pwc-archive/papers-with-abstracts huggingface dataset:
DoodleFormer: Creative Sketch Drawing with Transformers
Authors: Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Jorma Laaksonen, Michael Felsberg
Date: 2021-12-06 00:00:00
Tasks: Decoder, Image Generation

Creative sketching or doodling is an expressive activity, where imaginative and previously unseen depictions of everyday visual objects are drawn. Creative sketch image generation is a challenging vision problem, where the task is to generate diverse, yet realistic creative sketches possessing the unseen composition of the visual-world objects. Here, we propose a novel coarse-to-fine two-stage framework, DoodleFormer, that decomposes the creative sketch generation problem into the creation of coarse sketch composition followed by the incorporation of fine-details in the sketch. We introduce graph-aware transformer encoders that effectively capture global dynamic as well as local static structural relations among different body parts. To ensure diversity of the generated creative sketches, we introduce a probabilistic coarse sketch decoder that explicitly models the variations of each sketch body part to be drawn. Experiments are performed on two creative sketch datasets: Creative Birds and Creative Creatures. Our qualitative, quantitative and human-based evaluations show that DoodleFormer outperforms the state-of-the-art on both datasets, yielding realistic and diverse creative sketches. On Creative Creatures, DoodleFormer achieves an absolute gain of 25 in terms of Fr`echet inception distance (FID) over the state-of-the-art. We also demonstrate the effectiveness of DoodleFormer for related applications of text to creative sketch generation and sketch completion.
CSG0: Continual Urban Scene Generation with Zero Forgetting
Authors: Himalaya Jain, Tuan-Hung Vu, Patrick Pérez, Matthieu Cord
Date: 2021-12-06 00:00:00
Tasks: Continual Learning, Scene Generation, Transfer Learning

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains; ideally, the learned models should eventually be able to generate new scenes in all seen domains. This setup reflects the real-life scenario where data are continuously acquired in different places at different times. In such a continual setup, we aim for learning with zero forgetting, \IE, with no degradation in synthesis quality over earlier domains due to catastrophic forgetting. To this end, we introduce a novel framework that not only (i) enables seamless knowledge transfer in continual training but also (ii) guarantees zero forgetting with a small overhead cost. While being more memory efficient, thanks to continual learning, our model obtains better synthesis quality as compared against the brute-force solution that trains one full model for each domain. Especially, under extreme low-data regimes, our approach outperforms the brute-force one by a large margin.
Input-level Inductive Biases for 3D Reconstruction
Authors: Wang Yifan, Carl Doersch, Relja Arandjelović, João Carreira, Andrew Zisserman
Date: 2021-12-06 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D Reconstruction, Depth Estimation

Much of the recent progress in 3D vision has been driven by the development of specialized architectures that incorporate geometrical inductive biases. In this paper we tackle 3D reconstruction using a domain agnostic architecture and study how instead to inject the same type of inductive biases directly as extra inputs to the model. This approach makes it possible to apply existing general models, such as Perceivers, on this rich domain, without the need for architectural changes, while simultaneously maintaining data efficiency of bespoke models. In particular we study how to encode cameras, projective ray incidence and epipolar geometry as model inputs, and demonstrate competitive multi-view depth estimation performance on multiple benchmarks.
From Coarse to Fine-grained Concept based Discrimination for Phrase Detection
Authors: Maan Qraitem, Bryan A. Plummer
Date: 2021-12-06 00:00:00

Phrase detection requires methods to identify if a phrase is relevant to an image and localize it, if applicable. A key challenge for training more discriminative detection models is sampling negatives. Sampling techniques from prior work focus primarily on hard, often noisy, negatives disregarding the broader distribution of negative samples. Our proposed CFCD-Net addresses this through two novels methods. First, we generate groups of semantically similar words we call concepts (\eg, \{dog, cat, horse\} and \ \{car, truck, SUV\}), and then train our CFCD-Net to discriminate between a region of interest and its unrelated concepts. Second, for phrases containing fine-grained mutually-exclusive words (\eg, colors), we force the model to select only one applicable phrase for each region using our novel fine-grained module (FGM). We evaluate our approach on Flickr30K Entities and RefCOCO+, where we improve mAP over the state-of-the-art by 1.5-2 points. When considering only the phrases affected by our FGM module, we improve by 3-4 points on both datasets.
Text2Mesh: Text-Driven Neural Stylization for Meshes
Authors: Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, Rana Hanocka
Date: 2021-12-06 00:00:00
Proceeding: CVPR 2022 1
Tasks: Neural Stylization

In this work, we develop intuitive controls for editing the style of 3D objects. Our framework, Text2Mesh, stylizes a 3D mesh by predicting color and local geometric details which conform to a target text prompt. We consider a disentangled representation of a 3D object using a fixed mesh input (content) coupled with a learned neural network, which we term neural style field network. In order to modify style, we obtain a similarity score between a text prompt (describing style) and a stylized mesh by harnessing the representational power of CLIP. Text2Mesh requires neither a pre-trained generative model nor a specialized 3D mesh dataset. It can handle low-quality meshes (non-manifold, boundaries, etc.) with arbitrary genus, and does not require UV parameterization. We demonstrate the ability of our technique to synthesize a myriad of styles over a wide variety of 3D meshes.
Simultaneously Predicting Multiple Plant Traits from Multiple Sensors via Deformable CNN Regression
Authors: Pranav Raja, Alex Olenskyj, Hamid Kamangir, Mason Earles
Date: 2021-12-06 00:00:00
Tasks: regression

Trait measurement is critical for the plant breeding and agricultural production pipeline. Typically, a suite of plant traits is measured using laborious manual measurements and then used to train and/or validate higher throughput trait estimation techniques. Here, we introduce a relatively simple convolutional neural network (CNN) model that accepts multiple sensor inputs and predicts multiple continuous trait outputs - i.e. a multi-input, multi-output CNN (MIMO-CNN). Further, we introduce deformable convolutional layers into this network architecture (MIMO-DCNN) to enable the model to adaptively adjust its receptive field, model complex variable geometric transformations in the data, and fine-tune the continuous trait outputs. We examine how the MIMO-CNN and MIMO-DCNN models perform on a multi-input (i.e. RGB and depth images), multi-trait output lettuce dataset from the 2021 Autonomous Greenhouse Challenge. Ablation studies were conducted to examine the effect of using single versus multiple inputs, and single versus multiple outputs. The MIMO-DCNN model resulted in a normalized mean squared error (NMSE) of 0.068 - a substantial improvement over the top 2021 leaderboard score of 0.081. Open-source code is provided.
Semantic Segmentation In-the-Wild Without Seeing Any Segmentation Examples
Authors: Nir Zabari, Yedid Hoshen
Date: 2021-12-06 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Semantic segmentation is a key computer vision task that has been actively researched for decades. In recent years, supervised methods have reached unprecedented accuracy, however they require many pixel-level annotations for every new class category which is very time-consuming and expensive. Additionally, the ability of current semantic segmentation networks to handle a large number of categories is limited. That means that images containing rare class categories are unlikely to be well segmented by current methods. In this paper we propose a novel approach for creating semantic segmentation masks for every object, without the need for training segmentation networks or seeing any segmentation masks. Our method takes as input the image-level labels of the class categories present in the image; they can be obtained automatically or manually. We utilize a vision-language embedding model (specifically CLIP) to create a rough segmentation map for each class, using model interpretability methods. We refine the maps using a test-time augmentation technique. The output of this stage provides pixel-level pseudo-labels, instead of the manual pixel-level labels required by supervised methods. Given the pseudo-labels, we utilize single-image segmentation techniques to obtain high-quality output segmentation masks. Our method is shown quantitatively and qualitatively to outperform methods that use a similar amount of supervision. Our results are particularly remarkable for images containing rare categories.
HIVE: Evaluating the Human Interpretability of Visual Explanations
Authors: Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky
Date: 2021-12-06 00:00:00
Tasks: Decision Making, Diversity

As AI technology is increasingly applied to high-impact, high-risk domains, there have been a number of new methods aimed at making AI models more human interpretable. Despite the recent growth of interpretability work, there is a lack of systematic evaluation of proposed techniques. In this work, we introduce HIVE (Human Interpretability of Visual Explanations), a novel human evaluation framework that assesses the utility of explanations to human users in AI-assisted decision making scenarios, and enables falsifiable hypothesis testing, cross-method comparison, and human-centered evaluation of visual interpretability methods. To the best of our knowledge, this is the first work of its kind. Using HIVE, we conduct IRB-approved human studies with nearly 1000 participants and evaluate four methods that represent the diversity of computer vision interpretability works: GradCAM, BagNet, ProtoPNet, and ProtoTree. Our results suggest that explanations engender human trust, even for incorrect predictions, yet are not distinct enough for users to distinguish between correct and incorrect predictions. We open-source HIVE to enable future studies and encourage more human-centered approaches to interpretability research.
Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization
Authors: Yunhao Ge, Zhi Xu, Yao Xiao, Gan Xin, Yunkui Pang, Laurent Itti
Date: 2021-12-06 00:00:00
Tasks: Data Augmentation, Disentanglement, Fairness, Image Generation, Representation Learning

We focus on controllable disentangled representation learning (C-Dis-RL), where users can control the partition of the disentangled latent space to factorize dataset attributes (concepts) for downstream tasks. Two general problems remain under-explored in current methods: (1) They lack comprehensive disentanglement constraints, especially missing the minimization of mutual information between different attributes across latent and observation domains. (2) They lack convexity constraints, which is important for meaningfully manipulating specific attributes for downstream tasks. To encourage both comprehensive C-Dis-RL and convexity simultaneously, we propose a simple yet efficient method: Controllable Interpolation Regularization (CIR), which creates a positive loop where disentanglement and convexity can help each other. Specifically, we conduct controlled interpolation in latent space during training, and we reuse the encoder to help form a 'perfect disentanglement' regularization. In that case, (a) disentanglement loss implicitly enlarges the potential understandable distribution to encourage convexity; (b) convexity can in turn improve robust and precise disentanglement. CIR is a general module and we merge CIR with three different algorithms: ELEGANT, I2I-Dis, and GZS-Net to show the compatibility and effectiveness. Qualitative and quantitative experiments show improvement in C-Dis-RL and latent convexity by CIR. This further improves downstream tasks: controllable image synthesis, cross-modality image translation, and zero-shot synthesis.
Embedding Arithmetic of Multimodal Queries for Image Retrieval
Authors: Guillaume Couairon, Matthieu Cord, Matthijs Douze, Holger Schwenk
Date: 2021-12-06 00:00:00
Tasks: Image Retrieval, Image-text matching, Retrieval, Sentence, Text Matching

Latent text representations exhibit geometric regularities, such as the famous analogy: queen is to king what woman is to man. Such structured semantic relations were not demonstrated on image representations. Recent works aiming at bridging this semantic gap embed images and text into a multimodal space, enabling the transfer of text-defined transformations to the image modality. We introduce the SIMAT dataset to evaluate the task of Image Retrieval with Multimodal queries. SIMAT contains 6k images and 18k textual transformation queries that aim at either replacing scene elements or changing pairwise relationships between scene elements. The goal is to retrieve an image consistent with the (source image, text transformation) query. We use an image/text matching oracle (OSCAR) to assess whether the image transformation is successful. The SIMAT dataset will be publicly available. We use SIMAT to evaluate the geometric properties of multimodal embedding spaces trained with an image/text matching objective, like CLIP. We show that vanilla CLIP embeddings are not very well suited to transform images with delta vectors, but that a simple finetuning on the COCO dataset can bring dramatic improvements. We also study whether it is beneficial to leverage pretrained universal sentence encoders (FastText, LASER and LaBSE).
Diffusion Models for Implicit Image Segmentation Ensembles
Authors: Julia Wolleb, Robin Sandkühler, Florentin Bieder, Philippe Valmaggia, Philippe C. Cattin
Date: 2021-12-06 00:00:00
Tasks: Brain Tumor Segmentation, Image Segmentation, Lesion Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Diffusion models have shown impressive performance for generative modelling of images. In this paper, we present a novel semantic segmentation method based on diffusion models. By modifying the training and sampling scheme, we show that diffusion models can perform lesion segmentation of medical images. To generate an image specific segmentation, we train the model on the ground truth segmentation, and use the image as a prior during training and in every step during the sampling process. With the given stochastic sampling process, we can generate a distribution of segmentation masks. This property allows us to compute pixel-wise uncertainty maps of the segmentation, and allows an implicit ensemble of segmentations that increases the segmentation performance. We evaluate our method on the BRATS2020 dataset for brain tumor segmentation. Compared to state-of-the-art segmentation models, our approach yields good segmentation results and, additionally, detailed uncertainty maps.
Prototypical Model with Novel Information-theoretic Loss Function for Generalized Zero Shot Learning
Authors: Chunlin Ji, Hanchu Shen, Zhan Xiong, Feng Chen, Meiying Zhang, Huiwen Yang
Date: 2021-12-06 00:00:00
Tasks: Generalized Zero-Shot Learning, Relation, Transfer Learning, Zero-Shot Learning

Generalized zero shot learning (GZSL) is still a technical challenge of deep learning as it has to recognize both source and target classes without data from target classes. To preserve the semantic relation between source and target classes when only trained with data from source classes, we address the quantification of the knowledge transfer and semantic relation from an information-theoretic viewpoint. To this end, we follow the prototypical model and format the variables of concern as a probability vector. Leveraging on the proposed probability vector representation, the information measurement such as mutual information and entropy, can be effectively evaluated with simple closed forms. We discuss the choice of common embedding space and distance function when using the prototypical model. Then We propose three information-theoretic loss functions for deterministic GZSL model: a mutual information loss to bridge seen data and target classes; an uncertainty-aware entropy constraint loss to prevent overfitting when using seen data to learn the embedding of target classes; a semantic preserving cross entropy loss to preserve the semantic relation when mapping the semantic representations to the common space. Simulation shows that, as a deterministic model, our proposed method obtains state of the art results on GZSL benchmark datasets. We achieve 21%-64% improvements over the baseline model -- deep calibration network (DCN) and for the first time demonstrate a deterministic model can perform as well as generative ones. Moreover, our proposed model is compatible with generative models. Simulation studies show that by incorporating with f-CLSWGAN, we obtain comparable results compared with advanced generative models.
Ethics and Creativity in Computer Vision
Authors: Negar Rostamzadeh, Emily Denton, Linda Petrini
Date: 2021-12-06 00:00:00
Tasks: Ethics

This paper offers a retrospective of what we learnt from organizing the workshop *Ethical Considerations in Creative applications of Computer Vision* at CVPR 2021 conference and, prior to that, a series of workshops on *Computer Vision for Fashion, Art and Design* at ECCV 2018, ICCV 2019, and CVPR 2020. We hope this reflection will bring artists and machine learning researchers into conversation around the ethical and social dimensions of creative applications of computer vision.
General Facial Representation Learning in a Visual-Linguistic Manner
Authors: Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, Fang Wen
Date: 2021-12-06 00:00:00
Proceeding: CVPR 2022 1
Tasks: Face Alignment, Face Parsing, Representation Learning

How to learn a universal facial representation that boosts all face analysis tasks? This paper takes one step toward this goal. In this paper, we study the transfer performance of pre-trained models on face analysis tasks and introduce a framework, called FaRL, for general Facial Representation Learning in a visual-linguistic manner. On one hand, the framework involves a contrastive loss to learn high-level semantic meaning from image-text pairs. On the other hand, we propose exploring low-level information simultaneously to further enhance the face representation, by adding a masked image modeling. We perform pre-training on LAION-FACE, a dataset containing large amount of face image-text pairs, and evaluate the representation capability on multiple downstream tasks. We show that FaRL achieves better transfer performance compared with previous pre-trained models. We also verify its superiority in the low-data regime. More importantly, our model surpasses the state-of-the-art methods on face analysis tasks including face parsing and face alignment.
Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021
Authors: Hanna Siebert, Lasse Hansen, Mattias P. Heinrich
Date: 2021-12-06 00:00:00
Tasks: Deformable Medical Image Registration, Image Registration, Medical Image Registration

Current approaches for deformable medical image registration often struggle to fulfill all of the following criteria: versatile applicability, small computation or training times, and the being able to estimate large deformations. Furthermore, end-to-end networks for supervised training of registration often become overly complex and difficult to train. For the Learn2Reg2021 challenge, we aim to address these issues by decoupling feature learning and geometric alignment. First, we introduce a new very fast and accurate optimisation method. By using discretised displacements and a coupled convex optimisation procedure, we are able to robustly cope with large deformations. With the help of an Adam-based instance optimisation, we achieve very accurate registration performances and by using regularisation, we obtain smooth and plausible deformation fields. Second, to be versatile for different registration tasks, we extract hand-crafted features that are modality and contrast invariant and complement them with semantic features from a task-specific segmentation U-Net. With our results we were able to achieve the overall Learn2Reg2021 challenge's second place, winning Task 1 and being second and third in the other two tasks.
Scaling Up Influence Functions
Authors: Andrea Schioppa, Polina Zablotskaia, David Vilar, Artem Sokolov
Date: 2021-12-06 00:00:00
Tasks: image-classification, Image Classification

We address efficient calculation of influence functions for tracking predictions back to the training data. We propose and analyze a new approach to speeding up the inverse Hessian calculation based on Arnoldi iteration. With this improvement, we achieve, to the best of our knowledge, the first successful implementation of influence functions that scales to full-size (language and vision) Transformer models with several hundreds of millions of parameters. We evaluate our approach on image classification and sequence-to-sequence tasks with tens to a hundred of millions of training examples. Our code will be available at https://github.com/google-research/jax-influence.
Controllable Animation of Fluid Elements in Still Images
Authors: Aniruddha Mahapatra, Kuldeep Kulkarni
Date: 2021-12-06 00:00:00
Proceeding: CVPR 2022 1
Tasks: Generative Adversarial Network, Optical Flow Estimation

We propose a method to interactively control the animation of fluid elements in still images to generate cinemagraphs. Specifically, we focus on the animation of fluid elements like water, smoke, fire, which have the properties of repeating textures and continuous fluid motion. Taking inspiration from prior works, we represent the motion of such fluid elements in the image in the form of a constant 2D optical flow map. To this end, we allow the user to provide any number of arrow directions and their associated speeds along with a mask of the regions the user wants to animate. The user-provided input arrow directions, their corresponding speed values, and the mask are then converted into a dense flow map representing a constant optical flow map (FD). We observe that FD, obtained using simple exponential operations can closely approximate the plausible motion of elements in the image. We further refine computed dense optical flow map FD using a generative-adversarial network (GAN) to obtain a more realistic flow map. We devise a novel UNet based architecture to autoregressively generate future frames using the refined optical flow map by forward-warping the input image features at different resolutions. We conduct extensive experiments on a publicly available dataset and show that our method is superior to the baselines in terms of qualitative and quantitative metrics. In addition, we show the qualitative animations of the objects in directions that did not exist in the training set and provide a way to synthesize videos that otherwise would not exist in the real world.