Look and Modify: Modification Networks for Image Captioning

Attention-based neural encoder-decoder frameworks have been widely used for image captioning. Many of these frameworks deploy their full focus on generating the caption from scratch by relying solely on the image features or the object detection regional features. In this paper, we introduce a novel framework that learns to modify existing captions from a given framework by modeling the residual information, where at each timestep the model learns what to keep, remove or add to the existing caption allowing the model to fully focus on "what to modify" rather than on "what to predict". We evaluate our method on the COCO dataset, trained on top of several image captioning frameworks and show that our model successfully modifies captions yielding better ones with better evaluation scores.

Fingerprint Recognition under Missing Image Pixels Scenario

This work observed the problem of fingerprint image recognition in the case of missing pixels from the original image. The possibility of missing pixels recovery is tested by applying the Compressive Sensing approach. Namely, different percentage of missing pixels is observed and the image reconstruction is done by applying commonly used approach for sparse image reconstruction. The theory is verified by experiments, showing successful image reconstruction and later person identification even if less then 90% of the image pixels is missing.

Microscopy Image Restoration with Deep Wiener-Kolmogorov filters

Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes. Yet, the observed images of the objects at the micro-scale suffer from two major inherent distortions: the blur caused by the diffraction of light, and the background noise caused by the imperfections of the imaging detectors. The latter is especially severe in fluorescence and in confocal microscopes, which are known for operating at the low photon count with the Poisson noise statistics. Restoration of such images is usually accomplished by image deconvolution, with the nature of the noise statistics taken into account, and by solving an optimization problem given some prior information about the underlying data (i.e., regularization). In this work, we propose a unifying framework of algorithms for Poisson image deblurring and denoising. The algorithms are based on deep learning techniques for the design of learnable regularizers paired with an appropriate optimization scheme. Our extensive experimentation line showcases that the proposed approach achieves superior quality of image reconstruction and beats the solutions that rely on deep learning or on the optimization schemes alone. Moreover, several implementations of the proposed framework demonstrate competitive performance at a low computational complexity, which is of high importance for real-time imaging applications.

Spatio-Temporal Dual-Stream Neural Network for Sequential Whole-Body PET Segmentation

Sequential whole-body 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) scans are regarded as the imaging modality of choice for the assessment of treatment response in the lymphomas because they detect treatment response when there may not be changes on anatomical imaging. Any computerized analysis of lymphomas in whole-body PET requires automatic segmentation of the studies so that sites of disease can be quantitatively monitored over time. State-of-the-art PET image segmentation methods are based on convolutional neural networks (CNNs) given their ability to leverage annotated datasets to derive high-level features about the disease process. Such methods, however, focus on PET images from a single time-point and discard information from other scans or are targeted towards specific organs and cannot cater for the multiple structures in whole-body PET images. In this study, we propose a spatio-temporal 'dual-stream' neural network (ST-DSNN) to segment sequential whole-body PET scans. Our ST-DSNN learns and accumulates image features from the PET images done over time. The accumulated image features are used to enhance the organs / structures that are consistent over time to allow easier identification of sites of active lymphoma. Our results show that our method outperforms the state-of-the-art PET image segmentation methods.

Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks

Recent studies on unsupervised image-to-image translation have made a remarkable progress by training a pair of generative adversarial networks with a cycle-consistent loss. However, such unsupervised methods may generate inferior results when the image resolution is high or the two image domains are of significant appearance differences, such as the translations between semantic layouts and natural images in the Cityscapes dataset. In this paper, we propose novel Stacked Cycle-Consistent Adversarial Networks (SCANs) by decomposing a single translation into multi-stage transformations, which not only boost the image translation quality but also enable higher resolution image-to-image translations in a coarse-to-fine manner. Moreover, to properly exploit the information from the previous stage, an adaptive fusion block is devised to learn a dynamic integration of the current stage's output and the previous stage's output. Experiments on multiple datasets demonstrate that our proposed approach can improve the translation quality compared with previous single-stage unsupervised methods.

E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning

Vision-language pre-training (VLP) on large-scale image-text pairs has achieved huge success for the cross-modal downstream tasks. The most existing pre-training methods mainly adopt a two-step training procedure, which firstly employs a pre-trained object detector to extract region-based visual features, then concatenates the image representation and text embedding as the input of Transformer to train. However, these methods face problems of using task-specific visual representation of the specific object detector for generic cross-modal understanding, and the computation inefficiency of two-stage pipeline. In this paper, we propose the first end-to-end vision-language pre-trained model for both V+L understanding and generation, namely E2E-VLP, where we build a unified Transformer framework to jointly learn visual representation, and semantic alignments between image and text. We incorporate the tasks of object detection and image captioning into pre-training with a unified Transformer encoder-decoder architecture for enhancing visual learning. An extensive set of experiments have been conducted on well-established vision-language downstream tasks to demonstrate the effectiveness of this novel VLP paradigm.

Robust Contrastive Learning Using Negative Samples with Diminished Semantics

Unsupervised learning has recently made exceptional progress because of the development of more effective contrastive learning methods. However, CNNs are prone to depend on low-level features that humans deem non-semantic. This dependency has been conjectured to induce a lack of robustness to image perturbations or domain shift. In this paper, we show that by generating carefully designed negative samples, contrastive learning can learn more robust representations with less dependence on such features. Contrastive learning utilizes positive pairs that preserve semantic information while perturbing superficial features in the training images. Similarly, we propose to generate negative samples in a reversed way, where only the superfluous instead of the semantic features are preserved. We develop two methods, texture-based and patch-based augmentations, to generate negative samples. These samples achieve better generalization, especially under out-of-domain settings. We also analyze our method and the generated texture-based samples, showing that texture features are indispensable in classifying particular ImageNet classes and especially finer classes. We also show that model bias favors texture and shape features differently under different test settings. Our code, trained models, and ImageNet-Texture dataset can be found at https://github.com/SongweiGe/Contrastive-Learning-with-Non-Semantic-Negatives.

SPG-VTON: Semantic Prediction Guidance for Multi-pose Virtual Try-on

Image-based virtual try-on is challenging in fitting a target in-shop clothes into a reference person under diverse human poses. Previous works focus on preserving clothing details ( e.g., texture, logos, patterns ) when transferring desired clothes onto a target person under a fixed pose. However, the performances of existing methods significantly dropped when extending existing methods to multi-pose virtual try-on. In this paper, we propose an end-to-end Semantic Prediction Guidance multi-pose Virtual Try-On Network (SPG-VTON), which could fit the desired clothing into a reference person under arbitrary poses. Concretely, SPG-VTON is composed of three sub-modules. First, a Semantic Prediction Module (SPM) generates the desired semantic map. The predicted semantic map provides more abundant guidance to locate the desired clothes region and produce a coarse try-on image. Second, a Clothes Warping Module (CWM) warps in-shop clothes to the desired shape according to the predicted semantic map and the desired pose. Specifically, we introduce a conductible cycle consistency loss to alleviate the misalignment in the clothes warping process. Third, a Try-on Synthesis Module (TSM) combines the coarse result and the warped clothes to generate the final virtual try-on image, preserving details of the desired clothes and under the desired pose. Besides, we introduce a face identity loss to refine the facial appearance and maintain the identity of the final virtual try-on result at the same time. We evaluate the proposed method on the most massive multi-pose dataset (MPV) and the DeepFashion dataset. The qualitative and quantitative experiments show that SPG-VTON is superior to the state-of-the-art methods and is robust to the data noise, including background and accessory changes, i.e., hats and handbags, showing good scalability to the real-world scenario.

Multi-Scale Weight Sharing Network for Image Recognition

In this paper, we explore the idea of weight sharing over multiple scales in convolutional networks. Inspired by traditional computer vision approaches, we share the weights of convolution kernels over different scales in the same layers of the network. Although multi-scale feature aggregation and sharing inside convolutional networks are common in practice, none of the previous works address the issue of convolutional weight sharing. We evaluate our weight sharing scheme on two heterogeneous image recognition datasets - ImageNet (object recognition) and Places365-Standard (scene classification). With approximately 25% fewer parameters, our shared-weight ResNet model provides similar performance compared to baseline ResNets. Shared-weight models are further validated via transfer learning experiments on four additional image recognition datasets - Caltech256 and Stanford 40 Actions (object-centric) and SUN397 and MIT Inddor67 (scene-centric). Experimental results demonstrate significant redundancy in the vanilla implementations of the deeper networks, and also indicate that a shift towards increasing the receptive field per parameter may improve future convolutional network architectures.

ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data

In this paper, we introduce a new vision-language pre-trained model -- ImageBERT -- for image-text joint embedding. Our model is a Transformer-based model, which takes different modalities as input and models the relationship between them. The model is pre-trained on four tasks simultaneously: Masked Language Modeling (MLM), Masked Object Classification (MOC), Masked Region Feature Regression (MRFR), and Image Text Matching (ITM). To further enhance the pre-training quality, we have collected a Large-scale weAk-supervised Image-Text (LAIT) dataset from Web. We first pre-train the model on this dataset, then conduct a second stage pre-training on Conceptual Captions and SBU Captions. Our experiments show that multi-stage pre-training strategy outperforms single-stage pre-training. We also fine-tune and evaluate our pre-trained ImageBERT model on image retrieval and text retrieval tasks, and achieve new state-of-the-art results on both MSCOCO and Flickr30k datasets.