Proposes a multi-region CNN with semantic segmentation-aware features and iterative bounding box refinement, achieving 78.2% mAP on VOC2007.

Spyros Gidaris, Nikos Komodakis

Neural-Image-QA combines CNN and LSTM, doubling previous accuracy to 17.49%, advancing multi-modal visual question answering.

Mateusz Malinowski, Marcus Rohrbach, Mario Fritz

Proposes a deep convolutional neural network-based direct perception model estimating 13 key affordance indicators for autonomous driving, trained on 12 hours of video game data.

Chenyi Chen, Ari Seff, Alain Kornhauser et al.

Fast R-CNN achieves 9× faster training, 146× faster detection, with 66% mAP on VOC2007, by sharing features and end-to-end multi-task learning.

Ross Girshick

FlowNet learns dense optical flow end-to-end, training on Flying Chairs and reaching 5–10 fps while generalizing to Sintel and KITTI.

Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg et al.

Introduced HED, a new edge detection algorithm achieving an ODS F-score of 0.782 on the BSD500 dataset.

Saining Xie, Zhuowen Tu

MAP-Elites maps high-performance solutions across feature space, revealing solution distribution and diversity.

Jean-Baptiste Mouret, Jeff Clune

Proposes incremental sparse Gaussian process regression for continuous-time trajectory estimation, achieving 3x speedup while maintaining accuracy.

Xinyan Yan, Vadim Indelman, Byron Boots

Kernel Manifold Alignment (KEMA) enables multi-source, unpaired domain alignment with superior performance on synthetic and real datasets.

Devis Tuia, Gustau Camps-Valls

Proposes end-to-end training of deep visuomotor policies using Guided Policy Search with a 92,000-parameter CNN for direct image-to-torque mapping.

Sergey Levine, Chelsea Finn, Trevor Darrell et al.

K-FAC approximates Fisher matrix via Kronecker decomposition, enabling faster natural gradient optimization in neural networks.

James Martens, Roger Grosse

LINE efficiently embeds large-scale networks by optimizing first- and second-order proximities with edge sampling, handling millions of nodes and billions of edges.

Jian Tang, Meng Qu, Mingzhe Wang et al.

DC-IGN learns interpretable image representations using SGVB, generating images with varied poses and lighting.

Tejas D. Kulkarni, Will Whitney, Pushmeet Kohli et al.

Knowledge distillation transfers ensemble model knowledge into a single small model, achieving near-ensemble performance on MNIST and speech recognition tasks.

Geoffrey Hinton, Oriol Vinyals, Jeff Dean

The Bayesian Case Model (BCM) integrates case-based reasoning with a generative framework.

Been Kim, Cynthia Rudin, Julie Shah

Support function-based online convex optimization achieves Blackwell approachability with O(T−1/2) convergence.

Nahum Shimkin

Exp3.G classifies feedback graphs: strongly observable gives ~√(αT), weakly observable ~δ^(1/3)T^(2/3), and unobservable Θ(T).

Noga Alon, Nicolò Cesa-Bianchi, Ofer Dekel et al.

Proposed Deep Convolutional Neural Field model outperforms existing methods in monocular depth estimation.

Fayao Liu, Chunhua Shen, Guosheng Lin et al.

Proposes a meta-algorithm (SAOL) transforming low-regret algorithms into strongly adaptive ones, ensuring near-optimal performance on every interval with \( O(\log T) \) overhead.

Amit Daniely, Alon Gonen, Shai Shalev-Shwartz

Proposes 20 synthetic QA tasks to evaluate reasoning; extends Memory Networks, revealing current model limitations in multi-step inference.

Jason Weston, Antoine Bordes, Sumit Chopra et al.