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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

Surveys explainable AI (XAI), reviewing approaches, trends, and research directions for making black-box AI systems transparent.

This survey addresses the lack of transparency in AI systems, whose black-box nature enables powerful predictions that cannot be directly explained. It frames explainable AI (XAI) as a field holding substantial promise for improving trust and transparency, and as essential for AI to keep making progress without disruption. Through the lens of the literature, it provides an entry point for researchers and practitioners, reviewing existing approaches, discussing trends, and presenting major research trajectories.

Based on: Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) · IEEE Access

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OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Introduces OverFeat, an integrated ConvNet framework performing classification, localization, and detection with a single shared network.

OverFeat is an integrated framework that uses convolutional networks for classification, localization, and detection. It efficiently implements a multiscale, sliding-window approach within a ConvNet and introduces a localization method that learns to predict object boundaries, accumulating bounding boxes to increase detection confidence. A single shared network learns all three tasks simultaneously. The framework won the ILSVRC2013 localization task, gave competitive detection and classification results, and set a new state of the art for detection.

Based on: OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks · International Conference on Learning Representations

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Qwen2.5-VL Technical Report

Presents Qwen2.5-VL, a vision-language model with strong object localization, document parsing, and long-video understanding.

Qwen2.5-VL is the flagship Qwen vision-language model, advancing visual recognition, precise object localization via bounding boxes or points, robust document and structured-data parsing, and long-video comprehension. It adds dynamic resolution processing and absolute time encoding to handle varying image sizes and hours-long videos with second-level event localization, using a natively trained dynamic-resolution ViT with window attention. Its 72B version matches models like GPT-4o and Claude 3.5 Sonnet while retaining strong language skills.

Based on: Qwen2.5-VL Technical Report · arXiv.org

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Deep High-Resolution Representation Learning for Human Pose Estimation

Proposes HRNet, which maintains high-resolution representations throughout the network for more precise human pose estimation.

The paper addresses human pose estimation by learning reliable high-resolution representations. Unlike methods that recover high-resolution features from low-resolution ones, the proposed network maintains high resolution throughout by connecting multi-resolution subnetworks in parallel and repeatedly fusing them across scales. This yields richer representations and more accurate, spatially precise keypoint heatmaps. It achieves superior results on the COCO keypoint, MPII, and PoseTrack benchmarks, with code released publicly.

Based on: Deep High-Resolution Representation Learning for Human Pose Estimation · Computer Vision and Pattern Recognition

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Understanding deep learning requires rethinking generalization

Shows that large neural networks can perfectly fit random labels, challenging conventional explanations of why deep nets generalize.

The paper questions why large neural networks generalize well despite their size. Through systematic experiments, it shows state-of-the-art convolutional networks trained with stochastic gradient methods can easily fit random labels, and even unstructured random noise, with explicit regularization having little effect. A theoretical construction shows simple depth-two networks already achieve perfect finite-sample expressivity once parameters exceed data points, indicating traditional explanations of generalization are insufficient.

Based on: Understanding deep learning requires rethinking generalization · International Conference on Learning Representations

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Wasserstein GAN

Introduces WGAN, an alternative GAN training algorithm that improves training stability and eliminates mode collapse.

The paper introduces WGAN, an alternative to conventional GAN training. It improves the stability of learning, removes issues such as mode collapse, and yields meaningful learning curves useful for debugging and hyperparameter searches. The authors show that the corresponding optimization problem is sound and provide extensive theoretical work linking it to other distances between probability distributions.

Based on: Wasserstein GAN · arXiv.org

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What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Presents a Bayesian deep learning framework combining aleatoric and epistemic uncertainty, improving semantic segmentation and depth regression.

Two uncertainties matter: aleatoric, the noise inherent in observations, and epistemic, model uncertainty that more data can explain away. Epistemic uncertainty is hard to model in vision, but new Bayesian deep learning tools make it tractable. They present a framework combining input-dependent aleatoric and epistemic uncertainty, studied on semantic segmentation and depth regression. Their explicit formulation yields new loss functions interpretable as learned attenuation, making training robust to noisy data and setting new state-of-the-art on these benchmarks.

Based on: What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? · Neural Information Processing Systems

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Learning Transferable Architectures for Scalable Image Recognition

Learns transferable CNN cells by searching a building block on CIFAR-10 (the NASNet search space) and stacking it for ImageNet-scale recognition.

Because designing image classification networks demands heavy engineering, this work learns architectures directly from data. Since searching a large dataset is costly, it searches a convolutional cell on small CIFAR-10 and transfers it to ImageNet by stacking copies, enabled by a new transferable 'NASNet search space' and a ScheduledDropPath regularizer. The best cell gives 2.4% error on CIFAR-10 and, stacked, 82.7% top-1 on ImageNet, 1.2% above the best human-designed models with 9 billion fewer FLOPS. Its features also transfer to COCO detection at 43.1% mAP.

Based on: Learning Transferable Architectures for Scalable Image Recognition · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Striving for Simplicity: The All Convolutional Net

Shows max-pooling can be replaced by strided convolutions, proposing an all-convolutional network competitive on CIFAR and ImageNet.

Most modern CNNs for object recognition alternate convolution and max-pooling layers followed by a few fully connected layers. The authors re-evaluate small-image object recognition, questioning whether each component is necessary. They find max-pooling can be replaced by a convolutional layer with increased stride without losing accuracy on several benchmarks. They then propose an architecture made solely of convolutional layers that yields competitive or state-of-the-art results on CIFAR-10, CIFAR-100, and ImageNet, plus a new deconvolution-based feature visualization.

Based on: Striving for Simplicity: The All Convolutional Net · International Conference on Learning Representations

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FCOS: Fully Convolutional One-Stage Object Detection

Proposes FCOS, an anchor-free, proposal-free one-stage object detector that predicts objects per-pixel, analogous to semantic segmentation.

FCOS is a fully convolutional one-stage object detector solving detection by per-pixel prediction like semantic segmentation. Unlike leading detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN, which depend on pre-defined anchor boxes, FCOS is anchor-box and proposal free, avoiding complex overlap computation and sensitive anchor hyperparameters. Using only non-maximum suppression, FCOS with a ResNeXt-64x4d-101 backbone reaches 44.7% AP with single-model, single-scale testing, surpassing prior one-stage detectors while being simpler and more flexible.

Based on: FCOS: Fully Convolutional One-Stage Object Detection · IEEE International Conference on Computer Vision

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Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization

Introduces adaptive instance normalization (AdaIN), enabling arbitrary neural style transfer in real time with a single feed-forward network.

Gatys et al.'s neural style transfer renders a content image in another's style but relies on slow iterative optimization. Fast feed-forward methods speed this up but are tied to fixed styles. The paper presents a simple approach that for the first time enables arbitrary style transfer in real time. Its core is an adaptive instance normalization (AdaIN) layer that aligns content feature mean and variance to style features. It matches the fastest method's speed without predefined styles, and allows content-style trade-off, interpolation, and spatial control from one network.

Based on: Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization · IEEE International Conference on Computer Vision

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Cascade R-CNN: Delving Into High Quality Object Detection

Introduces Cascade R-CNN, a multi-stage detector trained with increasing IoU thresholds for high-quality object detection with fewer false positives.

An IoU threshold defines positives and negatives, but training at a low threshold (0.5) yields noisy detections while raising it degrades performance, due to overfitting from vanishing positives and inference IoU mismatch. Cascade R-CNN chains detectors trained at increasing IoU thresholds, each more selective against false positives. Each stage's output is a good training set for the next, and resampling keeps positive sets equal-sized to curb overfitting; the cascade also runs at inference. It beats single-model detectors on COCO and generalizes across architectures.

Based on: Cascade R-CNN: Delving Into High Quality Object Detection · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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