Perceptual Image Compression with Cooperative Cross-Modal Side Information
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东省新型安全智能重点实验室）；Huawei Technologies Company Ltd（华为技术有限公司）；Shenzhen University（深圳大学）
Shiyu Qin, Bin Chen, Yujun Huang, Baoyi An, Tao Dai, Shu-Tao Xia
The explosion of data has resulted in more and more associated text being transmitted along with images. Inspired by from distributed source coding, many works utilize image side information to enhance image compression. However, existing methods generally do not consider using text as side information to enhance perceptual compression of images, even though the benefits of multimodal synergy have been widely demonstrated in research. This begs the following question: How can we effectively transfer text-level semantic dependencies to help image compression, which is only available to the decoder? In this work, we propose a novel deep image compression method with text-guided side information to achieve a better rate-perception-distortion tradeoff. Specifically, we employ the CLIP text encoder and an effective Semantic-Spatial Aware block to fuse the text and image features. This is done by predicting a semantic mask to guide the learned text-adaptive affine transformation at the pixel level. Furthermore, we design a text-conditional generative adversarial networks to improve the perceptual quality of reconstructed images. Extensive experiments involving four datasets and ten image quality assessment metrics demonstrate that the proposed approach achieves superior results in terms of rate-perception trade-off and semantic distortion.
University of Cambridge（剑桥大学）；Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Super-resolution plays an essential role in medical imaging because it provides an alternative way to achieve high spatial resolutions and image quality with no extra acquisition costs. In the past few decades, the rapid development of deep neural networks has promoted super-resolution performance with novel network architectures, loss functions and evaluation metrics. Specifically, vision transformers dominate a broad range of computer vision tasks, but challenges still exist when applying them to low-level medical image processing tasks. This paper proposes an efficient vision transformer wi…
Tsinghua University（清华大学）；Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Science（中国科学院深圳先进技术研究院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；University College London（伦敦大学学院）
Most state-of-the-art semantic segmentation approaches only achieve high accuracy in good conditions. In practically-common but less-discussed adverse environmental conditions, their performance can decrease enormously. Existing studies usually cast the handling of segmentation in adverse conditions as a separate post-processing step after signal restoration, making the segmentation performance largely depend on the quality of restoration. In this paper, we propose a novel deep-learning framework to tackle semantic segmentation and image restoration in adverse environmental conditions in a hol…
Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input, has recently received considerable attention with regard to its tremendous application potentials. Although deep learning-based methods have achieved promising restoration quality on real-world image super-resolution datasets, they ignore the relationship between L1- and perceptual- minimization and roughly adopt auxiliary large-scale datasets for pre-training. In this paper, we discuss the image types within a corrupted image and the property of perceptual…
Technion（以色列理工学院）；NetApp；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Machine learning advances in the last decade have relied significantly on large-scale datasets that continue to grow in size. Increasingly, those datasets also contain different data modalities. However, large multi-modal datasets are hard to annotate, and annotations may contain biases that we are often unaware of. Deep-net-based classifiers, in turn, are prone to exploit those biases and to find shortcuts. To study and quantify this concern, we introduce the perceptual score, a metric that assesses the degree to which a model relies on the different subsets of the input features, i.e., modal…
City University of Hong Kong（香港城市大学）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）；The University of Tokyo（东京大学）
Existing psychophysical studies have revealed that the cross-modal visual-tactile perception is common for humans performing daily activities. However, it is still challenging to build the algorithmic mapping from one modality space to another, namely the cross-modal visual-tactile data translation/generation, which could be potentially important for robotic operation. In this paper, we propose a deep-learning-based approach for cross-modal visual-tactile data generation by leveraging the framework of the generative adversarial networks (GANs). Our approach takes the visual image of a material…
TriPSS: A Tri-Modal Keyframe Extraction Framework Using Perceptual, Structural, and Semantic Representations
Computer and Information Science, University of Arkansas - Little Rock（计算机与信息科学，亚拉荷马州立大学）；ICSI, University of California, Berkeley（ICSI，加州大学伯克利分校）；COSMOS Research Center, University of Arkansas - Little Rock（COSMOS研究中心，亚拉荷马州立大学）
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
Cross modal face matching between the thermal and visible spectrum is a much desired capability for night-time surveillance and security applications. Due to a very large modality gap, thermal-to-visible face recognition is one of the most challenging face matching problem. In this paper, we present an approach to bridge this modality gap by a significant margin. Our approach captures the highly non-linear relationship between the two modalities by using a deep neural network. Our model attempts to learn a non-linear mapping from visible to thermal spectrum while preserving the identity inform…
CoMLP: Cooperatively-Gated MLPs for Fine-Grained Cross-Modal Information Fusion in Medical Image Segmentation
Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Zhongguancun Academy（中关村学院）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；School of Computer Science, the University of Sydney（悉尼大学计算机学院）；China United Network Communications Group Co., Ltd.（中国联合网络通信集团有限公司）
Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Department of Artificial Intelligence, Hanyang University, Seoul, South Korea（人工智能系，翰阳大学，首尔，韩国）；Department of Electronic Engineering, Hanyang University, Seoul, South Korea（电子工程系，翰阳大学，首尔，韩国）；Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, South Korea（计算机工程系，韩国外语大学， Yongin，韩国）；Division of AI Data Convergence, Hankuk University of Foreign Studies, Yongin, South Korea（AI数据融合系，韩国外语大学， Yongin，韩国）；Division of Language & AI, Hankuk University of Foreign Studies, Seoul, South Korea（语言与AI系，韩国外语大学，首尔，韩国）
University of Electronic Science and Technology of China（电子科技大学）
Spatial and temporal stream model has gained great success in video action recognition. Most existing works pay more attention to designing effective features fusion methods, which train the two-stream model in a separate way. However, it's hard to ensure discriminability and explore complementary information between different streams in existing works. In this work, we propose a novel cooperative cross-stream network that investigates the conjoint information in multiple different modalities. The jointly spatial and temporal stream networks feature extraction is accomplished by an end-to-end …
Texas State University（德克萨斯州立大学）；ReLER, University of Technology Sydney（悉尼科技大学 ReLER 实验室）；Baidu Research（百度研究院）
With the arising concerns for the AI systems provided with direct access to abundant sensitive information, researchers seek to develop more reliable AI with implicit information sources. To this end, in this paper, we introduce a new task called video description via two multi-modal cooperative dialog agents, whose ultimate goal is for one conversational agent to describe an unseen video based on the dialog and two static frames. Specifically, one of the intelligent agents - Q-BOT - is given two static frames from the beginning and the end of the video, as well as a finite number of opportuni…
Technical University of Darmstadt（达姆施塔特工业大学）；University of Cambridge（剑桥大学）
Current state-of-the-art approaches to cross-modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image. While offering unmatched retrieval performance, such models: 1) are typically pretrained from scratch and thus less scalable, 2) suffer from huge retrieval latency and inefficiency issues, which makes them impractical in realistic applications. To address these crucial gaps towards both improved and efficient cross-modal retrieval, we propose a novel fine-tuning framewor…
V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception
School of Vehicle and Mobility, Tsinghua University（车辆与移动性学院，清华大学）；Nanyang Technological University（南洋理工大学）；CUMTB（中国交通车辆技术研究所）；University of California, Los Angeles（加州大学洛杉矶分校）；Beijing Jiaotong University（北京交通大学）；ByteDance（字节跳动）
Cross-Modal RGB-D Fusion Transformer for 6D Pose Estimation of Non-Cooperative Spacecraft with Stereo-Derived Depth
School of Physics, Northeast Normal University（东北师范大学物理学院）；Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science（长春光学精密机械与物理研究所）
Washington University in St. Louis（圣路易斯华盛顿大学）；Massachusetts Institute of Technology（麻省理工学院）；Microsoft Research（微软研究院）
Inference of correspondences between images from different modalities is an extremely important perceptual ability that enables humans to understand and recognize cross-modal concepts. In this paper, we consider an instance of this problem that involves matching photographs of building interiors with their corresponding floorplan. This is a particularly challenging problem because a floorplan, as a stylized architectural drawing, is very different in appearance from a color photograph. Furthermore, individual photographs by themselves depict only a part of a floorplan (e.g., kitchen, bathroom,…
HeCoFuse: Cross-Modal Complementary V2X Cooperative Perception with Heterogeneous Sensors
College of Engineering, Center for Environmental Research and Technology, University of California at Riverside（加州大学河滨分校工程学院、环境研究与技术中心）；School of Transportation and Logistics, Southwest Jiaotong University（西南交通大学交通运输与物流学院）；Chair of Robotics, Artificial Intelligence and Real-time Systems, TUM School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算机、信息与技术学院机器人、人工智能与实时系统教授职位）
Peking University（北京大学）；Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Shopee（虾皮）；Tsinghua University（清华大学）；Peng Cheng Laboratory（鹏城实验室）
Contrastive learning-based video-language representation learning approaches, e.g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs. To clarify this coarse-grained global interaction and move a step further, we have to encounter challenging shell-breaking interactions for fine-grained cross-modal learning. In this paper, we creatively model video-text as game players with multivariate cooperative game theory to wisely handle the uncertainty during fine-grained semantic interaction with diverse granularity, flexible combination, a…
Beyond Sequential Distance: Inter-Modal Distance Invariant Position Encoding
MAIS, Institute of Automation, Chinese Academy of Sciences, China（MAIS，自动化研究所，中国科学院，中国）；University of Chinese Academy of Sciences, China（中国科学院大学，中国）；Tencent Hunyuan Team, China（腾讯文言团队，中国）
Perceptual Region-Driven Infrared-Visible Co-Fusion for Extreme Scene Enhancement
College of Aerospace Science and Engineering, National University of Defense Technology（航天科学与工程学院，国防科技大学）；Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation（湖南省图像测量与视觉导航重点实验室）；Beijing Institute of Tracking and Telecommunication Technology（北京跟踪与电信技术研究所）；National Key Laboratory of Space Integrated Information System（空间一体化信息系统国家重点实验室）
University of Freiburg（弗莱堡大学）；University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）
Many real-world video-text tasks involve different levels of granularity, such as frames and words, clip and sentences or videos and paragraphs, each with distinct semantics. In this paper, we propose a Cooperative hierarchical Transformer (COOT) to leverage this hierarchy information and model the interactions between different levels of granularity and different modalities. The method consists of three major components: an attention-aware feature aggregation layer, which leverages the local temporal context (intra-level, e.g., within a clip), a contextual transformer to learn the interaction…
University of Coimbra（科英布拉大学）；Institute of Systems and Robotics（系统与机器人研究所）
Human beings have developed fantastic abilities to integrate information from various sensory sources exploring their inherent complementarity. Perceptual capabilities are therefore heightened, enabling, for instance, the well-known "cocktail party" and McGurk effects, i.e., speech disambiguation from a panoply of sound signals. This fusion ability is also key in refining the perception of sound source location, as in distinguishing whose voice is being heard in a group conversation. Furthermore, neuroscience has successfully identified the superior colliculus region in the brain as the one re…
Cross-Modal Scene Semantic Alignment for Image Complexity Assessment
School of Computer Science Cardiff University（卡迪夫大学计算机科学学院）；School of Mathematical Sciences Beihang University（北航数学科学学院）；Department of Information Technology University of Klagenfurt（克雷格弗特大学信息技术系）；School of Biomedical Engineering Shenzhen University（深圳大学生物医学工程学院）；School of Artificial Intelligence Sun Yat-sen University（中山大学人工智能学院）
The widespread dissemination of Deepfakes demands effective approaches that can detect perceptually convincing forged images. In this paper, we aim to capture the subtle manipulation artifacts at different scales using transformer models. In particular, we introduce a Multi-modal Multi-scale TRansformer (M2TR), which operates on patches of different sizes to detect local inconsistencies in images at different spatial levels. M2TR further learns to detect forgery artifacts in the frequency domain to complement RGB information through a carefully designed cross modality fusion block. In addition…
Recent learning-based image fusion methods have marked numerous progress in pre-registered multi-modality data, but suffered serious ghosts dealing with misaligned multi-modality data, due to the spatial deformation and the difficulty narrowing cross-modality discrepancy. To overcome the obstacles, in this paper, we present a robust cross-modality generation-registration paradigm for unsupervised misaligned infrared and visible image fusion (IVIF). Specifically, we propose a Cross-modality Perceptual Style Transfer Network (CPSTN) to generate a pseudo infrared image taking a visible image as i…
Multi-modal word semantics aims to enhance embeddings with perceptual input, assuming that human meaning representation is grounded in sensory experience. Most research focuses on evaluation involving direct visual input, however, visual grounding can contribute to linguistic applications as well. Another motivation for this paper is the growing need for more interpretable models and for evaluating model efficiency regarding size and performance. This work explores the impact of visual information for semantics when the evaluation involves no direct visual input, specifically semantic similari…
We study the problem of automatically building hypernym taxonomies from textual and visual data. Previous works in taxonomy induction generally ignore the increasingly prominent visual data, which encode important perceptual semantics. Instead, we propose a probabilistic model for taxonomy induction by jointly leveraging text and images. To avoid hand-crafted feature engineering, we design end-to-end features based on distributed representations of images and words. The model is discriminatively trained given a small set of existing ontologies and is capable of building full taxonomies from sc…
University of Innsbruck（因斯布鲁克大学）；LEC GmbH（LEC有限公司）；INNIO Jenbacher GmbH & Co OG（INNIO颜巴赫两合公司）
Real-time estimation of actual object depth is an essential module for various autonomous system tasks such as 3D reconstruction, scene understanding and condition assessment. During the last decade of machine learning, extensive deployment of deep learning methods to computer vision tasks has yielded approaches that succeed in achieving realistic depth synthesis out of a simple RGB modality. Most of these models are based on paired RGB-depth data and/or the availability of video sequences and stereo images. The lack of sequences, stereo data and RGB-depth pairs makes depth estimation a fully …
Northwestern Polytechnical University（西北工业大学）；Ningbo Institute of Northwestern Polytechnical University（西北工业大学宁波研究院）；Nanchang University（南昌大学）；Shanghai Jiao Tong University（上海交通大学）
Incorporating the audio stream enables Video Saliency Prediction (VSP) to imitate the selective attention mechanism of human brain. By focusing on the benefits of joint auditory and visual information, most VSP methods are capable of exploiting semantic correlation between vision and audio modalities but ignoring the negative effects due to the temporal inconsistency of audio-visual intrinsics. Inspired by the biological inconsistency-correction within multi-sensory information, in this study, a consistency-aware audio-visual saliency prediction network (CASP-Net) is proposed, which takes a co…
Sorbonne Universités, UPMC Univ Paris 06, UMR 7606, CNRS, LIP6（索邦大学，巴黎第六大学，UMR 7606，法国国家科学研究中心，LIP6实验室）
Representing the semantics of words is a long-standing problem for the natural language processing community. Most methods compute word semantics given their textual context in large corpora. More recently, researchers attempted to integrate perceptual and visual features. Most of these works consider the visual appearance of objects to enhance word representations but they ignore the visual environment and context in which objects appear. We propose to unify text-based techniques with vision-based techniques by simultaneously leveraging textual and visual context to learn multimodal word embe…
