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Learning to Adapt Structured Output Space for Semantic Segmentation

A multi-level adversarial network is constructed to effectively perform output space domain adaptation at different feature levels and it is shown that the proposed method performs favorably against the state-of-the-art methods in terms of accuracy and visual quality.
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DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

The proposed Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes significantly improves the prediction accuracy compared to other baseline methods.
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Person Re-identification in the Wild

A new dataset, PRW, is introduced to evaluate Person Re-identification in the Wild, and it is shown that pedestrian detection aids re-ID through two simple yet effective improvements: a cascaded fine-tuning strategy that trains a detection model first and then the classification model, and a Confidence Weighted Similarity (CWS) metric that incorporates detection scores into similarity measurement.
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IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments

This work proposes DS, a novel dataset for road scene understanding in unstructured environments where the above assumptions are largely not satisfied, and proposes a new four-level label hierarchy, which allows varying degrees of complexity and opens up possibilities for new training methods.
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Towards Large-Pose Face Frontalization in the Wild

This work proposes a novel deep 3D Morphable Model (3DMM) conditioned Face Frontalization Generative Adversarial Network (GAN), termed as FF-GAN, to generate neutral head pose face images, which differs from both traditional GANs and 3DMM based modeling.
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Learning Efficient Object Detection Models with Knowledge Distillation

This work proposes a new framework to learn compact and fast object detection networks with improved accuracy using knowledge distillation and hint learning and shows consistent improvement in accuracy-speed trade-offs for modern multi-class detection models.
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Universal Correspondence Network

A convolutional spatial transformer to mimic patch normalization in traditional features like SIFT is proposed, which is shown to dramatically boost accuracy for semantic correspondences across intra-class shape variations.
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Active Adversarial Domain Adaptation

This work shows that the two views of adversarial domain alignment and importance sampling can be unified in one framework for domain adaptation and transfer learning when the source domain has many labeled examples while the target domain does not.
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Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image

A deep inverse rendering framework for indoor scenes, which combines novel methods to map complex materials to existing indoor scene datasets and a new physically-based GPU renderer to create a large-scale, photorealistic indoor dataset.
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Domain Adaptation for Structured Output via Discriminative Patch Representations

A domain adaptation method to adapt the source data to the unlabeled target domain by discovering multiple modes of patch-wise output distribution through the construction of a clustered space and using an adversarial learning scheme to push the feature representations of target patches in the clustered space closer to the distributions of source patches.
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