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Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning

Out-of-Distributed Semantic Pruning for Robust Semi-Supervis...

引用

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

作者： Wang, Yu Qiao, Pengchong Liu, Chang Song, Guoli Zheng, Xiawu Chen, Jie Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China Peking Univ AI Sci AI4S Preferred Program Shenzhen Grad Sch Beijing Peoples R China

ISBN: (纸本)9798350301298

Recent advances in robust semi-supervised learning (SSL) typically filter out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initial step to explore and propose a unified framework termed OOD Semantic Pruning (OSP), which aims at pruning OOD semantics out from in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD matching module to pair each ID sample with an OOD sample with semantic overlap. (ii) We design a soft orthogonality regularization, which first transforms each ID feature by suppressing its semantic component that is collinear with paired OOD sample. It then forces the predictions before and after soft orthogonality decomposition to be consistent. Being practically simple, our method shows a strong performance in OOD detection and ID classification on challenging benchmarks. In particular, OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset. The source codes are publicly available at https://***/rain305f/OSP.

关键词： detection recognition: Categorization retrieval

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Event-based Shape from Polarization

Event-based Shape from Polarization

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

作者： Muglikar, Manasi Bauersfeld, Leonard Moeys, Diederik Paul Scaramuzza, Davide Univ Zurich Robot & Percept Grp Zurich Switzerland SONY R&D Ctr Europe Adv Sensors & Modelling Grp SL1 Terre Haute IN USA

ISBN: (纸本)9798350301298

State-of-the-art solutions for Shape-from-Polarization (SfP) suffer from a speed-resolution tradeoff: they either sacrifice the number of polarization angles measured or necessitate lengthy acquisition times due to framerate constraints, thus compromising either accuracy or latency. We tackle this tradeoff using event cameras. Event cameras operate at microseconds resolution with negligible motion blur, and output a continuous stream of events that precisely measures how light changes over time asynchronously. We propose a setup that consists of a linear polarizer rotating at high speeds in front of an event camera. Our method uses the continuous event stream caused by the rotation to reconstruct relative intensities at multiple polarizer angles. Experiments demonstrate that our method outperforms physics-based baselines using frames, reducing the MAE by 25% in synthetic and real-world datasets. In the real world, we observe, however, that the challenging conditions (i.e., when few events are generated) harm the performance of physics-based solutions. To overcome this, we propose a learning-based approach that learns to estimate surface normals even at low event-rates, improving the physics-based approach by 52% on the real world dataset. The proposed system achieves an acquisition speed equivalent to 50 fps (>twice the framerate of the commercial polarization sensor) while retaining the spatial resolution of 1MP. Our evaluation is based on the first large-scale dataset for event-based SfP.

关键词： Physics-based vision and shape-from-X

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Hard Patches Mining for Masked Image Modeling

Hard Patches Mining for Masked Image Modeling

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

作者： Wang, Haochen Song, Kaiyou Fan, Junsong Wang, Yuxi Xie, Jin Zhang, Zhaoxiang Chinese Acad Sci Inst Automat Ctr Res Intelligent Percept & Comp Natl Lab Pattern Recognit Beijing Peoples R China Megvii Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Masked image modeling (MIM) has attracted much research attention due to its promising potential for learning scalable visual representations. In typical approaches, models usually focus on predicting specific contents of masked patches, and their performances are highly related to pre-defined mask strategies. Intuitively, this procedure can be considered as training a student (the model) on solving given problems (predict masked patches). However, we argue that the model should not only focus on solving given problems, but also stand in the shoes of a teacher to produce a more challenging problem by itself. To this end, we propose Hard Patches Mining (HPM), a brand-new framework for MIM pre-training. We observe that the reconstruction loss can naturally be the metric of the difficulty of the pre-training task. Therefore, we introduce an auxiliary loss predictor, predicting patch-wise losses first and deciding where to mask next. It adopts a relative relationship learning strategy to prevent overfitting to exact reconstruction loss values. Experiments under various settings demonstrate the effectiveness of HPM in constructing masked images. Furthermore, we empirically find that solely introducing the loss prediction objective leads to powerful representations, verifying the efficacy of the ability to be aware of where is hard to reconstruct.(1)

关键词： Self-supervised or unsupervised representation learning

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Learning Situation Hyper-Graphs for Video Question Answering

Learning Situation Hyper-Graphs for Video Question Answering

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

作者： Khan, Aisha Urooj Kuehne, Hilde Wu, Bo Chheu, Kim Bousselham, Walid Gan, Chuang Lobo, Niels Shah, Mubarak Univ Cent Florida CRCV Orlando FL 32816 USA MIT IBM Watson AI Lab Cambridge MA USA Mayo Clin Phoenix AZ 85054 USA Goethe Univ Frankfurt Germany Frankfurt Germany Western Michigan Univ Kalamazoo MI USA UMass Amherst Amherst MA USA

ISBN: (纸本)9798350301298

Answering questions about complex situations in videos requires not only capturing the presence of actors, objects, and their relations but also the evolution of these relationships over time. A situation hyper-graph is a representation that describes situations as scene sub-graphs for video frames and hyper-edges for connected sub-graphs and has been proposed to capture all such information in a compact structured form. In this work, we propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs, coined Situation Hyper-Graph based Video Question Answering (SHG-VQA). To this end, we train a situation hyper-graph decoder to implicitly identify graph representations with actions and object/human-object relationships from the input video clip. and to use cross-attention between the predicted situation hyper-graphs and the question embedding to predict the correct answer. The proposed method is trained in an end-to-end manner and optimized by a VQA loss with the cross-entropy function and a Hungarian matching loss for the situation graph prediction. The effectiveness of the proposed architecture is extensively evaluated on two challenging benchmarks: AGQA and STAR. Our results show that learning the underlying situation hypergraphs helps the system to significantly improve its performance for novel challenges of video question-answering tasks.

关键词： and reasoning language vision

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SmartAssign: Learning A Smart Knowledge Assignment Strategy for Deraining and Desnowing

SmartAssign: Learning A Smart Knowledge Assignment Strategy ...

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

作者： Wang, Yinglong Mai, Chao Liu, Jianzhuang Huawei Noahs Ark Lab Montreal PQ Canada Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9798350301298

Existing methods mainly handle single weather types. However, the connections of different weather conditions at deep representation level are usually ignored. These connections, if used properly, can generate complementary representations for each other to make up insufficient training data, obtaining positive performance gains and better generalization. In this paper, we focus on the very correlated rain and snow to explore their connections at deep representation level. Because sub-optimal connections may cause negative effect, another issue is that if rain and snow are handled in a multi-task learning way, how to find an optimal connection strategy to simultaneously improve deraining and desnowing performance. To build desired connection, we propose a smart knowledge assignment strategy, called SmartAssign, to optimally assign the knowledge learned from both tasks to a specific one. In order to further enhance the accuracy of knowledge assignment, we propose a novel knowledge contrast mechanism, so that the knowledge assigned to different tasks preserves better uniqueness. The inherited inductive biases usually limit the modelling ability of CNNs, we introduce a novel transformer block to constitute the backbone of our network to effectively combine long-range context dependency and local image details. Extensive experiments on seven benchmark datasets verify that proposed SmartAssign explores effective connection between rain and snow, and improves the performances of both deraining and desnowing apparently. The implementation code will be available at https:// ***/ mindspore/models/tree/master/ research/cv/SmartAssign.

关键词： Low-level vision

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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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3D-Aware Object Goal Navigation via Simultaneous Exploration and Identification

3D-Aware Object Goal Navigation via Simultaneous Exploration...

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

作者： Zhang, Jiazhao Dai, Liu Meng, Fanpeng Fan, Qingnan Chen, Xuelin Xu, Kai Wang, He Peking Univ CFCS Beijing Peoples R China Beijing Acad Artificial Intelligence Beijing Peoples R China Tongji Univ CEIE Shanghai Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Tencent AI Lab Shenzhen Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9798350301298

Object goal navigation (ObjectNav) in unseen environments is a fundamental task for Embodied AI. Agents in existing works learn ObjectNav policies based on 2D maps, scene graphs, or image sequences. Considering this task happens in 3D space, a 3D-aware agent can advance its ObjectNav capability via learning from fine-grained spatial information. However, leveraging 3D scene representation can be prohibitively unpractical for policy learning in this floor-level task, due to low sample efficiency and expensive computational cost. In this work, we propose a framework for the challenging 3D-aware ObjectNav based on two straightforward sub-policies. The two sub-polices, namely corner-guided exploration policy and category-aware identification policy, simultaneously perform by utilizing online fused 3D points as observation. Through extensive experiments, we show that this framework can dramatically improve the performance in ObjectNav through learning from 3D scene representation. Our framework achieves the best performance among all modular-based methods on the Matterport3D and Gibson datasets, while requiring (up to 30x) less computational cost for training. The code will be released to benefit the community.

关键词： Embodied vision: Active agents simulation

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From Images to Textual Prompts: Zero-shot Visual Question Answering with Frozen Large Language Models

From Images to Textual Prompts: Zero-shot Visual Question An...

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

作者： Guo, Jiaxian Li, Junnan Li, Dongxu Tiong, Anthony Meng Huat Li, Boyang Tao, Dacheng Hoi, Steven Univ Sydney Sydney NSW Australia Salesforce Res San Francisco CA USA Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350301298

Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnect and task disconnect between the LLM and VQA tasks. End-to-end training on multimodal data may bridge the disconnects, but is inflexible and computationally expensive. To address this issue, we propose Img2LLM, a plug-and-play module that provides LLM prompts to enable LLMs to perform zero-shot VQA tasks without end-to-end training. We develop LLM-agnostic models describe image content as exemplar question-answer pairs, which prove to be effective LLM prompts. Img2LLM offers the following benefits: 1) It achieves comparable or better performance than methods relying on end-to-end training. For example, we outperform Flamingo [3] by 5.6% on VQAv2. On the challenging A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. 3) It eliminates the need to specialize LLMs using end-to-end finetuning and serve highly specialized LLMs to end users, thereby reducing cost. Code is available via the LAVIS [28] framework at https://***/salesforce/LAVIS/ tree/main/projects/img2llm-vqa.

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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VEATIC: Video-based Emotion and Affect Tracking in Context Dataset

VEATIC: Video-based Emotion and Affect Tracking in Context D...

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

作者： Ren, Zhihang Ortega, Jefferson Wang, Yifan Chen, Zhimin Guo, Yunhui Yu, Stella X. Whitney, David Univ Calif Berkeley Berkeley CA 94720 USA Univ Texas Dallas Dallas TX USA Univ Michigan Ann Arbor MI USA

ISBN: (纸本)9798350318920;9798350318937

psychophysics and computer vision. However, the currently published datasets have many limitations. For example, most datasets contain frames that contain only information about facial expressions. Due to the limitations of previous datasets, it is very hard to either understand the mechanisms for affect recognition of humans or generalize well on common cases for computer vision models trained on those datasets. In this work, we introduce a brand new large dataset, the Video-based Emotion and Affect Tracking in Context Dataset (VEATIC), that can conquer the limitations of the previous datasets. VEATIC has 124 video clips from Hollywood movies, documentaries, and home videos with continuous valence and arousal ratings of each frame via real-time annotation. Along with the dataset, we propose a new computer vision task to infer the affect of the selected character via both context and character information in each video frame. Additionally, we propose a simple model to benchmark this new computer vision task. We also compare the performance of the pretrained model using our dataset with other similar datasets. Experiments show the competing results of our pretrained model via VEATIC, indicating the generalizability of VEATIC. Our dataset is available at https://***.

关键词： Algorithms Algorithms Algorithms Datasets and evaluations Image recognition and understanding Video recognition and understanding

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