Real-world image restoration is hampered by diverse degradations stemming from varying capture conditions, capture devices and post-processing pipelines. Existing works make improvements through simulating those degradations and leveraging image generative priors, however generalization to in-the-wild data remains an unresolved problem. In this paper, we focus on complex degradations, i.e., arbitrary mixtures of multiple types of known degradations, which is frequently seen in the wild. A simple yet flexible diffusionbased framework, named UniRes, is proposed to address such degradations in an end-to-end manner. It combines several specialized models during the diffusion sampling steps, hence transferring the knowledge from several well-isolated restoration tasks to the restoration of complex in-the-wild degradations. This only requires well-isolated training data for several degradation types. The framework is flexible as extensions can be added through a unified formulation, and the fidelity-quality trade-off can be adjusted through a new paradigm. Our proposed method is evaluated on both complex-degradation and single-degradation image restoration datasets. Extensive qualitative and quantitative experimental results show consistent performance gain especially for images with complex degradations.

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic assessment (IAA) methods primarily rely on human-labeled rating scores, which oversimplify the visual aesthetic information that humans perceive. Conversely, user comments offer more comprehensive information and are a more natural way to express human opinions and preferences regarding image aesthetics. In light of this, we propose learning image aesthetics from user comments, and exploring vision-language pretraining methods to learn multimodal aesthetic representations. Specifically, we pretrain an image-text encoder-decoder model with image-comment pairs, using contrastive and generative objectives to learn rich and generic aesthetic semantics without human labels. To efficiently adapt the pretrained model for downstream IAA tasks, we further propose a lightweight rank-based adapter that employs text as an anchor to learn the aesthetic ranking concept. Our results show that our pretrained aesthetic vision-language model outperforms prior works on image aesthetic captioning over the AVA-Captions dataset, and it has powerful zero-shot capability for aesthetic tasks such as zero-shot style classification and zero-shot IAA, surpassing many supervised baselines. With only minimal finetuning parameters using the proposed adapter module, our model achieves state-of-the-art IAA performance over the AVA dataset.11Our model is available at https://github.com/google-research/google-research/tree/master/VILA

We propose the first mechanism to train object detection models from weak supervision in the form of captions at the image level. Language-based supervision for detection is appealing and inexpensive: many blogs with images and descriptive text written by human users exist. However, there is significant noise in this supervision: captions do not mention all objects that are shown, and may mention extraneous concepts. We first propose a technique to determine which image-caption pairs provide suitable signal for supervision. We further propose several complementary mechanisms to extract image-level pseudo labels for training from the caption. Finally, we train an iterative weakly-supervised object detection model from these image-level pseudo labels. We use captions from four datasets (COCO, Flickr30K, MIRFlickr1M, and Conceptual Captions) whose level of noise varies. We evaluate our approach on two object detection datasets. Weighting the labels extracted from different captions provides a boost over treating all captions equally. Further, our primary proposed technique for inferring pseudo labels for training at the image level, outperforms alternative techniques under a wide variety of settings. Both techniques generalize to datasets beyond the one they were trained on.

Visual reasoning and question-answering have gathered attention in recent years. Many datasets and evaluation protocols have been proposed; some have been shown to contain bias that allows models to ``cheat'' without performing true, generalizable reasoning. A well-known bias is dependence on language priors (frequency of answers) resulting in the model not looking at the image. We discover a new type of bias in the Visual Commonsense Reasoning (VCR) dataset. In particular we show that most state-of-the-art models exploit co-occurring text between input (question) and output (answer options), and rely on only a few pieces of information in the candidate options, to make a decision. Unfortunately, relying on such superficial evidence causes models to be very fragile. To measure fragility, we propose two ways to modify the validation data, in which a few words in the answer choices are modified without significant changes in meaning. We find such insignificant changes cause models' performance to degrade significantly. To resolve the issue, we propose a curriculum-based masking approach, as a mechanism to perform more robust training. Our method improves the baseline by requiring it to pay attention to the answers as a whole, and is more effective than prior masking strategies.

Prior work in scene graph generation requires categorical supervision at the level of triplets—subjects and objects, and predicates that relate them, either with or without bounding box information. However, scene graph generation is a holistic task: thus holistic, contextual supervision should intuitively improve performance. In this work, we explore how linguistic structures in captions can benefit scene graph generation. Our method captures the information provided in captions about relations between individual triplets, and context for subjects and objects (e.g. visual properties are mentioned). Captions are a weaker type of supervision than triplets since the alignment between the exhaustive list of human-annotated subjects and objects in triplets, and the nouns in captions, is weak. However, given the large and diverse sources of multimodal data on the web (e.g. blog posts with images and captions), linguistic supervision is more scalable than crowdsourced triplets. We show extensive experimental comparisons against prior methods which leverage instance- and image-level supervision, and ablate our method to show the impact of leveraging phrasal and sequential context, and techniques to improve localization of subjects and objects.

Visual reasoning is a challenging but important task that is gaining momentum. Examples include reasoning about what will happen next in film, or interpreting what actions an image advertisement prompts. Both tasks are "puzzles" which invite the viewer to combine knowledge from prior experience, to find the answer. Intuitively, providing external knowledge to a model should be helpful, but it does not necessarily result in improved reasoning ability. An algorithm can learn to find answers to the prediction task yet not perform generalizable reasoning. In other words, models can leverage "shortcuts" between inputs and desired outputs, to bypass the need for reasoning. We develop a technique to effectively incorporate external knowledge, in a way that is both interpretable, and boosts the contribution of external knowledge for multiple complementary metrics. In particular, we mask evidence in the image and in retrieved external knowledge. We show this masking successfully focuses the method's attention on patterns that generalize. To properly understand how our method utilizes external knowledge, we propose a novel side evaluation task. We find that with our masking technique, the model can learn to select useful knowledge pieces to rely on.

Visual media have important persuasive power, but prior computer vision approaches have predominantly ignored the persuasive aspects of images. In this work, we propose a suite of data and techniques that enable progress on understanding the messages that visual advertisements convey. We make available a dataset of 64,832 image ads and 3,477 video ads, annotated with ten types of information: the topic and sentiment of the ad; whether it is funny, exciting, or effective; what action it prompts the viewer to do, and what arguments it provides for why this action should be taken; symbolic associations that the ad relies on; the metaphorical object transformations on which especially creative ads rely; and the climax in video ads. We develop methods that use multimodal cues, i.e., both visuals and slogans, for both the image and video domains. Our methods rely on finding poignant content spatially and temporally. We also examine the creative story construction in ads: for videos, we learn to predict when the climax occurs (if any), and how effective the story is; for images, we analyze how object transformations in ads metaphorically depict product properties.

Learning to localize and name object instances is a fundamental problem in vision, but state-of-the-art approaches rely on expensive bounding box supervision. While weakly supervised detection (WSOD) methods relax the need for boxes to that of image-level annotations, even cheaper supervision is naturally available in the form of unstructured textual descriptions that users may freely provide when uploading image content. However, straightforward approaches to using such data for WSOD wastefully discard captions that do not exactly match object names. Instead, we show how to squeeze the most information out of these captions by training a text-only classifier that generalizes beyond dataset boundaries. Our discovery provides an opportunity for learning detection models from noisy but more abundant and freely-available caption data. We also validate our model on three classic object detection benchmarks and achieve state-of-the-art WSOD performance. Our code is available at https://github.com/yekeren/Cap2Det.

There is more to images than their objective physical content: for example, advertisements are created to persuade a viewer to take a certain action. We propose the novel problem of automatic advertisement understanding. To enable research on this problem, we create two datasets: an image dataset of 64,832 image ads, and a video dataset of 3,477 ads. Our data contains rich annotations encompassing the topic and sentiment of the ads, questions and answers describing what actions the viewer is prompted to take and the reasoning that the ad presents to persuade the viewer (What should I do according to this ad, and why should I do it?), and symbolic references ads make (e.g. a dove symbolizes peace). We also analyze the most common persuasive strategies ads use, and the capabilities that computer vision systems should have to understand these strategies. We present baseline classification results for several prediction tasks, including automatically answering questions about the messages of the ads.