The proceedings contain 516 papers. The topics discussed include: OmniLayout: room layout reconstruction from indoor spherical panoramas;boosting adversarial robustness using feature level stochastic smoothing;beyond ...
ISBN:
(纸本)9781665448994
The proceedings contain 516 papers. The topics discussed include: OmniLayout: room layout reconstruction from indoor spherical panoramas;boosting adversarial robustness using feature level stochastic smoothing;beyond joint demosaicking and denoising: an image processing pipeline for a pixel-bin image sensor;assessment of deep learning based blood pressure prediction from PPG and rPPG signals;towards domain-specific explainable AI: model interpretation of a skin image classifier using a human approach;DAMSL: domain agnostic meta score-based learning;deep learning based spatial-temporal in-loop filtering for versatile video coding;automated tackle injury risk assessment in contact-based sports - a rugby union example;two-stage network for single image super-resolution;and ***: dataset for automatic mapping of buildings, woodlands, water and roads from aerial imagery.
The proceedings contain 802 papers. The topics discussed include: X-VARS: introducing explainability in football refereeing with multi-modal large language models;a hybrid ANN-SNN architecture for low-power and low-la...
ISBN:
(纸本)9798350365474
The proceedings contain 802 papers. The topics discussed include: X-VARS: introducing explainability in football refereeing with multi-modal large language models;a hybrid ANN-SNN architecture for low-power and low-latency visual perception;pseudo-label based unsupervised fine-tuning of a monocular 3D pose estimation model for sports motions;towards efficient audio-visual learners via empowering pre-trained vision transformers with cross-modal adaptation;a dual-mode approach for vision-based navigation in a lunar landing scenario;class similarity transition: decoupling class similarities and imbalance from generalized few-shot segmentation;ReweightOOD: loss reweighting for distance-based OOD detection;Hinge-Wasserstein: estimating multimodal aleatoric uncertainty in regression tasks;and ConPro: learning severity representation for medical images using contrastive learning and preference optimization.
The proceedings contain 355 papers. The topics discussed include: MultiNet++: multi-stream feature aggregation and geometric loss strategy for multi-task learning;privacy-preserving action recognition using coded aper...
ISBN:
(纸本)9781728125060
The proceedings contain 355 papers. The topics discussed include: MultiNet++: multi-stream feature aggregation and geometric loss strategy for multi-task learning;privacy-preserving action recognition using coded aperture videos;evading face recognition via partial tampering of faces;privacy-preserving annotation of face images through attribute-preserving face synthesis;towards deep neural network training on encrypted data;fooling automated surveillance cameras: adversarial patches to attack person detection;anonymousnet: natural face de-identification with measurable privacy;regularizer to mitigate gradient masking effect during single-step adversarial training;privacy preserving group membership verification and identification;defending against adversarial attacks using random forest;intersection to overpass: instance segmentation on filamentous structures with an orientation-aware neural network and terminus pairing algorithm;and surface parameterization and registration for statistical multiscale atlasing of organ development.
The proceedings contain 523 papers. The topics discussed include: latent fingerprint image enhancement based on progressive generative adversarial network;zero-shot learning in the presence of hierarchically coarsened...
ISBN:
(纸本)9781728193601
The proceedings contain 523 papers. The topics discussed include: latent fingerprint image enhancement based on progressive generative adversarial network;zero-shot learning in the presence of hierarchically coarsened labels;multivariate confidence calibration for object detection;context-guided super-class inference for zero-shot detection;learning sparse ternary neural networks with entropy-constrained trained ternarization (EC2T);now that i can see, i can improve: enabling data-driven finetuning of CNNs on the edge;enhancing facial data diversity with style-based face aging;a simplified framework for zero-shot cross-modal sketch data retrieval;unsupervised single image super-resolution network (USISResNet) for real-world data using generative adversarial network;cross-regional oil palm tree detection;and leaf spot attention network for apple leaf disease identification.
The proceedings contain 561 papers. The topics discussed include: CORE: consistent representation learning for face forgery detection;aria: adversarially robust image attribution for content provenance;the reliability...
ISBN:
(纸本)9781665487399
The proceedings contain 561 papers. The topics discussed include: CORE: consistent representation learning for face forgery detection;aria: adversarially robust image attribution for content provenance;the reliability of forensic body-shape identification;detecting real-time deep-fake videos using active illumination;on the exploitation of deepfake model recognition;is synthetic voice detection research going into the right direction?;on improving cross-dataset generalization of deepfake detectors;rethinking adversarial examples in wargames;privacy leakage of adversarial training models in federated learning systems;towards comprehensive testing on the robustness of cooperative multi-agent reinforcement learning;robustness and adaptation to hidden factors of variation;adversarial robustness through the lens of convolutional filters;RODD: a self-supervised approach for robust out-of-distribution detection;an empirical study of data-free quantization’s tuning robustness;exploring robustness connection between artificial and natural adversarial examples;and adversarial machine learning attacks against video anomaly detection systems.
The proceedings contain 698 papers. The topics discussed include: learning unbiased classifiers from biased data with meta-learning;robustness against gradient based attacks through cost effective network fine-tuning;...
ISBN:
(纸本)9798350302493
The proceedings contain 698 papers. The topics discussed include: learning unbiased classifiers from biased data with meta-learning;robustness against gradient based attacks through cost effective network fine-tuning;gradient attention balance network: mitigating face recognition racial bias via gradient attention;estimating and maximizing mutual information for knowledge distillation;synthetic sample selection for generalized zero-shot learning;training strategies for vision transformers for object detection;does image anonymization impact computervision training?;ultra-sonic sensor based object detection for autonomous vehicles;improvements to image reconstruction-based performance prediction for semantic segmentation in highly automated driving;zero-shot classification at different levels of granularity;difficulty estimation with action scores for computervision tasks;detail-preserving self-supervised monocular depth with self-supervised structural sharpening;isolated sign language recognition based on tree structure skeleton images;deep prototypical-parts ease morphological kidney stone identification and are competitively robust to photometric perturbations;wildlife image generation from scene graphs;towards characterizing the semantic robustness of face recognition;high-level context representation for emotion recognition in images;and mitigating catastrophic interference using unsupervised multi-part attention for RGB-IR face recognition.
The proceedings contain 1658 papers. The topics discussed include: single-stage instance shadow detection with bidirectional relation learning;learning Delaunay surface elements for mesh reconstruction;fusing the old ...
ISBN:
(纸本)9781665445092
The proceedings contain 1658 papers. The topics discussed include: single-stage instance shadow detection with bidirectional relation learning;learning Delaunay surface elements for mesh reconstruction;fusing the old with the new: learning relative camera pose with geometry-guided uncertainty;uncertainty guided collaborative training for weakly supervised temporal action detection;privacy-preserving collaborative learning with automatic transformation search;rethinking and improving the robustness of image style transfer;style-aware normalized loss for improving arbitrary style transfer;faster meta update strategy for noise-robust deep learning;a hyperbolic-to-hyperbolic graph convolutional network;training networks in null space of feature covariance for continual learning;and exponential moving average normalization for self-supervised and semi-supervised learning.
In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of vari...
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ISBN:
(纸本)9781665448994
In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of variation are labelled for a small fraction of our samples. We examine how the quality of learned representations is affected by the dimension of the unsupervised component of the latent space. We also consider a variational lower bound for the mutual information between the data and the semi-supervised component of the latent space, and analyze its role in the context of disentangled representation learning.
This work presents AFRIFASHION1600, an openly accessible contemporary African fashion image dataset containing 1600 samples labelled into 8 classes representing some African fashion styles. Each sample is coloured and...
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ISBN:
(纸本)9781665448994
This work presents AFRIFASHION1600, an openly accessible contemporary African fashion image dataset containing 1600 samples labelled into 8 classes representing some African fashion styles. Each sample is coloured and has an image size of 128 x 128. This is a niche dataset that aims to improve visibility, inclusion, and familiarity of African fashion in computervision ***1600 dataset is available here.
Self-attention is a corner stone for transformer models. However, our analysis shows that self-attention in vision transformer inference is extremely sparse. When applying a sparsity constraint, our experiments on ima...
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ISBN:
(纸本)9781665448994
Self-attention is a corner stone for transformer models. However, our analysis shows that self-attention in vision transformer inference is extremely sparse. When applying a sparsity constraint, our experiments on image (ImageNet-1K) and video (Kinetics-400) understanding show we can achieve 95% sparsity on the self-attention maps while maintaining the performance drop to be less than 2 points. This motivates us to rethink the role of self-attention in vision transformer models.
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