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IIRC: Incremental Implicitly-Refined Classification

IIRC: Incremental Implicitly-Refined Classification

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

作者： Abdelsalam, Mohamed Faramarzi, Mojtaba Sodhani, Shagun Chandar, Sarath Mila Quebec AI Inst Montreal PQ Canada Univ Montreal Montreal PQ Canada Facebook AI Res Menlo Pk CA USA Ecole Polytech Montreal Montreal PQ Canada Canada CIFAR AI Chair Toronto ON Canada

ISBN: (纸本)9781665445092

We introduce the "Incremental Implicitly-Refined Classification (IIRC)" setup, an extension to the class incremental learning setup where the incoming batches of classes have two granularity levels. i.e., each sample could have a high-level (coarse) label like "bear" and a low-level (fine) label like "polar bear". Only one label is provided at a time, and the model has to figure out the other label if it has already learned it. This setup is more aligned with real-life scenarios, where a learner usually interacts with the same family of entities multiple times, discovers more granularity about them, while still trying not to forget previous knowledge. Moreover, this setup enables evaluating models for some important lifelong learning challenges that cannot be easily addressed under the existing setups. These challenges can be motivated by the example "if a model was trained on the class bear in one task and on polar bear in another task, will it forget the concept of bear, will it rightfully infer that a polar bear is still a bear? and will it wrongfully associate the label of polar bear to other breeds of bear?". We develop a standardized benchmark that enables evaluating models on the IIRC setup. We evaluate several stateof-the-art lifelong learning algorithms and highlight their strengths and limitations. For example, distillation-based methods perform relatively well but are prone to incorrectly predicting too many labels per image. We hope that the proposed setup, along with the benchmark, would provide a meaningful problem setting to the practitioners.

关键词： computer vision Analytical models Computational modeling Benchmark testing Prediction algorithms pattern recognition Classification algorithms

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Room-and-Object Aware Knowledge Reasoning for Remote Embodied Referring Expression

Room-and-Object Aware Knowledge Reasoning for Remote Embodie...

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

作者： Gao, Chen Chen, Jinyu Liu, Si Wang, Luting Zhang, Qiong Wu, Qi Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Univ Adelaide Adelaide SA Australia Xiaomi Inc Xiaomi AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

The Remote Embodied Referring Expression (REVERIE) is a recently raised task that requires an agent to navigate to and localise a referred remote object according to a high-level language instruction. Different from related VLN tasks, the key to REVERIE is to conduct goal-oriented exploration instead of strict instruction-following, due to the lack of step-by-step navigation guidance. In this paper, we propose a novel Cross-modality Knowledge Reasoning (CKR) model to address the unique challenges of this task. The CKR, based on a transformer-architecture, learns to generate scene memory tokens and utilise these informative history clues for exploration. Particularly, a Room-and-Object Aware Attention (ROAA) mechanism is devised to explicitly perceive the room- and object-type information from both linguistic and visual observations. Moreover, through incorporating commonsense knowledge, we propose a Knowledge-enabled Entity Relationship Reasoning (KERR) module to learn the internal-external correlations among room- and object-entities for agent to make proper action at each viewpoint. Evaluation on REVERIE benchmark demonstrates the superiority of the CKR model, which significantly boosts SPL and REVERIE-success rate by 64.67% and 46.05%, respectively. Code is available at: https://***/alloldman/CKR.

关键词： Visualization Correlation Navigation Linguistics Transformers Cognition pattern recognition

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HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data

HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual I...

引用

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

作者： Yu, Qifan Li, Juncheng Wei, Longhui Pang, Liang Ye, Wentao Qin, Bosheng Tang, Siliang Tian, Qi Zhuang, Yueting Zhejiang Univ Hangzhou Peoples R China Huawei Cloud Suzhou Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350353006

Multi-modal Large Language Models (MLLMs) tuned on machine-generated instruction-following data have demonstrated remarkable performance in various multi-modal understanding and generation tasks. However, the hallucinations inherent in machine-generated data, which could lead to hallucinatory outputs in MLLMs, remain under-explored. This work aims to investigate various hallucinations (i.e., object, relation, attribute hallucinations) and mitigate those hallucinatory toxicities in large-scale machine-generated visual instruction datasets. Drawing on the human ability to identify factual errors, we present a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm. We use our framework to identify and eliminate hallucinations in the training data automatically. Interestingly, HalluciDoctor also indicates that spurious correlations arising from long-tail object cooccurrences contribute to hallucinations. Based on that, we execute counterfactual visual instruction expansion to balance data distribution, thereby enhancing MLLMs' resistance to hallucinations. Comprehensive experiments on hallucination evaluation benchmarks show that our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA. The data and code for this paper are publicly available.(1)

关键词： Hallucinations Multi-modal Language Model vision-language reasoning

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Space-Time Distillation for Video Super-Resolution

Space-Time Distillation for Video Super-Resolution

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

作者： Xiao, Zeyu Fu, Xueyang Huang, Jie Cheng, Zhen Xiong, Zhiwei Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9781665445092

Compact video super-resolution (VSR) networks can be easily deployed on resource-limited devices, e.g., smartphones and wearable devices, but have considerable performance gaps compared with complicated VSR networks that require a large amount of computing resources. In this paper, we aim to improve the performance of compact VSR networks without changing their original architectures, through a knowledge distillation approach that transfers knowledge from a complicated VSR network to a compact one. Specifically, we propose a space-time distillation (STD) scheme to exploit both spatial and temporal knowledge in the VSR task. For space distillation, we extract spatial attention maps that hint the high-frequency video content from both networks, which are further used for transferring spatial modeling capabilities. For time distillation, we narrow the performance gap between compact models and complicated models by distilling the feature similarity of the temporal memory cells, which are encoded from the sequence of feature maps generated in the training clips using ConvLSTM. During the training process, STD can be easily incorporated into any network without changing the original network architecture. Experimental results on standard benchmarks demonstrate that, in resource-constrained situations, the proposed method notably improves the performance of existing VSR networks without increasing the inference time.

关键词： Training Performance evaluation Knowledge engineering Wearable computers Superresolution Network architecture pattern recognition

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Generalising Fine-Grained Sketch-Based Image Retrieval 32

Generalising Fine-Grained Sketch-Based Image Retrieval

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Pang, Kaiyue Li, Ke Yang, Yongxin Zhang, Honggang Hospedales, Timothy M. Xiang, Tao Song, Yi-Zhe Univ Surrey SketchX CVSSP Guildford Surrey England Queen Mary Univ London London England Beijing Univ Posts & Telecommun Beijing Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9781728132938

Fine-grained sketch-based image retrieval (FG-SBIR) addresses matching specific photo instance using free-hand sketch as a query modality. Existing models aim to learn an embedding space in which sketch and photo can be directly compared. While successful, they require instancelevel pairing within each coarse-grained category as annotated training data. Since the learned embedding space is domain-specific, these models do not generalise well across categories. This limits the practical applicability of FG-SBIR. In this paper, we identify cross-category generalisation for FG-SBIR as a domain generalisation problem, and propose the first solution. Our key contribution is a novel unsupervised learning approach to model a universal manifold of prototypical visual sketch traits. This manifold can then be used to paramaterise the learning of a sketch/photo representation. Model adaptation to novel categories then becomes automatic via embedding the novel sketch in the manifold and updating the representation and retrieval function accordingly. Experiments on the two largest FG-SBIR datasets, Sketchy and QMUL-Shoe-V2, demonstrate the efficacy of our approach in enabling cross-category generalisation of FG-SBIR.

关键词： Big Data Categorization Deep Learning Large Scale Methods recognition: Detection Retrieval vision Applications and Systems

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Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera 32

Bringing a Blurry Frame Alive at High Frame-Rate with an Eve...

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Pan, Liyuan Scheerlinck, Cedric Yu, Xin Hartley, Richard Liu, Miaomiao Dai, Yuchao Australian Natl Univ Canberra ACT Australia Australian Ctr Robot Vis Canberra ACT Australia Northwestern Polytech Univ Sch Elect & Informat Xian Shaanxi Peoples R China

ISBN: (纸本)9781728132938

Event-based cameras can measure intensity changes (called 'events') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the active pixel sensor (APS), the event camera allows simultaneous output of the intensity frames. However, the output images are captured at a relatively low frame-rate and often suffer from motion blur. A blurry image can be regarded as the integral of a sequence of latent images, while the events indicate the changes between the latent images. Therefore, we are able to model the blur-generation process by associating event data to a latent image. In this paper, we propose a simple and effective approach, the Event-based Double Integral (EDI) model, to reconstruct a high frame-rate, sharp video from a single blurry frame and its event data. The video generation is based on solving a simple non-convex optimization problem in a single scalar variable. Experimental results on both synthetic and real images demonstrate the superiority of our EDI model and optimization method in comparison to the state-of-the-art.

关键词： Computational Photography Image and Video Synthesis vision Applications and Systems

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vision-Dialog Navigation by Exploring Cross-modal Memory

Vision-Dialog Navigation by Exploring Cross-modal Memory

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

作者： Zhu, Yi Zhu, Fengda Zhan, Zhaohuan Lin, Bingqian Jiao, Jianbin Chang, Xiaojun Liang, Xiaodan Univ Chinese Acad Sci Beijing Peoples R China Monash Univ Melbourne Vic Australia Sun Yat Sen Univ Guangzhou Peoples R China Dark Matter AI Inc Guangzhou Peoples R China

ISBN: (纸本)9781728171685

vision-dialog navigation posed as a new holy-grail task in vision-language disciplinary targets at learning an agent endowed with the capability of constant conversation for help with natural language and navigating according to human responses. Besides the common challenges faced in visual language navigation, vision-dialog navigation also requires to handle well with the language intentions of a series of questions about the temporal context from dialogue history and co-reasoning both dialogs and visual scenes. In this paper, we propose the Cross-modal Memory Network (CMN) for remembering and understanding the rich information relevant to historical navigation actions. Our CMN consists of two memory modules, the language memory module (L-mem) and the visual memory module (V-mem). Specifically, L-mem learns latent relationships between the current language interaction and a dialog history by employing a multi-head attention mechanism. V-mem learns to associate the current visual views and the cross-modal memory about the previous navigation actions. The cross-modal memory is generated via a vision-to-language attention and a language-to-vision attention. Benefiting from the collaborative learning of the L-mem and the V-mem, our CMN is able to explore the memory about the decision making of historical navigation actions which is for the current step. Experiments on the CVDN dataset show that our CMN outperforms the previous state-of-the-art model by a significant margin on both seen and unseen environments.

关键词： Navigation

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Large Loss Matters in Weakly Supervised Multi-Label Classification

Large Loss Matters in Weakly Supervised Multi-Label Classifi...

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

作者： Kim, Youngwook Kim, Jae Myung Akata, Zeynep Lee, Jungwoo Seoul Natl Univ Seoul South Korea Univ Tubingen Tubingen Germany Max Planck Inst Intelligent Syst Stuttgart Germany Max Planck Inst Informat Saarbrucken Germany HodooAI Lab Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Weakly supervised multi-label classification (WSML) task, which is to learn a multi-label classification using partially observed labels per image, is becoming increasingly important due to its huge annotation cost. In this work, we first regard unobserved labels as negative labels, casting the WSML task into noisy multi-label classification. From this point of view, we empirically observe that memorization effect, which was first discovered in a noisy multi-class setting, also occurs in a multi-label setting. That is, the model first learns the representation of clean labels, and then starts memorizing noisy labels. Based on this finding, we propose novel methods for WSML which reject or correct the large loss samples to prevent model from memorizing the noisy label. Without heavy and complex components, our proposed methods outperform previous state-of-the-art WSML methods on several partial label settings including Pascal VOC 2012, MS COCO, NUSWIDE, CUB, and OpenImages V3 datasets. Various analysis also show that our methodology actually works well, validating that treating large loss properly matters in a weakly supervised multi-label classification.

关键词： Training computer vision Costs Codes Annotations Computational modeling pattern recognition

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Rank-One Prior: Toward Real-Time Scene Recovery

Rank-One Prior: Toward Real-Time Scene Recovery

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

作者： Liu, Jun Liu, Ryan Wen Sun, Jianing Zeng, Tieyong Northeast Normal Univ Sch Math & Stat Changchun Peoples R China Northeast Normal Univ Key Lab Appl Stat MOE Changchun Peoples R China Wuhan Univ Technol Sch Nav Wuhan Peoples R China Northeast Normal Univ Jilin Natl Appl Math Ctr Changchun Peoples R China Chinese Univ Hong Kong Dept Math Shatin Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Scene recovery is a fundamental imaging task for several practical applications, e.g., video surveillance and autonomous vehicles, etc. To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwater, and haze. The heart of our work is that we propose an intensity projection strategy to estimate the transmission. This strategy is motivated by a straightforward rank-one transmission prior. The complexity of transmission estimation is O(N) where N is the size of the single image. Then we can recover the scene in real-time. Comprehensive experiments on different types of weather/imaging conditions illustrate that our method outperforms competitively several state-of-the-art imaging methods in terms of efficiency and robustness.

关键词： Learning systems computer vision Visualization Imaging Estimation Training data Video surveillance

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Efficient Featurized Image Pyramid Network for Single Shot Detector 32

Efficient Featurized Image Pyramid Network for Single Shot D...

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Pang, Yanwei Wang, Tiancai Anwer, Rao Muhammad Khan, Fahad Shahbaz Shao, Ling Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Linkoping Univ Dept Elect Engn Comp Vis Lab Linkoping Sweden

ISBN: (纸本)9781728132938

Single-stage object detectors have recently gained popularity due to their combined advantage of high detection accuracy and real-time speed. However, while promising results have been achieved by these detectors on standard-sized objects, their performance on small objects is far from satisfactory. To detect very small/large objects, classical pyramid representation can be exploited, where an image pyramid is used to build afeature pyramid (featurized image pyramid), enabling detection across a range of scales. Existing single-stage detectors avoid such afeaturized image pyramid representation due to its memory and time complexity. In this paper we introduce a light-weight architecture to efficiently produce featurized image pyramid in a single-stage detection framework. The resulting multi-scale features are then injected into the prediction layers of the detector using an attention module. The performance of our detector is validated on two benchmarks: PASCAL VOC and MS COCO. For a 300 x 300 input, our detector operates at 111 frames per second (FPS) on a Titan X GPU, providing state-of-the-art detection accuracy on PASCAL VOC 2007 testset. On the MS COCO testset, our detector achieves state-of-the-art results surpassing all existing single-stage methods in the case of single-scale inference.

关键词： Categorization recognition: Detection Retrieval Scene Analysis and Understanding

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