GenAI4ELab/papercli-papers huggingface dataset:
SemAug: Semantically Meaningful Image Augmentations for Object Detection through Language Grounding
Authors: Morgan Heisler; Amin Banitalebi-Dehkordi; Yong Zhang
Venue: ECCV 2022 (main)

Abstract: "Data augmentation is an essential technique in improving the generalization of deep neural networks. The majority of existing image-domain augmentations either rely on geometric and structural transformations, or apply different kinds of photometric distortions. In this paper, we propose an effective technique for image augmentation by injecting contextually meaningful knowledge into the scenes. Our method of semantically meaningful image augmentation for object detection via language grounding, SemAug, starts by calculating semantically appropriate new objects that can be placed into relevant locations in the image (the what and where problems). Then it embeds these objects into their relevant target locations, thereby promoting diversity of object instance distribution. Our method allows for introducing new object instances and categories that may not even exist in the training set. Furthermore, it does not require the additional overhead of training a context network, so it can be easily added to existing architectures. Our comprehensive set of evaluations showed that the proposed method is very effective in improving the generalization, while the overhead is negligible. In particular, for a wide range of model architectures, our method achieved 2-4% and 1-2% mAP improvements for the task of object detection on the Pascal VOC and COCO datasets, respectively. Code is available as supplementary."
Referring Object Manipulation of Natural Images with Conditional Classifier-Free Guidance
Authors: Myungsub Choi
Venue: ECCV 2022 (main)

Abstract: "We introduce the problem of referring object manipulation (ROM), which aims to generate photo-realistic image edits regarding two textual descriptions: 1) a text referring to an object in the input image and 2) a text describing how to manipulate the referred object. A successful ROM model would enable users to simply use natural language to manipulate images, removing the need for learning sophisticated image editing software. We present one of the first approach to address this challenging multi-modal problem by combining a referring image segmentation method with a text-guided diffusion model. Specifically, we propose a conditional classifier-free guidance scheme to better guide the diffusion process along the direction from the referring expression to the target prompt. In addition, we provide a new localized ranking method and further improvements to make the generated edits more robust. Experimental results show that the proposed framework can serve as a simple but strong baseline for referring object manipulation. Also, comparisons with several baseline text-guided diffusion models demonstrate the effectiveness of our conditional classifier-free guidance technique."
NewsStories: Illustrating Articles with Visual Summaries
Authors: Reuben Tan; Bryan A. Plummer; Kate Saenko; JP Lewis; Avneesh Sud; Thomas Leung
Venue: ECCV 2022 (main)

Abstract: "Recent self-supervised approaches have used large-scale image-text datasets to learn powerful representations that transfer to many tasks without finetuning. These methods often assume that there is one-to-one correspondence between its images and their (short) captions. However, many tasks require reasoning about multiple images and long text narratives, such as describing news articles with visual summaries. Thus, we explore a novel setting where the goal is to learn a self-supervised visual-language representation that is robust to varying text length and the number of images. In addition, unlike prior work which assumed captions have a literal relation to the image, we assume images only contain loose illustrative correspondence with the text. To explore this problem, we introduce a large-scale multimodal dataset containing over 31M articles, 22M images and 1M videos. We show that state-of-the-art image-text alignment methods are not robust to longer narratives with multiple images. Finally, we introduce an intuitive baseline that outperforms these methods on zero-shot image-set retrieval by 10% on the GoodNews dataset."
Webly Supervised Concept Expansion for General Purpose Vision Models
Authors: Amita Kamath; Christopher Clark; Tanmay Gupta; Eric Kolve; Derek Hoiem; Aniruddha Kembhavi
Venue: ECCV 2022 (main)

Abstract: "General purpose vision (GPV) systems are models that are designed to solve a wide array of visual tasks without requiring architectural changes. Today, GPVs primarily learn both skills and concepts from large fully supervised datasets. Scaling GPVs to tens of thousands of concepts by acquiring data to learn each concept for every skill quickly becomes prohibitive. This work presents an effective and inexpensive alternative: learn skills from supervised datasets, learn concepts from web image search, and leverage a key characteristic of GPVs -- the ability to transfer visual knowledge across skills. We use a dataset of 1M+ images spanning 10k+ visual concepts to demonstrate webly-supervised concept expansion for two existing GPVs (GPV-1 and VL-T5) on 3 benchmarks - 5 COCO based datasets (80 primary concepts), a newly curated series of 5 datasets based on the OpenImages and VisualGenome repositories ( 500 concepts) and the Web-derived dataset (10k+ concepts). We also propose a new architecture, GPV-2 that supports a variety of tasks -- from vision tasks like classification and localization to vision+language tasks like QA and captioning to more niche ones like human-object interaction detection. GPV-2 benefits hugely from web data and outperforms GPV-1 and VL-T5 across these benchmarks."
FedVLN: Privacy-Preserving Federated Vision-and-Language Navigation
Authors: Kaiwen Zhou; Xin Eric Wang
Venue: ECCV 2022 (main)

Abstract: "Data privacy is a central problem for embodied agents that can perceive the environment, communicate with humans, and act in the real world. While helping humans complete tasks, the agent may observe and process sensitive information of users, such as house environments, human activities, etc. In this work, we introduce privacy-preserving embodied agent learning for the task of Vision-and-Language Navigation (VLN), where an embodied agent navigates house environments by following natural language instructions. We view each house environment as a local client, which shares nothing other than local updates with the cloud server and other clients, and propose a novel federated vision-and-language navigation (FedVLN) framework to protect data privacy during both training and pre-exploration. Particularly, we propose a decentralized training strategy to limit the data of each client to its local model training and a federated pre-exploration method to do partial model aggregation to improve model generalizability to unseen environments. Extensive results on R2R and RxR datasets show that under our FedVLN framework, decentralized VLN models achieve comparable results with centralized training while protecting seen environment privacy, and federated pre-exploration significantly outperforms centralized pre-exploration while preserving unseen environment privacy. Code is available at https://github.com/eric-ai-lab/FedVLN."
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval
Authors: Haoran Wang; Dongliang He; Wenhao Wu; Boyang Xia; Min Yang; Fu Li; Yunlong Yu; Zhong Ji; Errui Ding; Jingdong Wang
Venue: ECCV 2022 (main)

Abstract: "Image-Text Retrieval (ITR) is challenging in bridging visual and lingual modalities. Contrastive learning has been adopted by most prior arts. Except for limited amount of negative image-text pairs, the capability of constrastive learning is restricted by manually weighting negative pairs as well as unawareness of external knowledge. In this paper, we propose our novel Coupled Diversity-Sensitive Momentum Constrastive Learning (CODER) for improving cross-modal representation. Firstly, a novel diversity-sensitive contrastive learning (DCL) architecture is invented. We introduce dynamic dictionaries for both modalities to enlarge the scale of image-text pairs, and diversity-sensitiveness is achieved by adaptive negative pair weighting. Furthermore, two branches are designed in CODER. One learns instance-level embeddings from image/text, and it also generates pseudo online clustering labels for its input image/text based on their embeddings. Meanwhile, the other branch learns to query from commonsense knowledge graph to form concept-level descriptors for both modalities. Afterwards, both branches leverage DCL to align the cross-modal embedding spaces while an extra pseudo clustering label prediction loss is utilized to promote concept-level representation learning for the second branch. Extensive experiments conducted on two popular benchmarks, i.e. MSCOCO and Flicker30K, validate CODER remarkably outperforms the state-of-the-art approaches."
Language-Driven Artistic Style Transfer
Authors: Tsu-Jui Fu; Xin Eric Wang; William Yang Wang
Venue: ECCV 2022 (main)

Abstract: "Despite having promising results, style transfer, which requires preparing style images in advance, may result in lack of creativity and accessibility. Following human instruction, on the other hand, is the most natural way to perform artistic style transfer that can significantly improve controllability for visual effect applications. We introduce a new task---language-driven artistic style transfer (LDAST)---to manipulate the style of a content image, guided by a text. We propose contrastive language visual artist (CLVA) that learns to extract visual semantics from style instructions and accomplish LDAST by the patch-wise style discriminator. The discriminator considers the correlation between language and patches of style images or transferred results to jointly embed style instructions. CLVA further compares contrastive pairs of content image and style instruction to improve the mutual relativeness. The results from the same content image can preserve consistent content structures. Besides, they should present analogous style patterns from style instructions that contain similar visual semantics. The experiments show that our CLVA is effective and achieves superb transferred results on LDAST."
Single-Stream Multi-level Alignment for Vision-Language Pretraining
Authors: Zaid Khan; Vijay Kumar B G; Xiang Yu; Samuel Schulter; Manmohan Chandraker; Yun Fu
Venue: ECCV 2022 (main)

Abstract: "Self-supervised vision-language pretraining from pure images and text with a contrastive loss is effective, but ignores fine-grained alignment due to a dual-stream architecture that aligns image and text representations only on a global level. Earlier, supervised, non-contrastive methods were capable of finer-grained alignment, but required dense annotations that were not scalable. We propose a single stream architecture that aligns images and language at multiple levels: global, fine-grained patch-token, and conceptual/semantic, using two novel tasks: symmetric cross-modality reconstruction (XMM) and a pseudo-labeled key word prediction (PSL). In XMM, we mask input tokens from one modality and use cross-modal information to reconstruct the masked token, thus improving fine-grained alignment between the two modalities. In PSL, we use attention to select keywords in a caption, use a momentum encoder to recommend other important keywords that are missing from the caption but represented in the image, and then train the visual encoder to predict the presence of those keywords, helping it learn semantic concepts that are essential for grounding a textual token to an image region. We demonstrate competitive performance and improved data efficiency on image-text retrieval, grounding, visual question answering/reasoning against larger models and models trained on more data. Code and models available at zaidkhan.me/SIMLA."
Most and Least Retrievable Images in Visual-Language Query Systems
Authors: Liuwan Zhu; Rui Ning; Jiang Li; Chunsheng Xin; Hongyi Wu
Venue: ECCV 2022 (main)

Abstract: "This is the first work to introduce the Most Retrievable Image(MRI) and Least Retrievable Image(LRI) concepts in modern text-to-image retrieval systems. An MRI is associated with and thus can be retrieved by many unrelated texts, while an LRI is disassociated from and thus not retrievable by related texts. Both of them have important practical applications and implications. Due to their one-to-many nature, it is fundamentally challenging to construct MRI and LRI. This research addresses this nontrivial problem by developing novel and effective loss functions to craft perturbations that essentially corrupt feature correlation between visual and language spaces, thus enabling MRI and LRI. The proposed schemes are implemented based on CLIP, a state-of-the-art image and text representation model, to demonstrate MRI and LRI and their application in privacy-preserved image sharing and malicious advertisement. They are evaluated by extensive experiments based on the modern visual-language models on multiple benchmarks, including Paris, ImageNet, Flickr30k, and MSCOCO. The experimental results show the effectiveness and robustness of the proposed schemes for constructing MRI and LRI."
Sports Video Analysis on Large-Scale Data
Authors: Dekun Wu; He Zhao; Xingce Bao; Richard P. Wildes
Venue: ECCV 2022 (main)

Abstract: "This paper investigates the modeling of automated machine description on sports video, which has seen much progress recently. Nevertheless, state-of-the-art approaches fall quite short of capturing how human experts analyze sports scenes. There are several major reasons: (1) The used dataset is collected from non-official providers, which naturally creates a gap between models trained on those datasets and real-world applications; (2) previously proposed methods require extensive annotation efforts (i.e., player and ball segmentation at pixel level) on localizing useful visual features to yield acceptable results; (3) very few public datasets are available. In this paper, we propose a novel large-scale NBA dataset for Sports Video Analysis (NSVA) with a focus on captioning, to address the above challenges. We also design a unified approach to process raw videos into a stack of meaningful features with minimum labelling efforts, showing that cross modeling on such features using a transformer architecture leads to strong performance. In addition, we demonstrate the broad application of NSVA by addressing two additional tasks, namely fine-grained sports action recognition and salient player identification. Code and dataset are available at https://github.com/jackwu502/NSVA."
Grounding Visual Representations with Texts for Domain Generalization
Authors: Seonwoo Min; Nokyung Park; Siwon Kim; Seunghyun Park; Jinkyu Kim
Venue: ECCV 2022 (main)

Abstract: "Reducing the representational discrepancy between source and target domains is a key component to maximize the model generalization. In this work, we advocate for leveraging natural language supervision for the domain generalization task. We introduce two modules to ground visual representations with texts containing typical reasoning of humans: (1) Visual and Textual Joint Embedder and (2) Textual Explanation Generator. The former learns the image-text joint embedding space where we can ground high-level class-discriminative information into the model. The latter leverages an explainable model and generates explanations justifying the rationale behind its decision. To the best of our knowledge, this is the first work to leverage the vision-and-language cross-modality approach for the domain generalization task. Our experiments with a newly created CUB-DG benchmark dataset demonstrate that cross-modality supervision can be successfully used to ground domain-invariant visual representations and improve the model generalization. Furthermore, in the large-scale DomainBed benchmark, our proposed method achieves state-of-the-art results and ranks 1st in average performance for five multi-domain datasets. The dataset and codes are available at https://github.com/mswzeus/GVRT."
Bridging the Visual Semantic Gap in VLN via Semantically Richer Instructions
Authors: Joaquín Ossandón; Benjamín Earle; Alvaro Soto
Venue: ECCV 2022 (main)

Abstract: "The Visual-and-Language Navigation (VLN) task requires understanding a textual instruction to navigate a natural indoor environment using only visual information. While this is a trivial task for most humans, it is still an open problem for AI models. In this work, we hypothesize that poor use of the visual information available is at the core of the low performance of current models. To support this hypothesis, we provide experimental evidence showing that state-of-the-art models are not severely affected when they receive just limited or even no visual data, indicating a strong overfitting to the textual instructions. To encourage a more suitable use of the visual information, we propose a new data augmentation method that fosters the inclusion of more explicit visual information in the generation of textual navigational instructions. Our main intuition is that current VLN datasets include textual instructions that are intended to inform an expert navigator, such as a human, but not a beginner visual navigational agent, such as a randomly initialized DL model. Specifically, to bridge the visual semantic gap of current VLN datasets, we take advantage of metadata available for the Matterport3D dataset that, among others, includes information about object labels that are present in the scenes. Training a state-of-the-art model with the new set of instructions increase its performance by 8% in terms of success rate on unseen environments, demonstrating the advantages of the proposed data augmentation method."
StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation
Authors: Adyasha Maharana; Darryl Hannan; Mohit Bansal
Venue: ECCV 2022 (main)

Abstract: "Recent advances in text-to-image synthesis have led to large pretrained transformers with excellent capabilities to generate visualizations from a given text. However, these models are ill-suited for specialized tasks like story visualization, which requires an agent to produce a sequence of images given a corresponding sequence of captions, forming a narrative. Moreover, we find that the story visualization task fails to accommodate generalization to unseen plots and characters in new narratives. Hence, we first propose the task of story continuation, where the generated visual story is conditioned on a source image, allowing for better generalization to narratives with new characters. Then, we enhance or ‘retro-fit’ the pretrained text-to-image synthesis models with task-specific modules for (a) sequential image generation and (b) copying relevant elements from an initial frame. We explore full-model finetuning, as well as prompt-based tuning for parameter-efficient adaptation, of the pretrained model. We evaluate our approach StoryDALL-E on two existing datasets, PororoSV and FlintstonesSV, and introduce a new dataset DiDeMoSV collected from a video-captioning dataset. We also develop a model StoryGANc based on Generative Adversarial Networks (GAN) for story continuation, and compare with the StoryDALL-E model to demonstrate the advantages of our approach. We show that our retro-fitting approach outperforms GAN-based models for story continuation. We also demonstrate that the ‘retro-fitting’ approach facilitates copying of visual elements from the source image and improved continuity in visual frames. Finally, our analysis suggests that pretrained transformers struggle with comprehending narratives containing multiple characters, and translating them into appropriate imagery. Our work encourages future research into story continuation and large-scale models for the task."
VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance
Authors: Katherine Crowson; Stella Biderman; Daniel Kornis; Dashiell Stander; Eric Hallahan; Louis Castricato; Edward Raff
Venue: ECCV 2022 (main)

Abstract: "Image generation and manipulation requires technical expertise to use, inhibiting adoption. Current methods rely heavily on training to a specific domain (e.g., only faces), manual work or algorithm tuning to latent vector discovery, and manual effort in mask selection to alter only a part of an image. We address all of these usability constraints while producing images of high visual and semantic quality through a unique combination of OpenAI’s CLIP (Radford et al., 2021), VQGAN (Esser et al., 2021), and a generation augmentation strategy to produce VQGAN-CLIP. This allows generation and manipulation of images using natural language text, without further training on any domain datasets. We demonstrate on a variety of tasks how VQGAN-CLIP produces higher visual quality outputs than prior, less flexible approaches like minDALL-E (Kakaobrain, 2021) and Open-Edit (Liu, 2020), despite not being trained for the tasks presented."
Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generation
Authors: Xian Liu; Yinghao Xu; Qianyi Wu; Hang Zhou; Wayne Wu; Bolei Zhou
Venue: ECCV 2022 (main)

Abstract: "Animating high-fidelity video portrait with speech audio is crucial for virtual reality and digital entertainment. While most previous studies rely on accurate explicit structural information, recent works explore the implicit scene representation of Neural Radiance Fields (NeRF) for realistic generation. In order to capture the inconsistent motions as well as the semantic difference between human head and torso, some work models them via two individual sets of NeRF, leading to unnatural results. In this work, we propose Semantic-aware Speaking Portrait NeRF (SSP-NeRF), which creates delicate audio-driven portraits using one unified set of NeRF. The proposed model can handle the detailed local facial semantics and the global head-torso relationship through two semantic-aware modules. Specifically, we first propose a Semantic-Aware Dynamic Ray Sampling module with an additional parsing branch that facilitates audio-driven volume rendering. Moreover, to enable portrait rendering in one unified neural radiance field, a Torso Deformation module is designed to stabilize the large-scale non-rigid torso motions. Extensive evaluations demonstrate that our proposed approach renders realistic video portraits. Demo video and more resources can be found in https://alvinliu0.github.io/projects/SSP-NeRF"
End-to-End Active Speaker Detection
Authors: Juan León Alcázar; Moritz Cordes; Chen Zhao; Bernard Ghanem
Venue: ECCV 2022 (main)

Abstract: "Abstract. Recent advances in the Active Speaker Detection (ASD) problem build upon a two stage process: feature extraction and spatio-temporal context aggregation. In this paper, we propose an end-to-end ASD workflow where feature learning and contextual predictions are jointly learned. Our end-to-end trainable network simultaneously learns multi-modal embeddings and aggregates spatio-temporal context. This results in more suitable feature representations and improved performance in the ASD task. We also introduce interleaved graph neural network (iGNN) blocks, which split the message passing according to the main sources of context in the ASD problem. Experiments show that the aggregated features from the iGNN blocks are more suitable for ASD, resulting in state-of-the art performance. Finally, we design a weakly-supervised strategy, which demonstrates that the ASD problem can also be approached by utilizing audiovisual data but relying exclusively on audio annotations. We achieve this by modelling the direct relationship between the audio signal and the possible sound sources (speakers), as well as introducing a contrastive loss."