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Found 8 papers, 3 papers with code
Knowledge Condensation and Reasoning for Knowledge-based VQA
We condense the retrieved knowledge passages from two perspectives.
Towards Efficient and Effective Text-to-Video Retrieval with Coarse-to-Fine Visual Representation Learning
To address this issue, we adopt multi-granularity visual feature learning, ensuring the model's comprehensiveness in capturing visual content features spanning from abstract to detailed levels during the training phase.
Cross-Domain Product Representation Learning for Rich-Content E-Commerce
It is the first dataset to cover product pages, short videos, and live streams simultaneously, providing the basis for establishing a unified product representation across different media domains.
Cross-view Semantic Alignment for Livestreaming Product Recognition
Moreover, a cRoss-vIew semantiC alignmEnt (RICE) model is proposed to learn discriminative instance features from the image and video views of the products.
3rd Place Solution to "Google Landmark Retrieval 2020"
Image retrieval is a fundamental problem in computer vision.
Locality-Sensitive Deconvolution Networks With Gated Fusion for RGB-D Indoor Semantic Segmentation
This layer can learn to adjust the contributions of RGB and depth over each pixel for high-performance object recognition.
Ranked #79 on
Semantic Segmentation
on NYU Depth v2
Query Adaptive Similarity Measure for RGB-D Object Recognition
The reasons are in two-fold: (1) existing similarity measures are sensitive to object pose and scale changes, as well as intra-class variations; and (2) effectively fusing RGB and depth cues is still an open problem.
MeshStereo: A Global Stereo Model With Mesh Alignment Regularization for View Interpolation
We present a novel global stereo model designed for view interpolation.
