GenAI4ELab/papercli-papers huggingface dataset:
MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes
Authors: Yang Jiao; Shaoxiang Chen; Zequn Jie; Jingjing Chen; Lin Ma; Yu-Gang Jiang
Venue: ECCV 2022 (main)

Abstract: "3D dense captioning is a recently-proposed novel task, where point clouds contain more geometric information than the 2D counterpart. However, it is also more challenging due to the higher complexity and wider variety of inter-object relations contained in point clouds. Existing methods only treat such relations as by-products of object feature learning in graphs without specifically encoding them, which leads to sub-optimal results. In this paper, aiming at improving 3D dense captioning via capturing and utilizing the complex relations in the 3D scene, we propose MORE, a Multi-Order RElation mining model, to support generating more descriptive and comprehensive captions. Technically, our MORE encodes object relations in a progressive manner since complex relations can be deduced from a limited number of basic ones. We first devise a novel Spatial Layout Graph Convolution (SLGC), which semantically encodes several first-order relations as edges of a graph constructed over 3D object proposals. Next, from the resulting graph, we further extract multiple triplets which encapsulate basic first-order relations as the basic unit, and construct several Object-centric Triplet Attention Graphs (OTAG) to infer multi-order relations for every target object. The updated node features from OTAG are aggregated and fed into the caption decoder to provide abundant relational cues, so that captions including diverse relations with context objects can be generated. Extensive experiments on the Scan2Cap dataset prove the effectiveness of our proposed MORE and its components, and we also outperform the current state-of-the-art method. Our code is available at https://github.com/SxJyJay/MORE."
SiRi: A Simple Selective Retraining Mechanism for Transformer-Based Visual Grounding
Authors: Mengxue Qu; Yu Wu; Wu Liu; Qiqi Gong; Xiaodan Liang; Olga Russakovsky; Yao Zhao; Yunchao Wei
Venue: ECCV 2022 (main)

Abstract: "In this paper, we investigate how to achieve better referring visual grounding with modern vision-language transformers, and propose a simple yet powerful Selective Retraining (SiRi) mechanism. Particularly, SiRi conveys a significant principle to the research of visual grounding, i.e, a better initialized vision-language encoder would help the model converge to a better local minimum, advancing the performance accordingly. With such a principle, we continually update the parameters of the encoder as the training goes on, while periodically re-initialize the rest parameters to compel the model to be better optimized based on an enhanced encoder. With such a simple training mechanism, our SiRi can significantly outperform previous approaches on three popular benchmarks. Additionally, we reveal that SiRi performs surprisingly superior even with limited training data. More importantly, the effectiveness of SiRi, are further verified by other model and other V-L tasks. Code is available in the supplementary materials."
Cross-Modal Prototype Driven Network for Radiology Report Generation
Authors: Jun Wang; Abhir Bhalerao; Yulan He
Venue: ECCV 2022 (main)

Abstract: "Radiology report generation (RRG) aims to describe automatically a radiology image with human-like language and could potentially support the work of radiologists, reducing the burden of manual reporting. Previous approaches often adopt an encoder-decoder architecture and focus on single-modal feature learning, while few studies explore cross-modal feature interaction. Here we propose a Cross-modal PROtotype driven NETwork (XPRONET) to promote cross-modal pattern learning and exploit it to improve the task of radiology report generation. This is achieved by three well-designed, fully differentiable and complementary modules: a shared cross-modal prototype matrix to record the cross-modal prototypes; a cross-modal prototype network to learn the cross-modal prototypes and embed the cross-modal information into the visual and textual features; and an improved multi-label contrastive loss to enable and enhance multi-label prototype learning. XPRONET obtains substantial improvements on the IU-Xray and MIMIC-CXR benchmarks, where its performance exceeds recent state-of-the-art approaches by a large margin on IU-Xray and comparable performance on MIMIC-CXR."
TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts
Authors: Chuan Guo; Xinxin Zuo; Sen Wang; Li Cheng
Venue: ECCV 2022 (main)

Abstract: "Inspired by the strong ties between vision and language, the two intimate human sensing and communication modalities, our paper aims to explore the generation of 3D human full-body motions from texts, as well as its reciprocal task, shorthanded for text2motion and motion2text, respectively. To tackle the existing challenges, especially to enable the generation of multiple distinct motions from the same text, and to avoid the undesirable production of trivial motionless pose sequences, we propose the use of motion token, a discrete and compact motion representation, where motions and texts could then be considered on one level playing ground, as the motion and text tokens. Moreover, our motion2text module is integrated into the inverse alignment process of our text2motion training pipeline, where a significant deviation of synthesized text (text2motion-2text) from the input text would be penalized by a large training loss; empirically this is shown to achieve improved performance. Finally, the mappings in-between the two modalities of motions and texts are facilitated by adapting the neural model for machine translation (NMT) to our context. Autoregressive modeling on the underlying distribution of discrete motion tokens further enables the production of non-deterministic motions from texts. Overall our approach is flexible, and could be used for both text2motion and motion2text tasks. Empirical evaluations on two benchmark datasets demonstrate the superior performance of our approach over a variety of state-of-the-art methods on both tasks."
SeqTR: A Simple Yet Universal Network for Visual Grounding
Authors: Chaoyang Zhu; Yiyi Zhou; Yunhang Shen; Gen Luo; Xingjia Pan; Mingbao Lin; Chao Chen; Liujuan Cao; Xiaoshuai Sun; Rongrong Ji
Venue: ECCV 2022 (main)

Abstract: "In this paper, we propose a simple yet universal network termed SeqTR for visual grounding tasks, e.g., phrase localization, referring expression comprehension (REC) and segmentation (RES). The canonical paradigms for visual grounding often require substantial expertise in designing network architectures and loss functions, making them hard to generalize across tasks. To simplify and unify the modeling, we cast visual grounding as a point prediction problem conditioned on image and text inputs, where either the bounding box or binary mask is represented as a sequence of discrete coordinate tokens. Under this paradigm, visual grounding tasks are unified in our SeqTR network without task-specific branches or heads, e.g., the convolutional mask decoder for RES, which greatly reduces the complexity of multi-task modeling. In addition, SeqTR also shares the same optimization objective for all tasks with a simple cross-entropy loss, further reducing the complexity of deploying hand-crafted loss functions. Experiments on five benchmark datasets demonstrate that the proposed SeqTR outperforms (or is on par with) the existing state-of-the-arts, proving that a simple yet universal approach for visual grounding is indeed feasible. Source code is available at https://github.com/sean-zhuh/SeqTR."
VTC: Improving Video-Text Retrieval with User Comments
Authors: Laura Hanu; James Thewlis; Yuki M. Asano; Christian Rupprecht
Venue: ECCV 2022 (main)

Abstract: "Multi-modal retrieval is an important problem for many applications, such as recommendation and search. Current benchmarks and even datasets are often manually constructed and consist of mostly clean samples where all modalities are well-correlated with the content. Thus, current video-text retrieval literature largely focuses on video titles or audio transcripts, while ignoring user comments, since users often tend to discuss topics only vaguely related to the video. Despite the ubiquity of user comments online, there is currently no multi-modal representation learning datasets that includes comments. In this paper, we a) introduce a new dataset of videos, titles and comments; b) present an attention-based mechanism that allows the model to learn from sometimes irrelevant data such as comments; c) show that by using comments, our method is able to learn better, more contextualised, representations for image, video and audio representations."
FashionViL: Fashion-Focused Vision-and-Language Representation Learning
Authors: Xiao Han; Licheng Yu; Xiatian Zhu; Li Zhang; Yi-Zhe Song; Tao Xiang
Venue: ECCV 2022 (main)

Abstract: "Large-scale Vision-and-Language (V+L) pre-training for representation learning has proven to be effective in boosting various downstream V+L tasks. However, when it comes to the fashion domain, existing V+L methods are inadequate as they overlook the unique characteristics of both fashion V+L data and downstream tasks. In this work, we propose a novel fashion-focused V+L representation learning framework, dubbed as FashionViL. It contains two novel fashion-specific pre-training tasks designed particularly to exploit two intrinsic attributes with fashion V+L data. First, in contrast to other domains where a V+L datum contains only a single image-text pair, there could be multiple images in the fashion domain. We thus propose a Multi-View Contrastive Learning task for pulling closer the visual representation of one image to the compositional multimodal representation of another image+text. Second, fashion text (e.g., product description) often contains rich fine-grained concepts (attributes/noun phrases). To capitalize this, a Pseudo-Attributes Classification task is introduced to encourage the learned unimodal (visual/textual) representations of the same concept to be adjacent. Further, fashion V+L tasks uniquely include ones that do not conform to the common one-stream or two-stream architectures (e.g., text-guided image retrieval). We thus propose a flexible, versatile V+L model architecture consisting of a modality-agnostic Transformer so that it can be flexibly adapted to any downstream tasks. Extensive experiments show that our FashionViL achieves new state of the art across five downstream tasks. Code is available at https://github.com/BrandonHanx/mmf."
Weakly Supervised Grounding for VQA in Vision-Language Transformers
Authors: Aisha Urooj; Hilde Kuehne; Chuang Gan; Niels Da Vitoria Lobo; Mubarak Shah
Venue: ECCV 2022 (main)

Abstract: "Transformers for visual-language representation learning have been getting a lot of interest and shown tremendous performance on visual question answering (VQA) and grounding. However, most systems that show good performance of those tasks still rely on pre-trained object detectors during training, which limits their applicability to the object classes available for those detectors. To mitigate this limitation, this paper focuses on the problem of weakly supervised grounding in the context of visual question answering in transformers. Our approach leverages capsules by transforming each visual token into a capsule representation in the visual encoder; it then uses activations from language self-attention layers as a text-guided selection module to mask those capsules before they are forwarded to the next layer. We evaluate our approach on the challenging GQA as well as VQA-HAT dataset for VQA grounding. Our experiments show that: while removing the information of masked objects from standard transformer architectures leads to a significant drop in performance, the integration of capsules significantly improves the grounding ability of such systems and provides new state-of-the-art results compared to other approaches in the field."
Automatic Dense Annotation of Large-Vocabulary Sign Language Videos
Authors: Liliane Momeni; Hannah Bull; K R Prajwal; Samuel Albanie; Gül Varol; Andrew Zisserman
Venue: ECCV 2022 (main)

Abstract: "Recently, sign language researchers have turned to sign language interpreted TV broadcasts, comprising (i) a video of continuous signing and (ii) subtitles corresponding to the audio content, as a readily available and large-scale source of training data. One key challenge in the usability of such data is the lack of sign annotations. Previous work exploiting such weakly-aligned data only found sparse correspondences between keywords in the subtitle and individual signs. In this work, we propose a simple, scalable framework to vastly increase the density of automatic annotations. Our contributions are the following: (1) we significantly improve previous annotation methods by making use of synonyms and subtitle-signing alignment; (2) we show the value of pseudo-labelling from a sign recognition model as a way of sign spotting; (3) we propose a novel approach for increasing our annotations of known and unknown classes based on in domain exemplars; (4) on the BOBSL BSL sign language corpus, we increase the number of confident automatic annotations from 670K to 5M. We make these annotations publicly available to support the sign language research community."
MILES: Visual BERT Pre-training with Injected Language Semantics for Video-Text Retrieval
Authors: Yuying Ge; Yixiao Ge; Xihui Liu; Jinpeng Wang; Jianping Wu; Ying Shan; Xiaohu Qie; Ping Luo
Venue: ECCV 2022 (main)

Abstract: Dominant pre-training work for video-text retrieval mainly adopt the "dual-encoder" architectures to enable efficient retrieval, where two separate encoders are used to contrast global video and text representations, but ignore detailed local semantics. The recent success of image BERT pre-training with masked visual modeling that promotes the learning of local visual context, motivates a possible solution to address the above limitation. In this work, we for the first time investigate masked visual modeling in video-text pre-training with the "dual-encoder" architecture. We perform Masked visual modeling with Injected LanguagE Semantics (MILES) by employing an extra snapshot video encoder as an evolving "tokenizer" to produce reconstruction targets for masked video patch prediction. Given the corrupted video, the video encoder is trained to recover text-aligned features of the masked patches via reasoning with the visible regions along the spatial and temporal dimensions, which enhances the discriminativeness of local visual features and the fine-grained cross-modality alignment. Our method outperforms state-of-the-art methods for text-to-video retrieval on four datasets with both zero-shot and fine-tune evaluation protocols. Our approach also surpasses the baseline models significantly on zero-shot action recognition, which can be cast as video-to-text retrieval.
"GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval"
Authors: Yuxuan Wang; Difei Gao; Licheng Yu; Weixian Lei; Matt Feiszli; Mike Zheng Shou
Venue: ECCV 2022 (main)

Abstract: "Cognitive science has shown that humans perceive videos in terms of events separated by the state changes of dominant subjects. State changes trigger new events and are one of the most useful among the large amount of redundant information perceived. However, previous research focuses on the overall understanding of segments without evaluating the fine-grained status changes inside. In this paper, we introduce a new dataset called Kinetic-GEB+. The dataset consists of over 170k boundaries associated with captions describing status changes in the generic events in 12K videos. Upon this new dataset, we propose three tasks supporting the development of a more fine-grained, robust, and human-like understanding of videos through status changes. We evaluate many representative baselines in our dataset, where we also design a new TPD (Temporal-based Pairwise Difference) Modeling method for visual difference and achieve significant performance improvements. Besides, the results show there are still formidable challenges for current methods in the utilization of different granularities, representation of visual difference, and the accurate localization of status changes. Further analysis shows that our dataset can drive developing more powerful methods to understand status changes and thus improve video level comprehension. The dataset is available at https://github.com/Yuxuan-W/GEB-Plus"
A Simple and Robust Correlation Filtering Method for Text-Based Person Search
Authors: Wei Suo; Mengyang Sun; Kai Niu; Yiqi Gao; Peng Wang; Yanning Zhang; Qi Wu
Venue: ECCV 2022 (main)

Abstract: "Text-based person search aims to associate pedestrian images with natural language descriptions. In this task, extracting differentiated representations and aligning them among identities and descriptions is an essential yet challenging problem. Most of the previous methods depend on additional language parsers or vision techniques to select the relevant regions or words from noise inputs. But there exists heavy computation cost and inevitable error accumulation. Meanwhile, simply using horizontal segmentation images to obtain local-level features would harm the reliability of models as well. In this paper, we present a novel end-to-end Simple and Robust Correlation Filtering (SRCF) method which can effectively extract key clues and adaptively align the discriminative features. Different from previous works, our framework focuses on computing the similarity between templates and inputs. In particular, we design two different types of filtering modules (i.e., denoising filters and dictionary filters) to extract crucial features and establish multi-modal mappings. Extensive experiments have shown that our method improves the robustness of the model and achieves better performance on the two text-based person search datasets. Source code is available at https://github.com/Suo-Wei/SRCF."
Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing
Authors: Benedikt Boecking; Naoto Usuyama; Shruthi Bannur; Daniel C. Castro; Anton Schwaighofer; Stephanie Hyland; Maria Wetscherek; Tristan Naumann; Aditya Nori; Javier Alvarez-Valle; Hoifung Poon; Ozan Oktay
Venue: ECCV 2022 (main)

Abstract: "Multi-modal data abounds in biomedicine, such as radiology images and reports. Interpreting this data at scale is essential for improving clinical care and accelerating clinical research. Biomedical text with its complex semantics poses additional challenges in vision-language modelling compared to the general domain, and previous work has used insufficiently adapted models that lack domain-specific language understanding. In this paper, we show that principled textual semantic modelling can substantially improve contrastive learning in self-supervised vision-language processing. We release a language model that achieves state-of-the-art results in radiology natural-language inference through its improved vocabulary and novel language pretraining objective leveraging semantics and discourse characteristics in radiology reports. Further, we propose a self-supervised joint vision-language approach with a focus on better text modelling. It establishes new state of the art results on a wide range of publicly available benchmarks, in part by leveraging our new domain-specific language model. We release a new dataset with locally-aligned phrase grounding annotations by radiologists to facilitate the study of complex semantic modelling in biomedical vision-language processing. A broad evaluation, including on this new dataset, shows that our contrastive learning approach, aided by textual-semantic modelling, outperforms prior methods in segmentation tasks, despite only using a global-alignment objective."
Generative Negative Text Replay for Continual Vision-Language Pretraining
Authors: Shipeng Yan; Lanqing Hong; Hang Xu; Jianhua Han; Tinne Tuytelaars; Zhenguo Li; Xuming He
Venue: ECCV 2022 (main)

Abstract: "Vision-language pre-training (VLP) has attracted increasing attention recently. With a large amount of image-text pairs, VLP models trained with contrastive loss have achieved impressive performance in various tasks, especially the zero-shot generalization on downstream datasets. In practical applications, however, massive data are usually collected in a streaming fashion, requiring VLP models to continuously integrate novel knowledge from incoming data. In this work, we focus on learning a VLP model with sequential data chunks of image-text pairs. To tackle the catastrophic forgetting issue in this multi-modal continual learning setting, we first introduce pseudo text replay that generates hard negative texts conditioned on the training images in memory, which not only preserves learned knowledge but also improves the diversity of negative samples in the contrastive loss. Moreover, we propose multi-modal knowledge distillation between images and texts to align the instance-wise prediction between models. We incrementally pre-train our model on the both instance and class incremental splits of Conceptual Caption dataset, and evaluate the model on zero-shot image classification and image-text retrieval tasks. Our method consistently outperforms the existing baselines with a large margin, which demonstrates its superiority. Notably, we realize an average performance boost of $4.60\%$ on image-classification downstream datasets for class incremental split."
Video Graph Transformer for Video Question Answering
Authors: Junbin Xiao; Pan Zhou; Tat-Seng Chua; Shuicheng Yan
Venue: ECCV 2022 (main)

Abstract: "This paper proposes a Video Graph Transformer (VGT) model for Video Quetion Answering (VideoQA). VGT’s uniqueness are two-fold: 1) it designs a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations, and dynamics for complex spatio-temporal reasoning; and 2) it exploits disentangled video and text Transformers for relevance comparison between the video and text to perform QA, instead of entangled cross-modal Transformer for answer classification. Vision-text communication is done by additional cross-modal interaction modules. With more reasonable video encoding and QA solution, we show that VGT can achieve much better performances on VideoQA tasks that challenge dynamic relation reasoning than prior arts in the pretraining-free scenario. Its performances even surpass those models that are pretrained with millions of external data. We further show that VGT can also benefit a lot from self-supervised cross-modal pretraining, yet with orders of magnitude smaller data. These results clearly demonstrate the effectiveness and superiority of VGT, and reveal its potential for more data-efficient pretraining. With comprehensive analyses and some heuristic observations, we hope that VGT can promote VQA research beyond coarse recognition/description towards fine-grained relation reasoning in realistic videos."