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Balanced Energy Regularization Loss for Out-of-distribution Detection

Balanced Energy Regularization Loss for Out-of-distribution ...

引用

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

作者： Choi, Hyunjun Jeong, Hawook Choi, Jin Young Seoul Natl Univ ASRI ECE Seoul South Korea RideFlux Inc Jeju South Korea

ISBN: (纸本)9798350301298

In the field of out-of-distribution (OOD) detection, a previous method that use auxiliary data as OOD data has shown promising performance. However, the method provides an equal loss to all auxiliary data to differentiate them from inliers. However, based on our observation, in various tasks, there is a general imbalance in the distribution of the auxiliary OOD data across classes. We propose a balanced energy regularization loss that is simple but generally effective for a variety of tasks. Our balanced energy regularization loss utilizes class-wise different prior probabilities for auxiliary data to address the class imbalance in OOD data. The main concept is to regularize auxiliary samples from majority classes, more heavily than those from minority classes. Our approach performs better for OOD detection in semantic segmentation, long-tailed image classification, and image classification than the prior energy regularization loss. Furthermore, our approach achieves state-of-the-art performance in two tasks: OOD detection in semantic segmentation and long-tailed image classification.

关键词： vision applications and systems

来源：评论

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CAGE: Circumplex Affect Guided Expression Inference

CAGE: Circumplex Affect Guided Expression Inference

引用

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

作者： Wagner, Niklas Maetzler, Felix Vossberg, Samed R. Schneider, Helen Pavlitska, Svetlana Zoellner, J. Marius Karlsruhe Inst Technol KIT Karlsruhe Germany FZI Res Ctr Informat Technol Karlsruhe Germany

ISBN: (纸本)9798350365474

Understanding emotions and expressions is a task of interest across multiple disciplines, especially for improving user experiences. Contrary to the common perception, it has been shown that emotions are not discrete entities but instead exist along a continuum. People understand discrete emotions differently due to a variety of factors, including cultural background, individual experiences, and cognitive biases. Therefore, most approaches to expression understanding, particularly those relying on discrete categories, are inherently biased. In this paper, we present a comparative in-depth analysis of two common datasets (AffectNet and EMOTIC) equipped with the components of the circumplex model of affect. Further, we propose a model for the prediction of facial expressions tailored for lightweight applications. Using a small-scaled MaxViT-based model architecture, we evaluate the impact of discrete expression category labels in training with the continuous valence and arousal labels. We show that considering valence and arousal in addition to discrete category labels helps to significantly improve expression inference. The proposed model outperforms the current state-of-the-art models on AffectNet, establishing it as the best-performing model for inferring valence and arousal achieving a 7% lower RMSE. Training scripts and trained weights to reproduce our results can be found here: https:// ***/wagner-niklas/CAGE_expression_inference.

关键词： computer vision Expression Inference Transformer

来源：评论

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Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution

Cascaded Local Implicit Transformer for Arbitrary-Scale Supe...

引用

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

作者： Chen, Hao-Wei Xu, Yu-Syuan Hong, Min-Fong Tsai, Yi-Min Kuo, Hsien-Kai Lee, Chun-Yi Natl Tsing Hua Univ Elsa Lab Hsinchu Taiwan MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9798350301298

Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions. In this paper, we present a Local Implicit Transformer (LIT), which integrates the attention mechanism and frequency encoding technique into a local implicit image function. We design a cross-scale local attention block to effectively aggregate local features and a local frequency encoding block to combine positional encoding with Fourier domain information for constructing high-resolution images. To further improve representative power, we propose a Cascaded LIT (CLIT) that exploits multi-scale features, along with a cumulative training strategy that gradually increases the upsampling scales during training. We have conducted extensive experiments to validate the effectiveness of these components and analyze various training strategies. The qualitative and quantitative results demonstrate that LIT and CLIT achieve favorable results and outperform the prior works in arbitrary super-resolution tasks.

关键词： Low-level vision

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A Simple Framework for Text-Supervised Semantic Segmentation

A Simple Framework for Text-Supervised Semantic Segmentation

引用

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

作者： Yi, Muyang Cui, Quan Wu, Hao Yang, Cheng Yoshie, Osamu Lu, Hongtao Shanghai Jiao Tong Univ AI Inst Dept Comp Sci & Engn MoE Key Lab Artificial Intelligence Shanghai Peoples R China Waseda Univ Tokyo Japan ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

Text-supervised semantic segmentation is a novel research topic that allows semantic segments to emerge with image-text contrasting. However, pioneering methods could be subject to specifically designed network architectures. This paper shows that a vanilla contrastive language-image pre-training (CLIP) model is an effective text-supervised semantic segmentor by itself. First, we reveal that a vanilla CLIP is inferior to localization and segmentation due to its optimization being driven by densely aligning visual and language representations. Second, we propose the locality-driven alignment (LoDA) to address the problem, where CLIP optimization is driven by sparsely aligning local representations. Third, we propose a simple segmentation (SimSeg) framework. LoDA and SimSeg jointly ameliorate a vanilla CLIP to produce impressive semantic segmentation results. Our method outperforms previous state-of-the-art methods on PASCAL VOC 2012, PASCAL Context and COCO datasets by large margins. Code and models are available at ***/muyangyi/SimSeg.

关键词： and reasoning language vision

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Automatic High Resolution Wire Segmentation and Removal

Automatic High Resolution Wire Segmentation and Removal

引用

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

作者： Chiu, Mang Tik Zhang, Xuaner Wei, Zijun Zhou, Yuqian Shechtman, Eli Barnes, Connelly Lin, Zhe Kainz, Florian Amirghodsi, Sohrab Shi, Humphrey UIUC Champaign IL 61801 USA Adobe San Jose CA USA Univ Oregon Eugene OR USA

ISBN: (纸本)9798350301298

Wires and powerlines are common visual distractions that often undermine the aesthetics of photographs. The manual process of precisely segmenting and removing them is extremely tedious and may take up hours, especially on high-resolution photos where wires may span the entire space. In this paper, we present an automatic wire clean-up system that eases the process of wire segmentation and removal/inpainting to within a few seconds. We observe several unique challenges: wires are thin, lengthy, and sparse. These are rare properties of subjects that common segmentation tasks cannot handle, especially in high-resolution images. We thus propose a two-stage method that leverages both global and local contexts to accurately segment wires in high-resolution images efficiently, and a tile-based inpainting strategy to remove the wires given our predicted segmentation masks. We also introduce the first wire segmentation benchmark dataset, WireSegHR. Finally, we demonstrate quantitatively and qualitatively that our wire clean-up system enables fully automated wire removal with great generalization to various wire appearances.

关键词： vision applications and systems

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Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment

Hierarchical NeuroSymbolic Approach for Comprehensive and Ex...

引用

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

作者： Okamoto, Lauren Parmar, Paritosh Princeton Univ Princeton NJ 08544 USA ASTAR IHPC Singapore Singapore

ISBN: (纸本)9798350365474

Action quality assessment (AQA) applies computer vision to quantitatively assess the performance or execution of a human action. Current AQA approaches are end-to-end neural models, which lack transparency and tend to be biased because they are trained on subjective human judgements as ground-truth. To address these issues, we introduce a neuro-symbolic paradigm for AQA, which uses neural networks to abstract interpretable symbols from video data and makes quality assessments by applying rules to those symbols. We take diving as the case study. We found that domain experts prefer our system and find it more informative than purely neural approaches to AQA in diving. Our system also achieves state-of-the-art action recognition and temporal segmentation, and automatically generates a detailed report that breaks the dive down into its elements and provides objective scoring with visual evidence. As verified by a group of domain experts, this report may be used to assist judges in scoring, help train judges, and provide feedback to divers. Annotated training data and code: https://***/laurenok24/NSAQA.

关键词： action quality assessment action recognition AI Coach AI Diving Coach AI Diving Judge AI Olympics Judge explainable AI fairness in AI interpretable action analysis interpretable action quality assessment interpretable fine-grained action quality assessment neuro-symbolic computer vision neurosymbolic action assessment neurosymbolic action scoring neurosymbolic AI neurosymbolic fine-grained action analysis neurosymbolic fine-grained action quality assessment neurosymbolic fine-grained action recogntion neurosymbolic fine-grained action understanding neurosymbolic skills assessment neurosymbolic temporal segmentation neurosymbolic video understanding Olympics Scoring representation learning skills assessment temporal segmentation transparent AI XAI

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Multi-Modal Representation Learning with Text-Driven Soft Masks

Multi-Modal Representation Learning with Text-Driven Soft Ma...

引用

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

作者： Park, Jaeyoo Han, Bohyung Seoul Natl Univ Comp Vis Lab ECE Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (纸本)9798350301298

We propose a visual-linguistic representation learning approach within a self-supervised learning framework by introducing a new operation, loss, and data augmentation strategy. First, we generate diverse features for the image-text matching (ITM) task via soft-masking the regions in an image, which are most relevant to a certain word in the corresponding caption, instead of completely removing them. Since our framework relies only on image-caption pairs with no fine-grained annotations, we identify the relevant regions to each word by computing the word-conditional visual attention using multi-modal encoder. Second, we encourage the model to focus more on hard but diverse examples by proposing a focal loss for the image-text contrastive learning (ITC) objective, which alleviates the inherent limitations of overfitting and bias issues. Last, we perform multi-modal data augmentations for self-supervised learning via mining various examples by masking texts and rendering distortions on images. We show that the combination of these three innovations is effective for learning a pretrained model, leading to outstanding performance on multiple vision-language downstream tasks.

关键词： language reasoning vision

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NVTC: Nonlinear Vector Transform Coding

NVTC: Nonlinear Vector Transform Coding

引用

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

作者： Feng, Runsen Guo, Zongyu Li, Weiping Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (RD) performance [33]. Recent state-of-the-art methods for neural image compression are mainly based on nonlinear transform coding (NTC) with uniform scalar quantization, overlooking the benefits of VQ due to its exponentially increased complexity. In this paper, we first investigate on some toy sources, demonstrating that even if modern neural networks considerably enhance the compression performance of SQ with nonlinear transform, there is still an insurmountable chasm between SQ and VQ. Therefore, revolving around VQ, we propose a novel framework for neural image compression named Nonlinear Vector Transform Coding (NVTC). NVTC solves the critical complexity issue of VQ through (1) a multi-stage quantization strategy and (2) nonlinear vector transforms. In addition, we apply entropy-constrained VQ in latent space to adaptively determine the quantization boundaries for joint rate-distortion optimization, which improves the performance both theoretically and experimentally. Compared to previous NTC approaches, NVTC demonstrates superior rate-distortion performance, faster decoding speed, and smaller model size. Our code is available at https://***/USTC-IMCL/NVTC.

关键词： Low-level vision

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Revisiting Reverse Distillation for Anomaly Detection

Revisiting Reverse Distillation for Anomaly Detection

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

作者： Tran Dinh Tien Anh Tuan Nguyen Nguyen Hoang Tran Ta Duc Huy Duong, Soan T. M. Nguyen, Chanh D. Tr. Truong, Steven Q. H. VinBrain JSC Hanoi Vietnam VinUniversity Hanoi Vietnam Le Quy Don Tech Univ Hanoi Vietnam

ISBN: (纸本)9798350301298

Anomaly detection is an important application in large-scale industrial manufacturing. Recent methods for this task have demonstrated excellent accuracy but come with a latency trade-off. Memory based approaches with dominant performances like PatchCore or Coupled-hypersphere-based Feature Adaptation (CFA) require an external memory bank, which significantly lengthens the execution time. Another approach that employs Reversed Distillation (RD) can perform well while maintaining low latency. In this paper, we revisit this idea to improve its performance, establishing a new state-of-the-art benchmark on the challenging MVTec dataset for both anomaly detection and localization. The proposed method, called RD++, runs six times faster than PatchCore, and two times faster than CFA but introduces a negligible latency compared to RD. We also experiment on the BTAD and Retinal OCT datasets to demonstrate our method's generalizability and conduct important ablation experiments to provide insights into its configurations. Source code will be available at https:// ***/tientrandinh/Revisiting-Reverse-Distillation.

关键词： detection recognition: Categorization retrieval

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Dynamic Inference with Grounding Based vision and Language Models

Dynamic Inference with Grounding Based Vision and Language M...

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

作者： Uzkent, Burak Garg, Amanmeet Zhu, Wentao Doshi, Keval Yi, Jingru Wang, Xiaolong Omar, Mohamed Amazon Prime Video Seattle WA 30439 USA

ISBN: (纸本)9798350301298

Transformers have been recently utilized for vision and language tasks successfully. For example, recent image and language models with more than 200M parameters have been proposed to learn visual grounding in the pre-training step and show impressive results on downstream vision and language tasks. On the other hand, there exists a large amount of computational redundancy in these large models which skips their run-time efficiency. To address this problem, we propose dynamic inference for grounding based vision and language models conditioned on the input imagetext pair. We first design an approach to dynamically skip multihead self-attention and feed forward network layers across two backbones and multimodal network. Additionally, we propose dynamic token pruning and fusion for two backbones. In particular, we remove redundant tokens at different levels of the backbones and fuse the image tokens with the language tokens in an adaptive manner. To learn policies for dynamic inference, we train agents using reinforcement learning. In this direction, we replace the CNN backbone in a recent grounding-based vision and language model, MDETR, with a vision transformer and call it ViTMDETR. Then, we apply our dynamic inference method to ViTMDETR, called D-ViTDMETR, and perform experiments on image-language tasks. Our results show that we can improve the run-time efficiency of the state-of-the-art models MDETR and GLIP by up to similar to 50% on Referring Expression Comprehension and Segmentation, and VQA with only maximum similar to 0.3% accuracy drop.

关键词： Efficient and scalable vision

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