Revisiting RCAN: Improved Training for Image Super-Resolution

Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight. However, most SR models were optimized with dated training strategies. In this work, we revisit the popular RCAN model and examine the effect of different training options in SR. Surprisingly (or perhaps as expected), we show that RCAN can outperform or match nearly all the CNN-based SR architectures published after RCAN on standard benchmarks with a proper training strategy and minimal architecture change. Besides, although RCAN is a very large SR architecture with more than four hundred convolutional layers, we draw a notable conclusion that underfitting is still the main problem restricting the model capability instead of overfitting. We observe supportive evidence that increasing training iterations clearly improves the model performance while applying regularization techniques generally degrades the predictions. We denote our simply revised RCAN as RCAN-it and recommend practitioners to use it as baselines for future research. Code is publicly available at https://github.com/zudi-lin/rcan-it.

RePaint: Inpainting using Denoising Diffusion Probabilistic Models

Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks. RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions. Github Repository: git.io/RePaint

Collapse by Conditioning: Training Class-conditional GANs with Limited Data

Class-conditioning offers a direct means of controlling a Generative Adversarial Network (GAN) based on a discrete input variable. While necessary in many applications, the additional information provided by the class labels could even be expected to benefit the training of the GAN itself. Contrary to this belief, we observe that class-conditioning causes mode collapse in limited data settings, where unconditional learning leads to satisfactory generative ability. Motivated by this observation, we propose a training strategy for conditional GANs (cGANs) that effectively prevents the observed mode-collapse by leveraging unconditional learning. Our training strategy starts with an unconditional GAN and gradually injects conditional information into the generator and the objective function. The proposed method for training cGANs with limited data results not only in stable training but also in generating high-quality images, thanks to the early-stage exploitation of the shared information across classes. We analyze the aforementioned mode collapse problem in comprehensive experiments on four datasets. Our approach demonstrates outstanding results compared with state-of-the-art methods and established baselines. The code is available at: https://github.com/mshahbazi72/transitional-cGAN

End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization

Existing learning methods for LiDAR-based applications use 3D points scanned under a pre-determined beam configuration, e.g., the elevation angles of beams are often evenly distributed. Those fixed configurations are task-agnostic, so simply using them can lead to sub-optimal performance. In this work, we take a new route to learn to optimize the LiDAR beam configuration for a given application. Specifically, we propose a reinforcement learning-based learning-to-optimize (RL-L2O) framework to automatically optimize the beam configuration in an end-to-end manner for different LiDAR-based applications. The optimization is guided by the final performance of the target task and thus our method can be integrated easily with any LiDAR-based application as a simple drop-in module. The method is especially useful when a low-resolution (low-cost) LiDAR is needed, for instance, for system deployment at a massive scale. We use our method to search for the beam configuration of a low-resolution LiDAR for two important tasks: 3D object detection and localization. Experiments show that the proposed RL-L2O method improves the performance in both tasks significantly compared to the baseline methods. We believe that a combination of our method with the recent advances of programmable LiDARs can start a new research direction for LiDAR-based active perception. The code is publicly available at https://github.com/vniclas/lidar_beam_selection

Deep Facial Synthesis: A New Challenge

The goal of this paper is to conduct a comprehensive study on the facial sketch synthesis (FSS) problem. However, due to the high costs in obtaining hand-drawn sketch datasets, there lacks a complete benchmark for assessing the development of FSS algorithms over the last decade. As such, we first introduce a high-quality dataset for FSS, named FS2K, which consists of 2,104 image-sketch pairs spanning three types of sketch styles, image backgrounds, lighting conditions, skin colors, and facial attributes. FS2K differs from previous FSS datasets in difficulty, diversity, and scalability, and should thus facilitate the progress of FSS research. Second, we present the largest-scale FSS study by investigating 139 classical methods, including 24 handcrafted feature based facial sketch synthesis approaches, 37 general neural-style transfer methods, 43 deep image-to-image translation methods, and 35 image-to-sketch approaches. Besides, we elaborate comprehensive experiments for existing 19 cutting-edge models. Third, we present a simple baseline for FSS, named FSGAN. With only two straightforward components, i.e., facial-aware masking and style-vector expansion, FSGAN surpasses the performance of all previous state-of-the-art models on the proposed FS2K dataset, by a large margin. Finally, we conclude with lessons learned over the past years, and point out several unsolved challenges. Our open-source code is available at https://github.com/DengPingFan/FSGAN.

Flow-Guided Sparse Transformer for Video Deblurring

Exploiting similar and sharper scene patches in spatio-temporal neighborhoods is critical for video deblurring. However, CNN-based methods show limitations in capturing long-range dependencies and modeling non-local self-similarity. In this paper, we propose a novel framework, Flow-Guided Sparse Transformer (FGST), for video deblurring. In FGST, we customize a self-attention module, Flow-Guided Sparse Window-based Multi-head Self-Attention (FGSW-MSA). For each $query$ element on the blurry reference frame, FGSW-MSA enjoys the guidance of the estimated optical flow to globally sample spatially sparse yet highly related $key$ elements corresponding to the same scene patch in neighboring frames. Besides, we present a Recurrent Embedding (RE) mechanism to transfer information from past frames and strengthen long-range temporal dependencies. Comprehensive experiments demonstrate that our proposed FGST outperforms state-of-the-art (SOTA) methods on both DVD and GOPRO datasets and even yields more visually pleasing results in real video deblurring. Code and models will be released to the public.

Sound and Visual Representation Learning with Multiple Pretraining Tasks

Different self-supervised tasks (SSL) reveal different features from the data. The learned feature representations can exhibit different performance for each downstream task. In this light, this work aims to combine Multiple SSL tasks (Multi-SSL) that generalizes well for all downstream tasks. Specifically, for this study, we investigate binaural sounds and image data in isolation. For binaural sounds, we propose three SSL tasks namely, spatial alignment, temporal synchronization of foreground objects and binaural audio and temporal gap prediction. We investigate several approaches of Multi-SSL and give insights into the downstream task performance on video retrieval, spatial sound super resolution, and semantic prediction on the OmniAudio dataset. Our experiments on binaural sound representations demonstrate that Multi-SSL via incremental learning (IL) of SSL tasks outperforms single SSL task models and fully supervised models in the downstream task performance. As a check of applicability on other modality, we also formulate our Multi-SSL models for image representation learning and we use the recently proposed SSL tasks, MoCov2 and DenseCL. Here, Multi-SSL surpasses recent methods such as MoCov2, DenseCL and DetCo by 2.06%, 3.27% and 1.19% on VOC07 classification and +2.83, +1.56 and +1.61 AP on COCO detection. Code will be made publicly available.

Stochastic Layers in Vision Transformers

We introduce fully stochastic layers in vision transformers, without causing any severe drop in performance. The additional stochasticity boosts the robustness of visual features and strengthens privacy. In this process, linear layers with fully stochastic parameters are used, both during training and inference, to transform the feature activations of each multilayer perceptron. Such stochastic linear operations preserve the topological structure, formed by the set of tokens passing through the shared multilayer perceptron. This operation encourages the learning of the recognition task to rely on the topological structures of the tokens, instead of their values, which in turn offers the desired robustness and privacy of the visual features. In this paper, we use our features for three different applications, namely, adversarial robustness, network calibration, and feature privacy. Our features offer exciting results on those tasks. Furthermore, we showcase an experimental setup for federated and transfer learning, where the vision transformers with stochastic layers are again shown to be well behaved. Our source code will be made publicly available.

End-to-End Learning of Multi-category 3D Pose and Shape Estimation

In this paper, we study the representation of the shape and pose of objects using their keypoints. Therefore, we propose an end-to-end method that simultaneously detects 2D keypoints from an image and lifts them to 3D. The proposed method learns both 2D detection and 3D lifting only from 2D keypoints annotations. In this regard, a novel method that explicitly disentangles the pose and 3D shape by means of augmentation-based cyclic self-supervision is proposed, for the first time. In addition of being end-to-end in image to 3D learning, our method also handles objects from multiple categories using a single neural network. We use a Transformer-based architecture to detect the keypoints, as well as to summarize the visual context of the image. This visual context information is then used while lifting the keypoints to 3D, so as to allow the context-based reasoning for better performance. While lifting, our method learns a small set of basis shapes and their sparse non-negative coefficients to represent the 3D shape in canonical frame. Our method can handle occlusions as well as wide variety of object classes. Our experiments on three benchmarks demonstrate that our method performs better than the state-of-the-art. Our source code will be made publicly available.