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Two-way Multi-Label Loss

Two-way Multi-Label Loss

引用

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

作者： Kobayashi, Takumi Natl Inst Adv Ind Sci & Technol Tokyo Japan Univ Tsukuba Tsukuba Japan

ISBN: (纸本)9798350301298

A natural image frequently contains multiple classification targets, accordingly providing multiple class labels rather than a single label per image. While the single-label classification is effectively addressed by applying a softmax cross-entropy loss, the multi-label task is tackled mainly in a binary cross-entropy (BCE) framework. In contrast to the softmax loss, the BCE loss involves issues regarding imbalance as multiple classes are decomposed into a bunch of binary classifications;recent works improve the BCE loss to cope with the issue by means of weighting. In this paper, we propose a multi-label loss by bridging a gap between the softmax loss and the multi-label scenario. The proposed loss function is formulated on the basis of relative comparison among classes which also enables us to further improve discriminative power of features by enhancing classification margin. The loss function is so flexible as to be applicable to a multi-label setting in two ways for discriminating classes as well as samples. In the experiments on multi-label classification, the proposed method exhibits competitive performance to the other multi-label losses, and it also provides transferrable features on single-label ImageNet training. Codes are available at https: //***/tk1980/TwowayMultiLabelLoss.

关键词： detection recognition: Categorization retrieval

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GIVL: Improving Geographical Inclusivity of vision-Language Models with Pre-Training Methods

GIVL: Improving Geographical Inclusivity of Vision-Language ...

引用

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

作者： Yin, Da Gao, Feng Thattai, Govind Johnston, Michael Chang, Kai -Wei Univ Calif Los Angeles Los Angeles CA 90095 USA Amazon Alexa AI Lexington MA USA

ISBN: (纸本)9798350301298

A key goal for the advancement of AI is to develop technologies that serve the needs not just of one group but of all communities regardless of their geographical region. In fact, a significant proportion of knowledge is locally shared by people from certain regions but may not apply equally in other regions because of cultural differences. If a model is unaware of regional characteristics, it may lead to performance disparity across regions and result in bias against underrepresented groups. We propose GIVL, a Geographically Inclusive vision-and-Language Pre-trained model. There are two attributes of geo-diverse visual concepts which can help to learn geodiverse knowledge: 1) concepts under similar categories have unique knowledge and visual characteristics, 2) concepts with similar visual features may fall in completely different categories. Motivated by the attributes, we design new pre-training objectives Image-Knowledge Matching (IKM) and Image Edit Checking (IEC) to pre-train GIVL. Compared with similar-size models pre-trained with similar scale of data, GIVL achieves state-of-the-art (SOTA) and more balanced performance on geo-diverse V&L tasks.

关键词： language reasoning vision

来源：评论

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Collecting Cross-Modal Presence-Absence Evidence for Weakly-Supervised Audio-Visual Event Perception

Collecting Cross-Modal Presence-Absence Evidence for Weakly-...

引用

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

作者： Gao, Junyu Chen, Mengyuan Xu, Changsheng Chinese Acad Sci CASIA 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

ISBN: (纸本)9798350301298

With only video-level event labels, this paper targets at the task of weakly-supervised audio-visual event perception (WS-AVEP), which aims to temporally localize and categorize events belonging to each modality. Despite the recent progress, most existing approaches either ignore the unsynchronized property of audio-visual tracks or discount the complementary modality for explicit enhancement. We argue that, for an event residing in one modality, the modality itself should provide ample presence evidence of this event, while the other complementary modality is encouraged to afford the absence evidence as a reference signal. To this end, we propose to collect Cross-Modal Presence-Absence Evidence (CMPAE) in a unified framework. Specifically, by leveraging uni-modal and cross-modal representations, a presence-absence evidence collector (PAEC) is designed under Subjective Logic theory. To learn the evidence in a reliable range, we propose a joint-modal mutual learning (JML) process, which calibrates the evidence of diverse audible, visible, and audi-visible events adaptively and dynamically. Extensive experiments show that our method surpasses state-of-the-arts (e.g., absolute gains of 3.6% and 6.1% in terms of event-level visual and audio metrics). Code is available in ***/MengyuanChen21/cvpr2023-CMPAE.

关键词： 2023 ieee/CVF conference on computer vision and pattern recognition (cvpr)

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Rethinking Feature-based Knowledge Distillation for Face recognition

Rethinking Feature-based Knowledge Distillation for Face Rec...

引用

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

作者： Li, Jingzhi Guo, Zidong Li, Hui Han, Seungju Baek, Ji-won Yang, Min Yang, Ran Suh, Sungjoo Samsung R&D Inst China Xian SRCX Xian Peoples R China Samsung Adv Inst Technol SAIT Suwon South Korea

ISBN: (纸本)9798350301298

With the continual expansion of face datasets, feature-based distillation prevails for large-scale face recognition. In this work, we attempt to remove identity supervision in student training, to spare the GPU memory from saving massive class centers. However, this naive removal leads to inferior distillation result. We carefully inspect the performance degradation from the perspective of intrinsic dimension, and argue that the gap in intrinsic dimension, namely the intrinsic gap, is intimately connected to the infamous capacity gap problem. By constraining the teacher's search space with reverse distillation, we narrow the intrinsic gap and unleash the potential of feature-only distillation. Remarkably, the proposed reverse distillation creates universally student-friendly teacher that demonstrates outstanding student improvement. We further enhance its effectiveness by designing a student proxy to better bridge the intrinsic gap. As a result, the proposed method surpasses state-of-the-art distillation techniques with identity supervision on various face recognition benchmarks, and the improvements are consistent across different teacher-student pairs.

关键词： body gesture Humans: Face movement pose

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Multi-modal Gait recognition via Effective Spatial-Temporal Feature Fusion

Multi-modal Gait Recognition via Effective Spatial-Temporal ...

引用

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

作者： Cui, Yufeng Kang, Yimei Beihang Univ Coll Software Beijing Peoples R China

ISBN: (纸本)9798350301298

Gait recognition is a biometric technology that identifies people by their walking patterns. The silhouettes-based method and the skeletons-based method are the two most popular approaches. However, the silhouette data are easily affected by clothing occlusion, and the skeleton data lack body shape information. To obtain a more robust and comprehensive gait representation for recognition, we propose a transformer-based gait recognition framework called MMGaitFormer, which effectively fuses and aggregates the spatial-temporal information from the skeletons and silhouettes. Specifically, a Spatial Fusion Module (SFM) and a Temporal Fusion Module (TFM) are proposed for effective spatial-level and temporal-level feature fusion, respectively. The SFM performs fine-grained body parts spatial fusion and guides the alignment of each part of the silhouette and each joint of the skeleton through the attention mechanism. The TFM performs temporal modeling through Cycle Position Embedding (CPE) and fuses temporal information of two modalities. Experiments demonstrate that our MMGaitFormer achieves state-of-the-art performance on popular gait datasets. For the most challenging "CL" (i.e., walking in different clothes) condition in CASIAB, our method achieves a rank-1 accuracy of 94.8%, which outperforms the state-of-the-art single-modal methods by a large margin.

关键词： body gesture Humans: Face movement pose

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Physical-World Optical Adversarial Attacks on 3D Face recognition

Physical-World Optical Adversarial Attacks on 3D Face Recogn...

引用

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

作者： Li, Yanjie Li, Yiquan Dai, Xuelong Guo, Songtao Xiao, Bin Hong Kong Polytech Univ Hong Kong Peoples R China Chongqing Univ Chongqing Peoples R China

ISBN: (纸本)9798350301298

The success rate of current adversarial attacks remains low on real-world 3D face recognition tasks because the 3D-printing attacks need to meet the requirement that the generated points should be adjacent to the surface, which limits the adversarial example' searching space. Additionally, they have not considered unpredictable head movements or the non-homogeneous nature of skin reflectance in the real world. To address the real-world challenges, we propose a novel structured-light attack against structured-light-based 3D face recognition. We incorporate the 3D reconstruction process and skin's reflectance in the optimization process to get the end-to-end attack and present 3D transform invariant loss and sensitivity maps to improve robustness. Our attack enables adversarial points to be placed in any position and is resilient to random head movements while maintaining the perturbation unnoticeable. Experiments show that our new method can attack point-cloud-based and depth-image-based 3D face recognition systems with a high success rate, using fewer perturbations than previous physical 3D adversarial attacks.

关键词： Adversarial attack and defense

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Directional Connectivity-based Segmentation of Medical Images

Directional Connectivity-based Segmentation of Medical Image...

引用

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

作者： Yang, Ziyun Farsiu, Sina Duke Univ Durham NC 27706 USA

ISBN: (纸本)9798350301298

Anatomical consistency in biomarker segmentation is crucial for many medical image analysis tasks. A promising paradigm for achieving anatomically consistent segmentation via deep networks is incorporating pixel connectivity, a basic concept in digital topology, to model inter-pixel relationships. However, previous works on connectivity modeling have ignored the rich channel-wise directional information in the latent space. In this work, we demonstrate that effective disentanglement of directional sub-space from the shared latent space can significantly enhance the feature representation in the connectivity-based network. To this end, we propose a directional connectivity modeling scheme for segmentation that decouples, tracks, and utilizes the directional information across the network. Experiments on various public medical image segmentation benchmarks show the effectiveness of our model as compared to the state-of-the-art methods. Code is available at https://***/Zyun-Y/DconnNet.

关键词： cell microscopy Medical and biological vision

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FFCV: Accelerating Training by Removing Data Bottlenecks

FFCV: Accelerating Training by Removing Data Bottlenecks

引用

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

作者： Leclerc, Guillaume Ilyas, Andrew Engstrom, Logan Park, Sung Min Salman, Hadi Madry, Aleksander MIT Cambridge MA 02139 USA

ISBN: (纸本)9798350301298

We present FFCV, a library for easy and fast machine learning model training. FFCV speeds up model training by eliminating (often subtle) data bottlenecks from the training process. In particular, we combine techniques such as an efficient file storage format, caching, data pre-loading, asynchronous data transfer, and just-in-time compilation to (a) make data loading and transfer significantly more efficient, ensuring that GPUs can reach full utilization;and (b) offload as much data processing as possible to the CPU asynchronously, freeing GPU cycles for training. Using FFCV, we train ResNet-18 and ResNet-50 on the ImageNet dataset with a state-of-the-art tradeoff between accuracy and training time. For example, across the range of ResNet-50 models we test, we obtain the same accuracy as the best baselines in half the time. We demonstrate FFCV's performance, ease-of-use, extensibility, and ability to adapt to resource constraints through several case studies. Detailed installation instructions, documentation, and Slack support channel are available at https://***/.

关键词： Efficient and scalable vision

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DELTA: Decoupling Long-Tailed Online Continual Learning

DELTA: Decoupling Long-Tailed Online Continual Learning

引用

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

作者： Raghavan, Siddeshwar He, Jiangpeng Zhu, Fengqing Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9798350365474

A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of models to rapidly learn new information in real-world scenarios where data follows long-tailed distributions, all while avoiding forgetting previously acquired knowledge. In this work, we study the under-explored problem of Long-Tailed Online Continual Learning (LTOCL), which aims to learn new tasks from sequentially arriving class-imbalanced data streams. Each data is observed only once for training without knowing the task data distribution. We present DELTA, a decoupled learning approach designed to enhance learning representations and address the substantial imbalance in LTOCL. We enhance the learning process by adapting supervised contrastive learning to attract similar samples and repel dissimilar (out-of-class) samples. Further, by balancing gradients during training using an equalization loss, DELTA significantly enhances learning outcomes and successfully mitigates catastrophic forgetting. Through extensive evaluation, we demonstrate that DELTA improves the capacity for incremental learning, surpassing existing OCL methods. Our results suggest considerable promise for applying OCL in real-world applications. Code is available online (1)

关键词： computer vision long-tailed image classification online continual learning

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Use Your Head: Improving Long-Tail Video recognition

Use Your Head: Improving Long-Tail Video Recognition

引用

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

作者： Perrett, Toby Sinha, Saptarshi Burghardt, Tilo Mirmehdi, Majid Damen, Dima Univ Bristol Bristol Avon England

ISBN: (纸本)9798350301298

This paper presents an investigation into long-tail video recognition. We demonstrate that, unlike naturally-collected video datasets and existing long-tail image benchmarks, current video benchmarks fall short on multiple long-tailed properties. Most critically, they lack few-shot classes in their tails. In response, we propose new video benchmarks that better assess long-tail recognition, by sampling subsets from two datasets: SSv2 and VideoLT. We then propose a method, Long-Tail Mixed Reconstruction (LMR), which reduces overfitting to instances from few-shot classes by reconstructing them as weighted combinations of samples from head classes. LMR then employs label mixing to learn robust decision boundaries. It achieves state-of-the-art average class accuracy on EPIC-KITCHENS and the proposed SSv2-LT and VideoLT-LT. Benchmarks and code at: ***/tobyperrett/lmr

关键词： Video: Action and event understanding

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