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Found 15 papers, 3 papers with code
Neural Progressive Meshes
The recent proliferation of 3D content that can be consumed on hand-held devices necessitates efficient tools for transmitting large geometric data, e. g., 3D meshes, over the Internet.
Breaking Bad: A Dataset for Geometric Fracture and Reassembly
We introduce Breaking Bad, a large-scale dataset of fractured objects.
Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions
The pipeline of current robotic pick-and-place methods typically consists of several stages: grasp pose detection, finding inverse kinematic solutions for the detected poses, planning a collision-free trajectory, and then executing the open-loop trajectory to the grasp pose with a low-level tracking controller.
Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors
While the majority of existing part assembly methods focus on correctly posing semantic parts to recreate a whole object, we interpret assembly more literally: as mating geometric parts together to achieve a snug fit.
Self-Attentive 3D Human Pose and Shape Estimation from Videos
The key insights of our method are two-fold.
Ranked #53 on
3D Human Pose Estimation
on MPI-INF-3DHP
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos
Learning from visual data opens the potential to accrue a large range of manipulation behaviors by leveraging human demonstrations without specifying each of them mathematically, but rather through natural task specification.
NAS-DIP: Learning Deep Image Prior with Neural Architecture Search
Recent work has shown that the structure of deep convolutional neural networks can be used as a structured image prior for solving various inverse image restoration tasks.
Learning to Learn in a Semi-Supervised Fashion
To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme.
Deep Semantic Matching with Foreground Detection and Cycle-Consistency
Establishing dense semantic correspondences between object instances remains a challenging problem due to background clutter, significant scale and pose differences, and large intra-class variations.
Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond
Person re-identification (re-ID) aims at matching images of the same person across camera views.
CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency
Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e. g., synthetic to real images).
Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification
Person re-identification (re-ID) aims at matching images of the same identity across camera views.
Learning Resolution-Invariant Deep Representations for Person Re-Identification
Moreover, the extension of our model for semi-supervised re-ID further confirms the scalability of our proposed method for real-world scenarios and applications.
Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-segmentation
In contrast to existing algorithms that tackle the tasks of semantic matching and object co-segmentation in isolation, our method exploits the complementary nature of the two tasks.
Deep Learning for Malicious Flow Detection
We also conduct a partial flow experiment which shows the feasibility of real-time detection and a zero-shot learning experiment which justifies the generalization capability of deep learning in cyber security.
