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Hierarchical Semantic Correspondence Networks for Video Paragraph Grounding

Hierarchical Semantic Correspondence Networks for Video Para...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Tan, Chaolei Lin, Zihang Hu, Jian-Fang Zheng, Wei-Shi Lai, Jianhuang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (纸本)9798350301298

Video Paragraph Grounding (VPG) is an essential yet challenging task in vision-language understanding, which aims to jointly localize multiple events from an untrimmed video with a paragraph query description. One of the critical challenges in addressing this problem is to comprehend the complex semantic relations between visual and textual modalities. Previous methods focus on modeling the contextual information between the video and text from a single-level perspective (i.e., the sentence level), ignoring rich visual-textual correspondence relations at different semantic levels, e.g., the video-word and video-paragraph correspondence. To this end, we propose a novel Hierarchical Semantic Correspondence Network (HSCNet), which explores multi-level visual-textual correspondence by learning hierarchical semantic alignment and utilizes dense supervision by grounding diverse levels of queries. Specifically, we develop a hierarchical encoder that encodes the multi-modal inputs into semantics-aligned representations at different levels. To exploit the hierarchical semantic correspondence learned in the encoder for multi-level supervision, we further design a hierarchical decoder that progressively performs finer grounding for lower-level queries conditioned on higher-level semantics. Extensive experiments demonstrate the effectiveness of HSCNet and our method significantly outstrips the state-of-the-arts on two challenging benchmarks, i.e., ActivityNet-Captions and TACoS.

关键词： and reasoning language vision

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Detecting Everything in the Open World: Towards Universal Object Detection

Detecting Everything in the Open World: Towards Universal Ob...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Zhenyu Li, Yali Chen, Xi Lim, Ser-Nam Torralba, Antonio Zhao, Hengshuang Wang, Shengjin Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Univ Hong Kong Hong Kong Peoples R China Meta AI New York NY USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9798350301298

In this paper, we formally address universal object detection, which aims to detect every scene and predict every category. The dependence on human annotations, the limited visual information, and the novel categories in the open world severely restrict the universality of traditional detectors. We propose UniDetector, a universal object detector that has the ability to recognize enormous categories in the open world. The critical points for the universality of UniDetector are: 1) it leverages images of multiple sources and heterogeneous label spaces for training through the alignment of image and text spaces, which guarantees sufficient information for universal representations. 2) it generalizes to the open world easily while keeping the balance between seen and unseen classes, thanks to abundant information from both vision and language modalities. 3) it further promotes the generalization ability to novel categories through our proposed decoupling training manner and probability calibration. These contributions allow UniDetector to detect over 7k categories, the largest measurable category size so far, with only about 500 classes participating in training. Our UniDetector behaves the strong zero-shot generalization ability on large-vocabulary datasets - it surpasses the traditional supervised baselines by more than 4% on average without seeing any corresponding images. On 13 public detection datasets with various scenes, UniDetector also achieves state-of-the-art performance with only a 3% amount of training data. (1)

关键词： detection recognition: Categorization retrieval

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Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training

Geometric Visual Similarity Learning in 3D Medical Image Sel...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： He, Yuting Yang, Guanyu Ge, Rongjun Chen, Yang Coatrieux, Jean-Louis Wang, Boyu Li, Shuo Southeast Univ Dhaka Bangladesh Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Univ Rennes 1 Rennes France Western Univ London England Case Western Reserve Univ Cleveland OH 44106 USA

ISBN: (纸本)9798350301298

Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training, due to their sharing of numerous same semantic regions. However, the lack of the semantic prior in metrics and the semantic-independent variation in 3D medical images make it challenging to get a reliable measurement for the inter-image similarity, hindering the learning of consistent representation for same semantics. We investigate the challenging problem of this task, i.e., learning a consistent representation between images for a clustering effect of same semantic features. We propose a novel visual similarity learning paradigm, Geometric Visual Similarity Learning, which embeds the prior of topological invariance into the measurement of the inter-image similarity for consistent representation of semantic regions. To drive this paradigm, we further construct a novel geometric matching head, the Z-matching head, to collaboratively learn the global and local similarity of semantic regions, guiding the efficient representation learning for different scale-level inter-image semantic features. Our experiments demonstrate that the pre-training with our learning of inter-image similarity yields more powerful inner-scene, inter-scene, and global-local transferring ability on four challenging 3D medical image tasks. Our codes and pre-trained models will be publicly available.

关键词： cell microscopy Medical and biological vision

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Multimodal Prompting with Missing Modalities for Visual recognition

Multimodal Prompting with Missing Modalities for Visual Reco...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Lee, Yi-Lun Tsai, Yi-Hsuan Chiu, Wei-Chen Lee, Chen-Yu Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan Google Mountain View CA USA

ISBN: (纸本)9798350301298

In this paper, we tackle two challenges in multimodal learning for visual recognition: 1) when missing-modality occurs either during training or testing in real-world situations;and 2) when the computation resources are not available to finetune on heavy transformer models. To this end, we propose to utilize prompt learning and mitigate the above two challenges together. Specifically, our modality-missing-aware prompts can be plugged into multimodal transformers to handle general missing-modality cases, while only requiring less than 1% learnable parameters compared to training the entire model. We further explore the effect of different prompt configurations and analyze the robustness to missing modality. Extensive experiments are conducted to show the effectiveness of our prompt learning framework that improves the performance under various missing-modality cases, while alleviating the requirement of heavy model re-training. Code is available.

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Learning with Noisy labels via Self-supervised Adversarial Noisy Masking

Learning with Noisy labels via Self-supervised Adversarial N...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Tu, Yuanpeng Zhang, Boshen Li, Yuxi Liu, Liang Li, Jian Zhang, Jiangning Wang, Yabiao Wang, Chengjie Zhao, Cai Rong Tongji Univ Dept Elect & Informat Engn Shanghai Peoples R China Tencent YouTu Lab Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350301298

Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate this problem via identifying and removing noisy samples or correcting their labels according to the statistical properties (e.g., loss values) among training samples. In this paper, we aim to tackle this problem from a new perspective, delving into the deep feature maps, we empirically find that models trained with clean and mislabeled samples manifest distinguishable activation feature distributions. From this observation, a novel robust training approach termed adversarial noisy masking is proposed. The idea is to regularize deep features with a label quality guided masking scheme, which adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. Further, an auxiliary task is designed to reconstruct input data, it naturally provides noise-free self-supervised signals to reinforce the generalization ability of models. The proposed method is simple yet effective, it is tested on synthetic and real-world noisy datasets, where significant improvements are obtained over previous methods. Code is available at https://***/yuanpengtu/SANM.

关键词： detection recognition: Categorization retrieval

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Leverage Interactive Affinity for Affordance Learning

Leverage Interactive Affinity for Affordance Learning

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Luo, Hongchen Zhai, Wei Zhang, Jing Cao, Yang Tao, Dacheng Univ Sci & Technol China Hefei Peoples R China Univ Sydney Camperdown Australia JD Explore Acad Beijing Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350301298

Perceiving potential "action possibilities" (i.e., affordance) regions of images and learning interactive functionalities of objects from human demonstration is a challenging task due to the diversity of human-object interactions. Prevailing affordance learning algorithms often adopt the label assignment paradigm and presume that there is a unique relationship between functional region and affordance label, yielding poor performance when adapting to unseen environments with large appearance variations. In this paper, we propose to leverage interactive affinity for affordance learning, *** interactive affinity from human-object interaction and transferring it to non-interactive objects. Interactive affinity, which represents the contacts between different parts of the human body and local regions of the target object, can provide inherent cues of interconnectivity between humans and objects, thereby reducing the ambiguity of the perceived action possibilities. Specifically, we propose a pose-aided interactive affinity learning framework that exploits human pose to guide the network to learn the interactive affinity from human-object interactions. Particularly, a keypoint heuristic perception (KHP) scheme is devised to exploit the keypoint association of human pose to alleviate the uncertainties due to interaction diversities and contact occlusions. Besides, a contact-driven affordance learning (CAL) dataset is constructed by collecting and labeling over 5, 000 images. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. Code and dataset: ***/lhc1224/PIAL-Net.

关键词： Embodied vision: Active agents simulation

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Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR

Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Sain, Aneeshan Bhunia, Ayan Kumar Koley, Subhadeep Chowdhury, Pinaki Nath Chattopadhyay, Soumitri Xiang, Tao Song, Yi-Zhe Univ Surrey CVSSP SketchX Guildford Surrey England FlyTek Surrey Joint Res Ctr Artificial Intelligen Guildford England

ISBN: (纸本)9798350301298

This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by approximate to 11%. This is not via complicated design though, but by addressing two critical issues facing the community (i) the gold standard triplet loss does not enforce holistic latent space geometry, and (ii) there are never enough sketches to train a high accuracy model. For the former, we propose a simple modification to the standard triplet loss, that explicitly enforces separation amongst photos/sketch instances. For the latter, we put forward a novel knowledge distillation module can leverage photo data for model training. Both modules are then plugged into a novel plug-n-playable training paradigm that allows for more stable training. More specifically, for (i) we employ an intra-modal triplet loss amongst sketches to bring sketches of the same instance closer from others, and one more amongst photos to push away different photo instances while bringing closer a structurally augmented version of the same photo (offering a gain of approximate to 4-6%). To tackle (ii), we first pre-train a teacher on the large set of unlabelled photos over the aforementioned intra-modal photo triplet loss. Then we distill the contextual similarity present amongst the instances in the teacher's embedding space to that in the student's embedding space, by matching the distribution over inter-feature distances of respective samples in both embedding spaces (delivering a further gain of approximate to 4-5%). Apart from outperforming prior arts significantly, our model also yields satisfactory results on generalising to new classes. Project page: https://***/Sketch_PVT/

关键词： vision applications and systems

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HaGRID - HAnd Gesture recognition Image Dataset

HaGRID - HAnd Gesture Recognition Image Dataset

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Alexander, Kapitanov Karina, Kvanchiani Alexander, Nagaev Roman, Kraynov Andrei, Makhliarchuk SaluteDevices Moscow Russia

ISBN: (纸本)9798350318920;9798350318937

This paper introduces an enormous dataset, HaGRID (HAnd Gesture recognition Image Dataset), to build a hand gesture recognition (HGR) system concentrating on interaction with devices to manage them. That is why all 18 chosen gestures are endowed with the semiotic function and can be interpreted as a specific action. Although the gestures are static, they were picked up, especially for the ability to design several dynamic gestures. It allows the trained model to recognize not only static gestures such as "like" and "stop" but also "swipes" and "drag and drop" dynamic gestures. The HaGRID contains 554,800 images and bounding box annotations with gesture labels to solve hand detection and gesture classification tasks. The low variability in context and subjects of other datasets was the reason for creating the dataset without such limitations. Utilizing crowdsourcing platforms allowed us to collect samples recorded by 37,583 subjects in at least as many scenes with subject-to-camera distances from 0.5 to 4 meters in various natural light conditions. The influence of the diversity characteristics was assessed in ablation study experiments. Also, we demonstrate the HaGRID ability to be used for pretraining models in HGR tasks. The HaGRID and pre-trained models are publicly available(12).

关键词： Algorithms Algorithms Algorithms Biometrics body pose Datasets and evaluations face gesture Image recognition and understanding

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NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering

NeUDF: Leaning Neural Unsigned Distance Fields with Volume R...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liu, Yu-Tao Wang, Li Yang, Jie Chen, Weikai Meng, Xiaoxu Yang, Bo Gao, Lin Chinese Acad Sci Beijing Key Lab Mobile Comp & Pervas Device Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Tencent Games Digital Content Technol Ctr Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in neural implicit surface rendering. However, existing methods based on signed distancefunction (SDF) are limited to closed surfaces, failing to reconstruct a wide range of real-world objects that contain open-surface structures. In this work, we introduce a new neural rendering framework, coded NeUDF1, that can reconstruct surfaces with arbitrary topologies solely from multi-view supervision. To gain the flexibility of representing arbitrary surfaces, NeUDF leverages the unsigned distance function (UDF) as surface representation. While a naive extension of an SDF-based neural renderer cannot scale to UDF, we propose two new formulations of weight function specially tailored for UDF-based volume rendering. Furthermore, to cope with open surface rendering, where the in/out test is no longer valid, we present a dedicated normal regularization strategy to resolve the surface orientation ambiguity. We extensively evaluate our method over a number ofchallenging datasets, including DTU [21], MGN [5], and Deep Fashion 3D [61]. Experimental results demonstrate that NeUDF can significantly outperform the state-of-the-art method in the task of multi-view surface reconstruction, especially for complex shapes with open boundaries.

关键词： vision + graphics

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Adversarial Normalization: I Can visualize Everything (ICE)

Adversarial Normalization: I Can visualize Everything (ICE)

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Choi, Hoyoung Jin, Seungwan Han, Kyungsik Hanyang Univ Seoul South Korea

ISBN: (纸本)9798350301298

vision transformers use [CLS] tokens to predict image classes. Their explainability visualization has been studied using relevant information from [CLS] tokens or focusing on attention scores during self-attention. Such visualization, however, is challenging because of the dependence of the structure of a vision transformer on skip connections and attention operators, the instability of non-linearities in the learning process, and the limited reflection of self-attention scores on relevance. We argue that the output vectors for each input patch token in a vision transformer retain the image information of each patch location, which can facilitate the prediction of an image class. In this paper, we propose ICE (Adversarial Normalization: I Can visualize Everything), a novel method that enables a model to directly predict a class for each patch in an image;thus, advancing the effective visualization of the explainability of a vision transformer. Our method distinguishes background from foreground regions by predicting background classes for patches that do not determine image classes. We used the DeiT-S model, the most representative model employed in studies, on the explainability visualization of vision transformers. On the ImageNet-Segmentation dataset, ICE outperformed all explainability visualization methods for four cases depending on the model size. We also conducted quantitative and qualitative analyses on the tasks of weakly-supervised object localization and unsupervised object discovery. On the CUB200-2011 and PASCALVOC07/12 datasets, ICE achieved comparable performance to the state-of-the-art methods. We incorporated ICE into the encoder of DeiT-S and improved efficiency by 44.01% on the ImageNet dataset over that achieved by the original DeiT-S model. We showed performance on the accuracy and efficiency comparable to EViT, the state-of-the-art pruning model, demonstrating the effectiveness of ICE. The code is available at https://***/Hanyang-HCC-La

关键词： Explainable computer vision

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