Semantic segmentation using CNN and Domain Transform, achieving 66.35% mIOU with improved boundary precision.

Liang-Chieh Chen, Jonathan T. Barron, George Papandreou et al.

Neural Module Networks combine deep learning with linguistic structure, achieving top results on VQA datasets.

Jacob Andreas, Marcus Rohrbach, Trevor Darrell et al.

Introduces Boundary Neural Field (BNF) to enhance semantic segmentation by integrating boundary cues with FCN predictions.

Gedas Bertasius, Jianbo Shi, Lorenzo Torresani

Proposed a method to generate and comprehend unambiguous object descriptions in images, based on MS-COCO dataset.

Junhua Mao, Jonathan Huang, Alexander Toshev et al.

Using convolutional networks to learn visual features from 100 million Flickr images, achieving performance comparable to Imagenet.

Armand Joulin, Laurens van der Maaten, Allan Jabri et al.

Improved A+ combines five upgrades and raises Set5 ×3 PSNR from 32.59 to 33.46 dB.

Radu Timofte, Rasmus Rothe, Luc Van Gool

Deep compression combines pruning, trained quantization, and Huffman coding, achieving 35-49× compression without accuracy loss.

Song Han, Huizi Mao, William J. Dally

Proposes semantic amodal segmentation with large-scale datasets, achieving 78.5% AR on COCO, advancing scene understanding.

Yan Zhu, Yuandong Tian, Dimitris Mexatas et al.

Introduces LogDet-based non-convex rank approximation for subspace clustering, outperforming nuclear norm methods with improved accuracy and robustness.

Zhao Kang, Chong Peng, Jie Cheng et al.

Proposes an unsupervised multimodal approach combining video and narration, achieving 85% accuracy in key task step detection on a new dataset.

Jean-Baptiste Alayrac, Piotr Bojanowski, Nishant Agrawal et al.

Proposes a deep neural framework combining unsupervised sentence embeddings and video-text models for aligning books and movies, achieving over 70% accuracy.

Yukun Zhu, Ryan Kiros, Richard Zemel et al.

Deep visualization tools reveal computations in intermediate layers of convolutional neural networks.

Jason Yosinski, Jeff Clune, Anh Nguyen et al.

Proposes a graph-based visual relationship model using large-scale Amazon data for clothing and accessory recommendations.

Julian McAuley, Christopher Targett, Qinfeng Shi et al.

Unsupervised visual representation learning via context prediction, enhancing object discovery on Pascal VOC 2011 dataset.

Carl Doersch, Abhinav Gupta, Alexei A. Efros

Proposes CNN-based joint instance segmentation and depth ordering via multi-scale patch prediction and MRF fusion, achieving state-of-the-art on KITTI.

Ziyu Zhang, Alexander G. Schwing, Sanja Fidler et al.

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.