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Found 6 papers, 0 papers with code
Learning to Disambiguate by Asking Discriminative Questions
In particular, each tuple consists of a pair of images and 4. 6 discriminative questions (as positive samples) and 5. 9 non-discriminative questions (as negative samples) on average.
Unconstrained Face Alignment via Cascaded Compositional Learning
We present a practical approach to address the problem of unconstrained face alignment for a single image.
Ranked #18 on
Face Alignment
on AFLW-19
Slicing Convolutional Neural Network for Crowd Video Understanding
Learning and capturing both appearance and dynamic representations are pivotal for crowd video understanding.
Sketch Me That Shoe
We investigate the problem of fine-grained sketch-based image retrieval (SBIR), where free-hand human sketches are used as queries to perform instance-level retrieval of images.
Ranked #3 on
Sketch-Based Image Retrieval
on Chairs
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
In this paper, we propose deformable deep convolutional neural networks for generic object detection.
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection
In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty.
