Introduces Forged Calamity benchmark and shows that fine-tuned and zero-shot synthetic image detectors lose substantial accuracy on unseen generators and disaster types.
Con- vnext v2: Co-designing and scaling convnets with masked autoencoders
9 Pith papers cite this work, alongside 65 external citations. Polarity classification is still indexing.
9
Pith papers citing it
65
external citations · Pith
abstract
Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt, have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE). However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.
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Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
Behavior-aligned ANNs prospectively select diagnostic facial expressions that enlarge autistic–neurotypical emotion-judgment gaps, and GAN-guided transforms of those faces reduce the gaps under phenotype-matched validation.
LFNet fuses complementary representations from state space and convolutional backbones using liquid fusion and saliency-guided upsampling to reach state-of-the-art performance on five salient object detection tasks.
Mapping point clouds to Fourier features improves high-precision imitation learning policies on RoboCasa, ManiSkill3, and real-robot tasks compared with Cartesian inputs.
Transcoda achieves state-of-the-art zero-shot OMR with an 18.46% OMR-NED error rate on synthetic scores and 63.97% on historical Polish scans using a 59M model trained in 6 hours via synthetic data, kern normalization, and grammar decoding.
Random parameter pruning during targeted attack optimization on surrogate models yields up to 11.7% higher average attack success rates when transferring to Transformer targets.
Sparse MoE vision models show positive accuracy gaps only when routing a substantial compute fraction ρ and using k≥2 experts at large scale; batch-axis dispatch is identified as a key failure mode.
Upgrades to WeatherGFT PCNNs with WENO-5 solver, unified autoregressive block, and two new neural backbones yield 8-22% lower RMSE at 1-12 h leads on WeatherBench South Pacific data while improving physical consistency.
citing papers explorer
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Forged Calamity: Benchmark for Cross-Domain Synthetic Disaster Detection in the Age of Diffusion
Introduces Forged Calamity benchmark and shows that fine-tuned and zero-shot synthetic image detectors lose substantial accuracy on unseen generators and disaster types.
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Toward Calibrated, Fair, and accurate Deepfake Detection
Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
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AI-guided stimuli discovery and generation to optimize facial emotion perception studies in autism
Behavior-aligned ANNs prospectively select diagnostic facial expressions that enlarge autistic–neurotypical emotion-judgment gaps, and GAN-guided transforms of those faces reduce the gaps under phenotype-matched validation.
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Liquid Fusion of Heterogeneous Representations Towards General Salient Object Detection
LFNet fuses complementary representations from state space and convolutional backbones using liquid fusion and saliency-guided upsampling to reach state-of-the-art performance on five salient object detection tasks.
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Fourier Features Let Agents Learn High Precision Policies with Imitation Learning
Mapping point clouds to Fourier features improves high-precision imitation learning policies on RoboCasa, ManiSkill3, and real-robot tasks compared with Cartesian inputs.
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Transcoda: End-to-End Zero-Shot Optical Music Recognition via Data-Centric Synthetic Training
Transcoda achieves state-of-the-art zero-shot OMR with an 18.46% OMR-NED error rate on synthetic scores and 63.97% on historical Polish scans using a 59M model trained in 6 hours via synthetic data, kern normalization, and grammar decoding.
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RaPA: Enhancing Transferable Targeted Attacks via Random Parameter Pruning
Random parameter pruning during targeted attack optimization on surrogate models yields up to 11.7% higher average attack success rates when transferring to Transformer targets.
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When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse Routing
Sparse MoE vision models show positive accuracy gaps only when routing a substantial compute fraction ρ and using k≥2 experts at large scale; batch-axis dispatch is identified as a key failure mode.
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Physics-Constrained Neural Networks for Improved Short-Term Weather Forecasting: A Case Study over the South Pacific
Upgrades to WeatherGFT PCNNs with WENO-5 solver, unified autoregressive block, and two new neural backbones yield 8-22% lower RMSE at 1-12 h leads on WeatherBench South Pacific data while improving physical consistency.