bluuebunny/crossref_metadata_2025 huggingface dataset:
AGS: Affordable and Generalizable Substitute Training for Transferable Adversarial Attack
Authors: Wang Ruikui , Guo Yuanfang , Wang Yunhong
Published: March 2024

In practical black-box attack scenarios, most of the existing transfer-based attacks employ pretrained models (e.g. ResNet50) as the substitute models. Unfortunately, these substitute models are not always appropriate for transfer-based attacks. Firstly, these models are usually trained on a largescale annotated dataset, which is extremely expensive and time-consuming to construct. Secondly, the primary goal of these models is to perform a specific task, such as image classification, which is not developed for adversarial attacks. To tackle the above issues, i.e., high cost and over-fitting on taskspecific models, we propose an Affordable and Generalizable Substitute (AGS) training framework tailored for transferbased adversarial attack. Specifically, we train the substitute model from scratch by our proposed adversary-centric constrastive learning. This proposed learning mechanism introduces another sample with slight adversarial perturbations as an additional positive view of the input image, and then encourages the adversarial view and two benign views to interact comprehensively with each other. To further boost the generalizability of the substitute model, we propose adversarial invariant learning to maintain the representations of the adversarial example invariants under augmentations with various strengths. Our AGS model can be trained solely with unlabeled and out-of domain data and avoid overfitting to any task-specific models, because of its inherently self-supervised nature. Extensive experiments demonstrate that our AGS achieves comparable or superior performance compared to substitute models pretrained on the complete ImageNet training set, when executing attacks across a diverse range of target models, including ViTs, robustly trained models, object detection and segmentation models. Our source codes are available at https://github.com/lwmming/AGS.
Suppressing Uncertainty in Gaze Estimation
Authors: Wang Shijing , Huang Yaping
Published: March 2024

Uncertainty in gaze estimation manifests in two aspects: 1) low-quality images caused by occlusion, blurriness, inconsistent eye movements, or even non-face images; 2) uncorrected labels resulting from the misalignment between the labeled and actual gaze points during the annotation process. Allowing these uncertainties to participate in training hinders the improvement of gaze estimation. To tackle these challenges, in this paper, we propose an effective solution, named Suppressing Uncertainty in Gaze Estimation (SUGE), which introduces a novel triplet-label consistency measurement to estimate and reduce the uncertainties. Specifically, for each training sample, we propose to estimate a novel ``neighboring label'' calculated by a linearly weighted projection from the neighbors to capture the similarity relationship between image features and their corresponding labels, which can be incorporated with the predicted pseudo label and ground-truth label for uncertainty estimation. By modeling such triplet-label consistency, we can largely reduce the negative effects of unqualified images and wrong labels through our designed sample weighting and label correction strategies. Experimental results on the gaze estimation benchmarks indicate that our proposed SUGE achieves state-of-the-art performance.
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
Authors: Wang Yubin , Jiang Xinyang , Cheng De , Li Dongsheng , Zhao Cairong
Published: March 2024

Prompt learning has become a prevalent strategy for adapting vision-language foundation models to downstream tasks. As large language models (LLMs) have emerged, recent studies have explored the use of category-related descriptions as input to enhance prompt effectiveness. Nevertheless, conventional descriptions fall short of structured information that effectively represents the interconnections among entities or attributes linked to a particular category. To address this limitation and prioritize harnessing structured knowledge, this paper advocates for leveraging LLMs to build a graph for each description to model the entities and attributes describing the category, as well as their correlations. Preexisting prompt tuning methods exhibit inadequacies in managing this structured knowledge. Consequently, we propose a novel approach called Hierarchical Prompt Tuning (HPT), which enables simultaneous modeling of both structured and conventional linguistic knowledge. Specifically, we introduce a relationship-guided attention module to capture pair-wise associations among entities and attributes for low-level prompt learning. In addition, by incorporating high-level and global-level prompts modeling overall semantics, the proposed hierarchical structure forges cross-level interlinks and empowers the model to handle more complex and long-term relationships. Extensive experiments demonstrate that our HPT shows strong effectiveness and generalizes much better than existing SOTA methods. Our code is available at https://github.com/Vill-Lab/2024-AAAI-HPT.
GMMFormer: Gaussian-Mixture-Model Based Transformer for Efficient Partially Relevant Video Retrieval
Authors: Wang Yuting , Wang Jinpeng , Chen Bin , Zeng Ziyun , Xia Shu-Tao
Published: March 2024

Given a text query, partially relevant video retrieval (PRVR) seeks to find untrimmed videos containing pertinent moments in a database. For PRVR, clip modeling is essential to capture the partial relationship between texts and videos. Current PRVR methods adopt scanning-based clip construction to achieve explicit clip modeling, which is information-redundant and requires a large storage overhead. To solve the efficiency problem of PRVR methods, this paper proposes GMMFormer, a Gaussian-Mixture-Model based Transformer which models clip representations implicitly. During frame interactions, we incorporate Gaussian-Mixture-Model constraints to focus each frame on its adjacent frames instead of the whole video. Then generated representations will contain multi-scale clip information, achieving implicit clip modeling. In addition, PRVR methods ignore semantic differences between text queries relevant to the same video, leading to a sparse embedding space. We propose a query diverse loss to distinguish these text queries, making the embedding space more intensive and contain more semantic information. Extensive experiments on three large-scale video datasets (i.e., TVR, ActivityNet Captions, and Charades-STA) demonstrate the superiority and efficiency of GMMFormer.
SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing
Authors: Wang Zhecheng , Prabha Rajanie , Huang Tianyuan , Wu Jiajun , Rajagopal Ram
Published: March 2024

Remote sensing imagery, despite its broad applications in helping achieve Sustainable Development Goals and tackle climate change, has not yet benefited from the recent advancements of versatile, task-agnostic vision language models (VLMs). A key reason is that the large-scale, semantically diverse image-text dataset required for developing VLMs is still absent for remote sensing images. Unlike natural images, remote sensing images and their associated text descriptions cannot be efficiently collected from the public Internet at scale. In this work, we bridge this gap by using geo-coordinates to automatically connect open, unlabeled remote sensing images with rich semantics covered in OpenStreetMap, and thus construct SkyScript, a comprehensive vision-language dataset for remote sensing images, comprising 2.6 million image-text pairs covering 29K distinct semantic tags.
With continual pre-training on this dataset, we obtain a VLM that surpasses baseline models with a 6.2% average accuracy gain in zero-shot scene classification across seven benchmark datasets. It also demonstrates the ability of zero-shot transfer for fine-grained object attribute classification and cross-modal retrieval. We hope this dataset can support the advancement of VLMs for various multi-modal tasks in remote sensing, such as open-vocabulary classification, retrieval, captioning, and text-to-image synthesis.
Heterogeneous Test-Time Training for Multi-Modal Person Re-identification
Authors: Wang Zi , Huang Huaibo , Zheng Aihua , He Ran
Published: March 2024

Multi-modal person re-identification (ReID) seeks to mitigate challenging lighting conditions by incorporating diverse modalities. Most existing multi-modal ReID methods concentrate on leveraging complementary multi-modal information via fusion or interaction. However, the relationships among heterogeneous modalities and the domain traits of unlabeled test data are rarely explored. In this paper, we propose a Heterogeneous Test-time Training (HTT) framework for multi-modal person ReID. We first propose a Cross-identity Inter-modal Margin (CIM) loss to amplify the differentiation among distinct identity samples. Moreover, we design a Multi-modal Test-time Training (MTT) strategy to enhance the generalization of the model by leveraging the relationships in the heterogeneous modalities and the information existing in the test data. Specifically, in the training stage, we utilize the CIM loss to further enlarge the distance between anchor and negative by forcing the inter-modal distance to maintain the margin, resulting in an enhancement of the discriminative capacity of the ultimate descriptor. Subsequently, since the test data contains characteristics of the target domain, we adapt the MTT strategy to optimize the network before the inference by using self-supervised tasks designed based on relationships among modalities. Experimental results on benchmark multi-modal ReID datasets RGBNT201, Market1501-MM, RGBN300, and RGBNT100 validate the effectiveness of the proposed method. The codes can be found at https://github.com/ziwang1121/HTT.
Factorized Diffusion Autoencoder for Unsupervised Disentangled Representation Learning
Authors: Wu Ancong , Zheng Wei-Shi
Published: March 2024

Unsupervised disentangled representation learning aims to recover semantically meaningful factors from real-world data without supervision, which is significant for model generalization and interpretability. Current methods mainly rely on assumptions of independence or informativeness of factors, regardless of interpretability. Intuitively, visually interpretable concepts better align with human-defined factors. However, exploiting visual interpretability as inductive bias is still under-explored. Inspired by the observation that most explanatory image factors can be represented by ``content + mask'', we propose a content-mask factorization network (CMFNet) to decompose an image into different groups of content codes and masks, which are further combined as content masks to represent different visual concepts. To ensure informativeness of the representations, the CMFNet is jointly learned with a generator conditioned on the content masks for reconstructing the input image. The conditional generator employs a diffusion model to leverage its robust distribution modeling capability. Our model is called the Factorized Diffusion Autoencoder (FDAE). To enhance disentanglement of visual concepts, we propose a content decorrelation loss and a mask entropy loss to decorrelate content masks in latent space and spatial space, respectively. Experiments on Shapes3d, MPI3D and Cars3d show that our method achieves advanced performance and can generate visually interpretable concept-specific masks. Source code and supplementary materials are available at https://github.com/wuancong/FDAE.
Multiscale Low-Frequency Memory Network for Improved Feature Extraction in Convolutional Neural Networks
Authors: Wu Fuzhi , Wu Jiasong , Kong Youyong , Yang Chunfeng , Yang Guanyu , Shu Huazhong , Carrault Guy , Senhadji Lotfi
Published: March 2024

Deep learning and Convolutional Neural Networks (CNNs) have driven major transformations in diverse research areas. However, their limitations in handling low-frequency in-formation present obstacles in certain tasks like interpreting global structures or managing smooth transition images. Despite the promising performance of transformer struc-tures in numerous tasks, their intricate optimization com-plexities highlight the persistent need for refined CNN en-hancements using limited resources. Responding to these complexities, we introduce a novel framework, the Mul-tiscale Low-Frequency Memory (MLFM) Network, with the goal to harness the full potential of CNNs while keep-ing their complexity unchanged. The MLFM efficiently preserves low-frequency information, enhancing perfor-mance in targeted computer vision tasks. Central to our MLFM is the Low-Frequency Memory Unit (LFMU), which stores various low-frequency data and forms a parallel channel to the core network. A key advantage of MLFM is its seamless compatibility with various prevalent networks, requiring no alterations to their original core structure. Testing on ImageNet demonstrated substantial accuracy improvements in multiple 2D CNNs, including ResNet, MobileNet, EfficientNet, and ConvNeXt. Furthermore, we showcase MLFM's versatility beyond traditional image classification by successfully integrating it into image-to-image translation tasks, specifically in semantic segmenta-tion networks like FCN and U-Net. In conclusion, our work signifies a pivotal stride in the journey of optimizing the ef-ficacy and efficiency of CNNs with limited resources. This research builds upon the existing CNN foundations and paves the way for future advancements in computer vision. Our codes are available at https://github.com/AlphaWuSeu/MLFM.
SyFormer: Structure-Guided Synergism Transformer for Large-Portion Image Inpainting
Authors: Wu Jie , Feng Yuchao , Xu Honghui , Zhu Chuanmeng , Zheng Jianwei
Published: March 2024

Image inpainting is in full bloom accompanied by the progress of convolutional neural networks (CNNs) and transformers, revolutionizing the practical management of abnormity disposal, image editing, etc. However, due to the ever-mounting image resolutions and missing areas, the challenges of distorted long-range dependencies from cluttered background distributions and reduced reference information in image domain inevitably rise, which further cause severe performance degradation. To address the challenges, we propose a novel large-portion image inpainting approach, namely the Structure-Guided Synergism Transformer (SyFormer), to rectify the discrepancies in feature representation and enrich the structural cues from limited reference. Specifically, we devise a dual-routing filtering module that employs a progressive filtering strategy to eliminate invalid noise interference and establish global-level texture correlations. Simultaneously, the structurally compact perception module maps an affinity matrix within the introduced structural priors from a structure-aware generator, assisting in matching and filling the corresponding patches of large-proportionally damaged images. Moreover, we carefully assemble the aforementioned modules to achieve feature complementarity. Finally, a feature decoding alignment scheme is introduced in the decoding process, which meticulously achieves texture amalgamation across hierarchical features. Extensive experiments are conducted on two publicly available datasets, i.e., CelebA-HQ and Places2, to qualitatively and quantitatively demonstrate the superiority of our model over state-of-the-arts.
Swift-Mapping: Online Neural Implicit Dense Mapping in Urban Scenes
Authors: Wu Ke , Zhang Kaizhao , Gao Mingzhe , Zhao Jieru , Gan Zhongxue , Ding Wenchao
Published: March 2024

Online dense mapping of urban scenes is of paramount importance for scene understanding of autonomous navigation. Traditional online dense mapping methods fuse sensor measurements (vision, lidar, etc.) across time and space via explicit geometric correspondence. Recently, NeRF-based methods have proved the superiority of neural implicit representations by high-fidelity reconstruction of large-scale city scenes. However, it remains an open problem how to integrate powerful neural implicit representations into online dense mapping. Existing methods are restricted to constrained indoor environments and are too computationally expensive to meet online requirements. To this end, we propose Swift-Mapping, an online neural implicit dense mapping framework in urban scenes. We introduce a novel neural implicit octomap (NIO) structure that provides efficient neural representation for large and dynamic urban scenes while retaining online update capability. Based on that, we propose an online neural dense mapping framework that effectively manages and updates neural octree voxel features. Our approach achieves SOTA reconstruction accuracy while being more than 10x faster in reconstruction speed, demonstrating the superior performance of our method in both accuracy and efficiency.
Toward Open-Set Human Object Interaction Detection
Authors: Wu Mingrui , Liu Yuqi , Ji Jiayi , Sun Xiaoshuai , Ji Rongrong
Published: March 2024

This work is oriented toward the task of open-set Human Object Interaction (HOI) detection. The challenge lies in identifying completely new, out-of-domain relationships, as opposed to in-domain ones which have seen improvements in zero-shot HOI detection. To address this challenge, we introduce a simple Disentangled HOI Detection (DHD) model for detecting novel relationships by integrating an open-set object detector with a Visual Language Model (VLM). We utilize a disentangled image-text contrastive learning metric for training and connect the bottom-up visual features to text embeddings through lightweight unary and pair-wise adapters. Our model can benefit from the open-set object detector and the VLM to detect novel action categories and combine actions with novel object categories. We further present the VG-HOI dataset, a comprehensive benchmark with over 17k HOI relationships for open-set scenarios. Experimental results show that our model can detect unknown action classes and combine unknown object classes. Furthermore, it can generalize to over 17k HOI classes while being trained on just 600 HOI classes.
SphereDiffusion: Spherical Geometry-Aware Distortion Resilient Diffusion Model
Authors: Wu Tao , Li Xuewei , Qi Zhongang , Hu Di , Wang Xintao , Shan Ying , Li Xi
Published: March 2024

Controllable spherical panoramic image generation holds substantial applicative potential across a variety of domains. However, it remains a challenging task due to the inherent spherical distortion and geometry characteristics, resulting in low-quality content generation. In this paper, we introduce a novel framework of SphereDiffusion to address these unique challenges, for better generating high-quality and precisely controllable spherical panoramic images. For the spherical distortion characteristic, we embed the semantics of the distorted object with text encoding, then explicitly construct the relationship with text-object correspondence to better use the pre-trained knowledge of the planar images. Meanwhile, we employ a deformable technique to mitigate the semantic deviation in latent space caused by spherical distortion. For the spherical geometry characteristic, in virtue of spherical rotation invariance, we improve the data diversity and optimization objectives in the training process, enabling the model to better learn the spherical geometry characteristic. Furthermore, we enhance the denoising process of the diffusion model, enabling it to effectively use the learned geometric characteristic to ensure the boundary continuity of the generated images. With these specific techniques, experiments on Structured3D dataset show that SphereDiffusion significantly improves the quality of controllable spherical image generation and relatively reduces around 35% FID on average.
FD3D: Exploiting Foreground Depth Map for Feature-Supervised Monocular 3D Object Detection
Authors: Wu Zizhang , Gan Yuanzhu , Wu Yunzhe , Wang Ruihao , Wang Xiaoquan , Pu Jian
Published: March 2024

Monocular 3D object detection usually adopts direct or hierarchical label supervision. Recently, the distillation supervision transfers the spatial knowledge from LiDAR- or stereo-based teacher networks to monocular detectors, but remaining the domain gap. To mitigate this issue and pursue adequate label manipulation, we exploit Foreground Depth map for feature-supervised monocular 3D object detection named FD3D, which develops the high-quality instructive intermediate features to conduct desirable auxiliary feature supervision with only the original image and annotation foreground object-wise depth map (AFOD) as input. Furthermore, we build up our instructive feature generation network to create instructive spatial features based on the sufficient correlation between image features and pre-processed AFOD, where AFOD provides the attention focus only on foreground objects to achieve clearer guidance in the detection task. Moreover, we apply the auxiliary feature supervision from the pixel and distribution level to achieve comprehensive spatial knowledge guidance. Extensive experiments demonstrate that our method achieves state-of-the-art performance on both the KITTI and nuScenes datasets, with no external data and no extra inference computational cost. We also conduct quantitative and qualitative studies to reveal the effectiveness of our designs.
SocialCVAE: Predicting Pedestrian Trajectory via Interaction Conditioned Latents
Authors: Xiang Wei , YIN Haoteng , Wang He , Jin Xiaogang
Published: March 2024

Pedestrian trajectory prediction is the key technology in many applications for providing insights into human behavior and anticipating human future motions. Most existing empirical models are explicitly formulated by observed human behaviors using explicable mathematical terms with deterministic nature, while recent work has focused on developing hybrid models combined with learning-based techniques for powerful expressiveness while maintaining explainability. However, the deterministic nature of the learned steering behaviors from the empirical models limits the models' practical performance. To address this issue, this work proposes the social conditional variational autoencoder (SocialCVAE) for predicting pedestrian trajectories, which employs a CVAE to explore behavioral uncertainty in human motion decisions. SocialCVAE learns socially reasonable motion randomness by utilizing a socially explainable interaction energy map as the CVAE's condition, which illustrates the future occupancy of each pedestrian's local neighborhood area. The energy map is generated using an energy-based interaction model, which anticipates the energy cost (i.e., repulsion intensity) of pedestrians' interactions with neighbors. Experimental results on two public benchmarks including 25 scenes demonstrate that SocialCVAE significantly improves prediction accuracy compared with the state-of-the-art methods, with up to 16.85% improvement in Average Displacement Error (ADE) and 69.18% improvement in Final Displacement Error (FDE). Code is available at: https://github.com/ViviXiang/SocialCVAE.
SPEAL: Skeletal Prior Embedded Attention Learning for Cross-Source Point Cloud Registration
Authors: Xiong Kezheng , Zheng Maoji , Xu Qingshan , Wen Chenglu , Shen Siqi , Wang Cheng
Published: March 2024

Point cloud registration, a fundamental task in 3D computer vision, has remained largely unexplored in cross-source point clouds and unstructured scenes. The primary challenges arise from noise, outliers, and variations in scale and density. However, neglected geometric natures of point clouds restricts the performance of current methods. In this paper, we propose a novel method termed SPEAL to leverage skeletal representations for effective learning of intrinsic topologies of point clouds, facilitating robust capture of geometric intricacy. Specifically, we design the Skeleton Extraction Module to extract skeleton points and skeletal features in an unsupervised manner, which is inherently robust to noise and density variances. Then, we propose the Skeleton-Aware GeoTransformer to encode high-level skeleton-aware features. It explicitly captures the topological natures and inter-point-cloud skeletal correlations with the noise-robust and density-invariant skeletal representations. Next, we introduce the Correspondence Dual-Sampler to facilitate correspondences by augmenting the correspondence set with skeletal correspondences. Furthermore, we construct a challenging novel cross-source point cloud dataset named KITTI CrossSource for benchmarking cross-source point cloud registration methods. Extensive quantitative and qualitative experiments are conducted to demonstrate our approach’s superiority and robustness on both cross-source and same-source datasets. To the best of our knowledge, our approach is the first to facilitate point cloud registration with skeletal geometric priors.
ZOOM: Learning Video Mirror Detection with Extremely-Weak Supervision
Authors: Xu Ke , Siu Tsun Wai , Lau Rynson W.H.
Published: March 2024

Mirror detection is an active research topic in computer vision. However, all existing mirror detectors learn mirror representations from large-scale pixel-wise datasets, which are tedious and expensive to obtain. Although weakly-supervised learning has been widely explored in related topics, we note that popular weak supervision signals (e.g., bounding boxes, scribbles, points) still require some efforts from the user to locate the target objects, with a strong assumption that the images to annotate always contain the target objects. Such an assumption may result in the over-segmentation of mirrors. Our key idea of this work is that the existence of mirrors over a time period may serve as a weak supervision to train a mirror detector, for two reasons. First, if a network can predict the existence of mirrors, it can essentially locate the mirrors. Second, we observe that the reflected contents of a mirror tend to be similar to those in adjacent frames, but exhibit considerable contrast to regions in far-away frames (e.g., non-mirror frames). To this end, in this paper, we propose ZOOM, the first method to learn robust mirror representations from extremely-weak annotations of per-frame ZerO-One Mirror indicators in videos. The key insight of ZOOM is to model the similarity and contrast (between mirror and non-mirror regions) in temporal variations to locate and segment the mirrors. To this end, we propose a novel fusion strategy to leverage temporal consistency information for mirror localization, and a novel temporal similarity-contrast modeling module for mirror segmentation. We construct a new video mirror dataset for training and evaluation. Experimental results under new and standard metrics show that ZOOM performs favorably against existing fully-supervised mirror detection methods.
HACDR-Net: Heterogeneous-Aware Convolutional Network for Diabetic Retinopathy Multi-Lesion Segmentation
Authors: Xu QiHao , Luo Xiaoling , Huang Chao , Liu Chengliang , Wen Jie , Wang Jialei , Xu Yong
Published: March 2024

Diabetic Retinopathy (DR), the leading cause of blindness in diabetic patients, is diagnosed by the condition of retinal multiple lesions. As a difficult task in medical image segmentation, DR multi-lesion segmentation faces the main concerns as follows. On the one hand, retinal lesions vary in location, shape, and size. On the other hand, because some lesions occupy only a very small part of the entire fundus image, the high proportion of background leads to difficulties in lesion segmentation. To solve the above problems, we propose a heterogeneous-aware convolutional network (HACDR-Net) that composes heterogeneous cross-convolution, heterogeneous modulated deformable convolution, and optional near-far-aware convolution. Our network introduces an adaptive aggregation module to summarize the heterogeneous feature maps and get diverse lesion areas in the heterogeneous receptive field along the channels and space. In addition, to solve the problem of the highly imbalanced proportion of focal areas, we design a new medical image segmentation loss function, Noise Adjusted Loss (NALoss). NALoss balances the predictive feature distribution of background and lesion by jointing Gaussian noise and hard example mining, thus enhancing awareness of lesions. We conduct the experiments on the public datasets IDRiD and DDR, and the experimental results show that the proposed method achieves better performance than other state-of-the-art methods. The code is open-sourced on github.com/xqh180110910537/HACDR-Net.
Decoupled Contrastive Learning for Long-Tailed Recognition
Authors: Xuan Shiyu , Zhang Shiliang
Published: March 2024

Supervised Contrastive Loss (SCL) is popular in visual representation learning.
Given an anchor image, SCL pulls two types of positive samples, i.e., its augmentation and other images from the same class together, while pushes negative images apart to optimize the learned embedding. In the scenario of long-tailed recognition, where the number of samples in each class is imbalanced, treating two types of positive samples equally leads to the biased optimization for intra-category distance. In addition, similarity relationship among negative samples, that are ignored by SCL, also presents meaningful semantic cues. To improve the performance on long-tailed recognition, this paper addresses those two issues of SCL by decoupling the training objective. Specifically, it decouples two types of positives in SCL and optimizes their relations toward different objectives to alleviate the influence of the imbalanced dataset. We further propose a patch-based self distillation to transfer knowledge from head to tail classes to relieve the under-representation of tail classes. It uses patch-based features to mine shared visual patterns among different instances and leverages a self distillation procedure to transfer such knowledge. Experiments on different long-tailed classification benchmarks demonstrate the superiority of our method. For instance, it achieves the 57.7% top-1 accuracy on the ImageNet-LT dataset. Combined with the ensemble-based method, the performance can be further boosted to 59.7%, which substantially outperforms many recent works. Our code will be released.
Self-Supervised 3D Human Mesh Recovery from a Single Image with Uncertainty-Aware Learning
Authors: Yan Guoli , Zhong Zichun , Hua Jing
Published: March 2024

Despite achieving impressive improvement in accuracy, most existing monocular 3D human mesh reconstruction methods require large-scale 2D/3D ground-truths for supervision, which limits their applications on unlabeled in-the-wild data that is ubiquitous. To alleviate the reliance on 2D/3D ground-truths, we present a self-supervised 3D human pose and shape reconstruction framework that relies only on self-consistency between intermediate representations of images and projected 2D predictions. Specifically, we extract 2D joints and depth maps from monocular images as proxy inputs, which provides complementary clues to infer accurate 3D human meshes. Furthermore, to reduce the impacts from noisy and ambiguous inputs while better concentrate on the high-quality information, we design an uncertainty-aware module to automatically learn the reliability of the inputs at body-joint level based on the consistency between 2D joints and depth map. Experiments on benchmark datasets show that our approach outperforms other state-of-the-art methods at similar supervision levels.
Referred by Multi-Modality: A Unified Temporal Transformer for Video Object Segmentation
Authors: Yan Shilin , Zhang Renrui , Guo Ziyu , Chen Wenchao , Zhang Wei , Li Hongyang , Qiao Yu , Dong Hao , He Zhongjiang , Gao Peng
Published: March 2024

Recently, video object segmentation (VOS) referred by multi-modal signals, e.g., language and audio, has evoked increasing attention in both industry and academia. It is challenging for exploring the semantic alignment within modalities and the visual correspondence across frames.
However, existing methods adopt separate network architectures for different modalities, and neglect the inter-frame temporal interaction with references. In this paper, we propose MUTR, a Multi-modal Unified Temporal transformer for Referring video object segmentation. With a unified framework for the first time, MUTR adopts a DETR-style transformer and is capable of segmenting video objects designated by either text or audio reference. Specifically, we introduce two strategies to fully explore the temporal relations between videos and multi-modal signals.
Firstly, for low-level temporal aggregation before the transformer, we enable the multi-modal references to capture multi-scale visual cues from consecutive video frames. This effectively endows the text or audio signals with temporal knowledge and boosts the semantic alignment between modalities.
Secondly, for high-level temporal interaction after the transformer, we conduct inter-frame feature communication for different object embeddings, contributing to better object-wise correspondence for tracking along the video.
On Ref-YouTube-VOS and AVSBench datasets with respective text and audio references, MUTR achieves +4.2% and +8.7% J&F improvements to state-of-the-art methods, demonstrating our significance for unified multi-modal VOS. Code is released at https://github.com/OpenGVLab/MUTR.
Embracing Language Inclusivity and Diversity in CLIP through Continual Language Learning
Authors: Yang Bang , Dai Yong , Cheng Xuxin , Li Yaowei , Raza Asif , Zou Yuexian
Published: March 2024

While vision-language pre-trained models (VL-PTMs) have advanced multimodal research in recent years, their mastery in a few languages like English restricts their applicability in broader communities. To this end, there is an increasing interest in developing multilingual VL models via a joint-learning setup, which, however, could be unrealistic due to expensive costs and data availability. In this work, we propose to extend VL-PTMs' language capacity by continual language learning (CLL), where a model needs to update its linguistic knowledge incrementally without suffering from catastrophic forgetting (CF). We begin our study by introducing a model dubbed CLL-CLIP, which builds upon CLIP, a prevailing VL-PTM that has acquired image-English text alignment. Specifically, CLL-CLIP contains an expandable token embedding layer to handle linguistic differences. It solely trains token embeddings to improve memory stability and is optimized under cross-modal and cross-lingual objectives to learn the alignment between images and multilingual texts. To alleviate CF raised by covariate shift and lexical overlap, we further propose a novel approach that ensures the identical distribution of all token embeddings during initialization and regularizes token embedding learning during training. We construct a CLL benchmark covering 36 languages based on MSCOCO and XM3600 datasets and then evaluate multilingual image-text retrieval performance. Extensive experiments verify the effectiveness of CLL-CLIP and show that our approach can boost CLL-CLIP, e.g., by 6.7% in text-to-image average Recall@1 on XM3600, and improve various state-of-the-art methods consistently. Our code and data are available at https://github.com/yangbang18/CLFM.
Multi-Modal Prompting for Open-Vocabulary Video Visual Relationship Detection
Authors: Yang Shuo , Wang Yongqi , Ji Xiaofeng , Wu Xinxiao
Published: March 2024

Open-vocabulary video visual relationship detection aims to extend video visual relationship detection beyond annotated categories by detecting unseen relationships between objects in videos. Recent progresses in open-vocabulary perception, primarily driven by large-scale image-text pre-trained models like CLIP, have shown remarkable success in recognizing novel objects and semantic categories. However, directly applying CLIP-like models to video visual relationship detection encounters significant challenges due to the substantial gap between images and video object relationships. To address this challenge, we propose a multi-modal prompting method that adapts CLIP well to open-vocabulary video visual relationship detection by prompt-tuning on both visual representation and language input. Specifically, we enhance the image encoder of CLIP by using spatio-temporal visual prompting to capture spatio-temporal contexts, thereby making it suitable for object-level relationship representation in videos. Furthermore, we propose visual-guided language prompting to leverage CLIP's comprehensive semantic knowledge for discovering unseen relationship categories, thus facilitating recognizing novel video relationships. Extensive experiments on two public datasets, VidVRD and VidOR, demonstrate the effectiveness of our method, especially achieving a significant gain of nearly 10% in mAP on novel relationship categories on the VidVRD dataset.
Decomposing Semantic Shifts for Composed Image Retrieval
Authors: Yang Xingyu , Liu Daqing , Zhang Heng , Luo Yong , Wang Chaoyue , Zhang Jing
Published: March 2024

Composed image retrieval is a type of image retrieval task where the user provides a reference image as a starting point and specifies a text on how to shift from the starting point to the desired target image. However, most existing methods focus on the composition learning of text and reference images and oversimplify the text as a description, neglecting the inherent structure and the user's shifting intention of the texts. As a result, these methods typically take shortcuts that disregard the visual cue of the reference images. To address this issue, we reconsider the text as instructions and propose a Semantic Shift Network (SSN) that explicitly decomposes the semantic shifts into two steps: from the reference image to the visual prototype and from the visual prototype to the target image. Specifically, SSN explicitly decomposes the instructions into two components: degradation and upgradation, where the degradation is used to picture the visual prototype from the reference image, while the upgradation is used to enrich the visual prototype into the final representations to retrieve the desired target image. The experimental results show that the proposed SSN demonstrates a significant improvement of 5.42% and 1.37% on the CIRR and FashionIQ datasets, respectively, and establishes a new state-of-the-art performance. The code is available at https://github.com/starxing-yuu/SSN.
STDiff: Spatio-Temporal Diffusion for Continuous Stochastic Video Prediction
Authors: Ye Xi , Bilodeau Guillaume-Alexandre
Published: March 2024

Predicting future frames of a video is challenging because it is difficult to learn the uncertainty of the underlying factors influencing their contents. In this paper, we propose a novel video prediction model, which has infinite-dimensional latent variables over the spatio-temporal domain. Specifically, we first decompose the video motion and content information, then take a neural stochastic differential equation to predict the temporal motion information, and finally, an image diffusion model autoregressively generates the video frame by conditioning on the predicted motion feature and the previous frame. The better expressiveness and stronger stochasticity learning capability of our model lead to state-of-the-art video prediction performances. As well, our model is able to achieve temporal continuous prediction, i.e., predicting in an unsupervised way the future video frames with an arbitrarily high frame rate. Our code is available at https://github.com/XiYe20/STDiffProject.
Step Vulnerability Guided Mean Fluctuation Adversarial Attack against Conditional Diffusion Models
Authors: Yu Hongwei , Chen Jiansheng , Ding Xinlong , Zhang Yudong , Tang Ting , Ma Huimin
Published: March 2024

The high-quality generation results of conditional diffusion models have brought about concerns regarding privacy and copyright issues. As a possible technique for preventing the abuse of diffusion models, the adversarial attack against diffusion models has attracted academic attention recently. In this work, utilizing the phenomenon that diffusion models are highly sensitive to the mean value of the input noise, we propose the Mean Fluctuation Attack (MFA) to introduce mean fluctuations by shifting the mean values of the estimated noises during the reverse process. In addition, we reveal that the vulnerability of different reverse steps against adversarial attacks actually varies significantly. By modeling the step vulnerability and using it as guidance to sample the target steps for generating adversarial examples, the effectiveness of adversarial attacks can be substantially enhanced. Extensive experiments show that our algorithm can steadily cause the mean shift of the predicted noises so as to disrupt the entire reverse generation process and degrade the generation results significantly. We also demonstrate that the step vulnerability is intrinsic to the reverse process by verifying its effectiveness in an attack method other than MFA. Code and Supplementary is available at https://github.com/yuhongwei22/MFA
PaintHuman: Towards High-Fidelity Text-to-3D Human Texturing via Denoised Score Distillation
Authors: Yu Jianhui , Zhu Hao , Jiang Liming , Loy Chen Change , Cai Weidong , Wu Wayne
Published: March 2024

Recent advances in zero-shot text-to-3D human generation, which employ the human model prior (e.g., SMPL) or Score Distillation Sampling (SDS) with pre-trained text-to-image diffusion models, have been groundbreaking. However, SDS may provide inaccurate gradient directions under the weak diffusion guidance, as it tends to produce over-smoothed results and generate body textures that are inconsistent with the detailed mesh geometry. Therefore, directly leveraging existing strategies for high-fidelity text-to-3D human texturing is challenging. In this work, we propose a model called PaintHuman to addresses the challenges from two perspectives. We first propose a novel score function, Denoised Score Distillation (DSD), which directly modifies the SDS by introducing negative gradient components to iteratively correct the gradient direction and generate high-quality textures. In addition, we use the depth map as a geometric guide to ensure that the texture is semantically aligned to human mesh surfaces. To guarantee the quality of rendered results, we employ geometry-aware networks to predict surface materials and render realistic human textures. Extensive experiments, benchmarked against state-of-the-art (SoTA) methods, validate the efficacy of our approach.Project page: https://painthuman.github.io/.
Weakly-Supervised Temporal Action Localization by Inferring Salient Snippet-Feature
Authors: Yun Wulian , Qi Mengshi , Wang Chuanming , Ma Huadong
Published: March 2024

Weakly-supervised temporal action localization aims to locate action regions and identify action categories in untrimmed videos simultaneously by taking only video-level labels as the supervision. Pseudo label generation is a promising strategy to solve the challenging problem, but the current methods ignore the natural temporal structure of the video that can provide rich information to assist such a generation process. In this paper, we propose a novel weakly-supervised temporal action localization method by inferring salient snippet-feature. First, we design a saliency inference module that exploits the variation relationship between temporal neighbor snippets to discover salient snippet-features, which can reflect the significant dynamic change in the video. Secondly, we introduce a boundary refinement module that enhances salient snippet-features through the information interaction unit. Then, a discrimination enhancement module is introduced to enhance the discriminative nature of snippet-features. Finally, we adopt the refined snippet-features to produce high-fidelity pseudo labels, which could be used to supervise the training of the action localization network. Extensive experiments on two publicly available datasets, i.e., THUMOS14 and ActivityNet v1.3, demonstrate our proposed method achieves significant improvements compared to the state-of-the-art methods. Our source code is available at https://github.com/wuli55555/ISSF.
Zero-Shot Aerial Object Detection with Visual Description Regularization
Authors: Zang Zhengqing , Lin Chenyu , Tang Chenwei , Wang Tao , Lv Jiancheng
Published: March 2024

Existing object detection models are mainly trained on large-scale labeled datasets. However, annotating data for novel aerial object classes is expensive since it is time-consuming and may require expert knowledge. Thus, it is desirable to study label-efficient object detection methods on aerial images. In this work, we propose a zero-shot method for aerial object detection named visual Description Regularization, or DescReg.
Concretely, we identify the weak semantic-visual correlation of the aerial objects and aim to address the challenge with prior descriptions of their visual appearance. Instead of directly encoding the descriptions into class embedding space which suffers from the representation gap problem, we propose to infuse the prior inter-class visual similarity conveyed in the descriptions into the embedding learning. The infusion process is accomplished with a newly designed similarity-aware triplet loss which incorporates structured regularization on the representation space. We conduct extensive experiments with three challenging aerial object detection datasets, including DIOR, xView, and DOTA. The results demonstrate that DescReg significantly outperforms the state-of-the-art ZSD methods with complex projection designs and generative frameworks, e.g., DescReg outperforms
best reported ZSD method on DIOR by 4.5 mAP on unseen classes and 8.1 in HM. We further show the generalizability of DescReg by integrating it into generative ZSD methods as well as varying the detection architecture.
Codes will be released at https://github.com/zq-zang/DescReg.
Weakly-Supervised Mirror Detection via Scribble Annotations
Authors: Zha Mingfeng , Pei Yunqiang , Wang Guoqing , Li Tianyu , Yang Yang , Qian Wenbin , Shen Heng Tao
Published: March 2024

Mirror detection is of great significance for avoiding false recognition of reflected objects in computer vision tasks. Existing mirror detection frameworks usually follow a supervised setting, which relies heavily on high quality labels and suffers from poor generalization. To resolve this, we instead propose the first weakly-supervised mirror detection framework and also provide the first scribble-based mirror dataset. Specifically, we relabel 10,158 images, most of which have a labeled pixel ratio of less than 0.01 and take only about 8 seconds to label. Considering that the mirror regions usually show great scale variation, and also irregular and occluded, thus leading to issues of incomplete or over detection, we propose a local-global feature enhancement (LGFE) module to fully capture the context and details. Moreover, it is difficult to obtain basic mirror structure using scribble annotation, and the distinction between foreground (mirror) and background (non-mirror) features is not emphasized caused by mirror reflections. Therefore, we propose a foreground-aware mask attention (FAMA), integrating mirror edges and semantic features to complete mirror regions and suppressing the influence of backgrounds. Finally, to improve the robustness of the network, we propose a prototype contrast loss (PCL) to learn more general foreground features across images. Extensive experiments show that our network outperforms relevant state-of-the-art weakly supervised methods, and even some fully supervised methods. The dataset and codes are available at https://github.com/winter-flow/WSMD.