual scene understanding in unstructured traffic environments.
no code implementations • 18 Aug 2020 • Yuqian Zhou, Michael Kwan, Kyle Tolentino, Neil Emerton, Sehoon Lim, Tim Large, Lijiang Fu, Zhihong Pan, Baopu Li, Qirui Yang, Yihao Liu, Jigang Tang, Tao Ku, Shibin Ma, Bingnan Hu, Jiarong Wang, Densen Puthussery, Hrishikesh P. S, Melvin Kuriakose, Jiji C. V, Varun Sundar, Sumanth Hegde, Divya Kothandaraman, Kaushik Mitra, Akashdeep Jassal, Nisarg A. Shah, Sabari Nathan, Nagat Abdalla Esiad Rahel, Dafan Chen, Shichao Nie, Shuting Yin, Chengconghui Ma, Haoran Wang, Tongtong Zhao, Shanshan Zhao, Joshua Rego, Huaijin Chen, Shuai Li, Zhenhua Hu, Kin Wai Lau, Lai-Man Po, Dahai Yu, Yasar Abbas Ur Rehman, Yiqun Li, Lianping Xing
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
In this work, we present Deep Atrous Guided Filter (DAGF), a two-stage, end-to-end approach for image restoration in UDC systems.