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LINe: Out-of-Distribution Detection by Leveraging Important Neurons

LINe: Out-of-Distribution Detection by Leveraging Important ...

引用

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

作者： Ahn, Yong Hyun Park, Gyeong-Moon Kim, Seong Tae Kyung Hee Univ Dept Artificial Intelligence Seoul South Korea Kyung Hee Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350301298

It is important to quantify the uncertainty of input samples, especially in mission-critical domains such as autonomous driving and healthcare, where failure predictions on out-of-distribution (OOD) data are likely to cause big problems. OOD detection problem fundamentally begins in that the model cannot express what it is not aware of Post-hoc OOD detection approaches are widely explored because they do not require an additional re-training process which might degrade the model's performance and increase the training cost. In this study, from the perspective of neurons in the deep layer of the model representing high-level features, we introduce a new aspect for analyzing the difference in model outputs between in-distribution data and OOD data. We propose a novel method, Leveraging Important Neurons (LINe), for post-hoc Out of distribution detection. Shapley value-based pruning reduces the effects of noisy outputs by selecting only high-contribution neurons for predicting specific classes of input data and masking the rest. Activation clipping fixes all values above a certain threshold into the same value, allowing LINe to treat all the class-specificfeatures equally and just consider the difference between the number of activated feature differences between in-distribution and OOD data. Comprehensive experiments verify the effectiveness of the proposed method by outperforming state-of-the-art post-hoc OOD detection methods on CIFAR-10, CIFAR-100, and imageNet datasets. Code is available on https://***/LINe-OOD

关键词： detection recognition: Categorization retrieval

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Query-Dependent Video Representation for Moment Retrieval and Highlight Detection

Query-Dependent Video Representation for Moment Retrieval an...

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

作者： Moon, WonJun Hyun, Sangeek Park, SangUk Park, Dongchan Heo, Jae-Pil Sungkyunkwan Univ Seoul South Korea Pyler Seoul South Korea

ISBN: (纸本)9798350301298

Recently, video moment retrieval and highlight detection (MR/HD) are being spotlighted as the demand for video understanding is drastically increased. The key objective of MR/HD is to localize the moment and estimate clip-wise accordance level, i.e., saliency score, to the given text query. Although the recent transformer-based models brought some advances, we found that these methods do not fully exploit the information of a given query. For example, the relevance between text query and video contents is sometimes neglected when predicting the moment and its saliency. To tackle this issue, we introduce Query-Dependent DETR (QD-DETR), a detection transformer tailored for MR/HD. As we observe the insignificant role of a given query in transformer architectures, our encoding module starts with cross-attention layers to explicitly inject the context of text query into video representation. Then, to enhance the model's capability of exploiting the query information, we manipulate the video-query pairs to produce irrelevant pairs. Such negative (irrelevant) video-query pairs are trained to yield low saliency scores, which in turn, encourages the model to estimate precise accordance between query-video pairs. Lastly, we present an input-adaptive saliency predictor which adaptively defines the criterion of saliency scores for the given video-query pairs. Our extensive studies verify the importance of building the query-dependent representation for MR/HD. Specifically, QD-DETR outperforms state-of-the-art methods on QVHighlights, TVSum, and Charades-STA datasets. Codes are available at ***/wjun0830/QD-DETR.

关键词： detection recognition: Categorization retrieval

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NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering

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

引用

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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Unified Physical-Digital Attack Detection Challenge

Unified Physical-Digital Attack Detection Challenge

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

作者： Yuan, Haocheng Liu, Ajian Zheng, Junze Wan, Jun Deng, Jiankang Escalera, Sergio Escalante, Hugo Jair Guyon, Isabelle Lei, Zhen MUST Macau Peoples R China CASIA MAIS Beijing Peoples R China ICI PLC London England CVC Madrid Spain Chinese Acad Sci CAIR HKISI Beijing Peoples R China UCAS SAI Beijing Peoples R China CINVESTAV INAOE Mexico City DF Mexico ChaLearn Berkeley CA USA

ISBN: (纸本)9798350365474

Face Anti-Spoofing (FAS) is crucial to safeguard Face recognition (FR) Systems. In real-world scenarios, FRs are confronted with both physical and digital attacks. However, existing algorithms often address only one type of attack at a time, which poses significant limitations in realworld scenarios where FR systems face hybrid physical-digital threats. To facilitate the research of Unified Attack Detection (UAD) algorithms, a large-scale UniAttackData dataset has been collected. UniAttackData is the largest public dataset for Unified Attack Detection, with a total of 28,706 videos, where each unique identity encompasses all advanced attack types. Based on this dataset, we organized a Unified Physical-Digital Face Attack Detection Challenge to boost the research in Unified Attack Detections. It attracted 136 teams for the development phase, with 13 qualifying for the final round. The results re-verified by the organizing team were used for the final ranking. This paper comprehensively reviews the challenge, detailing the dataset introduction, protocol definition, evaluation criteria, and a summary of published results. Finally, we focus on the detailed analysis of the highest-performing algorithms and offer potential directions for unified physical-digital attack detection inspired by this competition. Challenge Website: https: // sites. google. com/ view/ face - anti - spoofing - challenge / welcome / challengecvpr2024

关键词： Face recognition

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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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VectorFloorSeg: Two-Stream Graph Attention Network for Vectorized Roughcast Floorplan Segmentation

VectorFloorSeg: Two-Stream Graph Attention Network for Vecto...

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

作者： Yang, Bingchen Jiang, Haiyong Pan, Hao Xiao, Jun Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

Vector graphics (VG) are ubiquitous in industrial designs. In this paper, we address semantic segmentation of a typical VG, i.e., roughcast floorplans with bare wall structures, whose output can be directly used for further applications like interior furnishing and room space modeling. Previous semantic segmentation works mostly process well-decorated floorplans in raster images and usually yield aliased boundaries and outlier fragments in segmented rooms, due to pixel-level segmentation that ignores the regular elements (e.g. line segments) in vector floorplans. To overcome these issues, we propose to fully utilize the regular elements in vector floorplans for more integral segmentation. Our pipeline predicts room segmentation from vector floorplans by dually classifying line segments as room boundaries, and regions partitioned by line segments as room segments. To fully exploit the structural relationships between lines and regions, we use two-stream graph neural networks to process the line segments and partitioned regions respectively, and devise a novel modulated graph attention layer to fuse the heterogeneous information from one stream to the other. Extensive experiments show that by directly operating on vector floorplans, we outperform image-based methods in both mIoU and mAcc. In addition, we propose a new metric that captures room integrity and boundary regularity, which confirms that our method produces much more regular segmentations. Source code is available at https://***/DrZiji/VecFloorSeg.

关键词： vision + graphics

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VQACL: A Novel Visual Question Answering Continual Learning Setting

VQACL: A Novel Visual Question Answering Continual Learning ...

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

作者： Zhang, Xi Zhang, Feifei Xu, Changsheng Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Tianjin Univ Technol Sch Comp Sci & Engn Tianjin Peoples R China

ISBN: (纸本)9798350301298

Research on continual learning has recently led to a variety of work in unimodal community, however little attention has been paid to multimodal tasks like visual question answering (VQA). In this paper, we establish a novel VQA Continual Learning setting named VQACL, which contains two key components: a dual-level task sequence where visual and linguistic data are nested, and a novel composition testing containing new skill-concept combinations. The former devotes to simulating the ever-changing multimodal datastream in real world and the latter aims at measuring models' generalizability for cognitive reasoning. Based on our VQACL, we perform in-depth evaluations of five well-established continual learning methods, and observe that they suffer from catastrophic forgetting and have weak generalizability. To address above issues, we propose a novel representation learning method, which leverages a sample-specific and a sample-invariant feature to learn representations that are both discriminative and generalizable for VQA. Furthermore, by respectively extracting such representation for visual and textual input, our method can explicitly disentangle the skill and concept. Extensive experimental results illustrate that our method significantly outperforms existing models, demonstrating the effectiveness and compositionality of the proposed approach. The code is available at https://***/zhangxi1997/VQACL.

关键词： language reasoning vision

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Effective Ambiguity Attack Against Passport-based DNN Intellectual Property Protection Schemes through Fully Connected Layer Substitution

Effective Ambiguity Attack Against Passport-based DNN Intell...

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

作者： Chen, Yiming Tian, Jinyu Chen, Xiangyu Zhou, Jiantao Univ Macau State Key Lab Internet Things Smart City Dept Comp & Informat Sci Taipa Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Since training a deep neural network (DNN) is costly, the well-trained deep models can be regarded as valuable intellectual property (IP) assets. The IP protection associated with deep models has been receiving increasing attentions in recent years. Passport-based method, which replaces normalization layers with passport layers, has been one of the few protection solutions that are claimed to be secure against advanced attacks. In this work, we tackle the issue of evaluating the security of passport-based IP protection methods. We propose a novel and effective ambiguity attack against passport-based method, capable of successfully forging multiple valid passports with a small training dataset. This is accomplished by inserting a specially designed accessory block ahead of the passport parameters. Using less than 10% of training data, with the forged passport, the model exhibits almost indistinguishable performance difference (less than 2%) compared with that of the authorized passport. In addition, it is shown that our attack strategy can be readily generalized to attack other IP protection methods based on watermark embedding. Directions for potential remedy solutions are also given.

关键词： accountability ethics in vision fairness privacy Transparency

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Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style

Zero-Shot Everything Sketch-Based Image Retrieval, and in Ex...

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

作者： Lin, Fengyin Li, Mingkang Li, Da Hospedales, Timothy Song, Yi-Zhe Qi, Yonggang Beijing Univ Posts & Telecommun Beijing Peoples R China Samsung AI Ctr Cambridge England Univ Edinburgh Edinburgh Midlothian Scotland Univ Surrey SketchX CVSSP Guildford Surrey England

ISBN: (纸本)9798350301298

This paper studies the problem of zero-short sketch-based image retrieval (ZS-SBIR), however with two significant differentiators to prior art (i) we tackle all variants (inter-category, intra-category, and cross datasets) of ZS-SBIR with just one network ("everything"), and (ii) we would really like to understand how this sketch-photo matching operates ("explainable"). Our key innovation lies with the realization that such a cross-modal matching problem could be reduced to comparisons of groups of key local patches - akin to the seasoned "bag-of-words" paradigm. Just with this change, we are able to achieve both of the aforementioned goals, with the added benefit of no longer requiring external semantic knowledge. Technically, ours is a transformer-based cross-modal network, with three novel components (i) a self-attention module with a learnable tokenizer to produce visual tokens that correspond to the most informative local regions, (ii) a cross-attention module to compute local correspondences between the visual tokens across two modalities, and finally (iii) a kernel-based relation network to assemble local putative matches and produce an overall similarity metric for a sketch-photo pair. Experiments show ours indeed delivers superior performances across all ZS-SBIR settings. The all important explainable goal is elegantly achieved by visualizing cross-modal token correspondences, and for the first time, via sketch to photo synthesis by universal replacement of all matched photo patches. Code and model are available at https://***/buptLinfy/ZSE-SBIR.

关键词： detection recognition: Categorization retrieval

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NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations

NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations

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

作者： Hsu, Joy Mao, Jiayuan Wu, Jiajun Stanford Univ Stanford CA 94305 USA MIT Cambridge MA USA

ISBN: (纸本)9798350301298

Grounding object properties and relations in 3D scenes is a prerequisite for a wide range of artificial intelligence tasks, such as visually grounded dialogues and embodied manipulation. However, the variability of the 3D domain induces two fundamental challenges: 1) the expense of labeling and 2) the complexity of 3D grounded language. Hence, essential desiderata for models are to be data-efficient, generalize to different data distributions and tasks with unseen semantic forms, as well as ground complex language semantics (e.g., view-point anchoring and multi-object reference). To address these challenges, we propose NS3D, a neuro-symbolic framework for 3D grounding. NS3D translates language into programs with hierarchical structures by leveraging large language-to-code models. Different functional modules in the programs are implemented as neural networks. Notably, NS3D extends prior neuro-symbolic visual reasoning methods by introducing functional modules that effectively reason about high-arity relations (i.e., relations among more than two objects), key in disambiguating objects in complex 3D scenes. Modular and compositional architecture enables NS3D to achieve state-of-the-art results on the ReferIt3D view-dependence task, a 3D referring expression comprehension benchmark. Importantly, NS3D shows significantly improved performance on settings of data-efficiency and generalization, and demonstrate zero-shot transfer to an unseen 3D question-answering task.

关键词： language reasoning vision

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