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Found 11 papers, 4 papers with code
Position-Aware Contrastive Alignment for Referring Image Segmentation
The main challenge of this task is to understand the visual and linguistic content simultaneously and to find the referred object accurately among all instances in the image.
1st Place Solution for YouTubeVOS Challenge 2022: Referring Video Object Segmentation
The task of referring video object segmentation aims to segment the object in the frames of a given video to which the referring expressions refer.
Referring Video Object Segmentation
Semantic Segmentation
+1
Transformer-based Entity Typing in Knowledge Graphs
We investigate the knowledge graph entity typing task which aims at inferring plausible entity types.
Visual Subtitle Feature Enhanced Video Outline Generation
Furthermore, based on the similarity between video outlines and textual outlines, we use a large number of articles with chapter headings to pretrain our model.
Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs
Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing information.
Deeply Interleaved Two-Stream Encoder for Referring Video Segmentation
To address this task, we first design a two-stream encoder to extract CNN-based visual features and transformer-based linguistic features hierarchically, and a vision-language mutual guidance (VLMG) module is inserted into the encoder multiple times to promote the hierarchical and progressive fusion of multi-modal features.
Ranked #1 on
Referring Expression Segmentation
on J-HMDB
Youling: an AI-Assisted Lyrics Creation System
In the lyrics generation process, \textit{Youling} supports traditional one pass full-text generation mode as well as an interactive generation mode, which allows users to select the satisfactory sentences from generated candidates conditioned on preceding context.
Encoder Fusion Network with Co-Attention Embedding for Referring Image Segmentation
In this work, we propose an encoder fusion network (EFN), which transforms the visual encoder into a multi-modal feature learning network, and uses language to refine the multi-modal features progressively.
Possible multi-orbital ground state in CeCu$_2$Si$_2$
The crystal-field ground state wave function of CeCu$_2$Si$_2$ has been investigated with linear polarized $M$-edge x-ray absorption spectroscopy from 250mK to 250K, thus covering the superconducting ($T_{\text{c}}$=0. 6K), the Kondo ($T_{\text{K}}$$\approx$20K) as well as the Curie-Weiss regime.
Strongly Correlated Electrons
Bi-Directional Relationship Inferring Network for Referring Image Segmentation
Combining with the language-guided visual attention, a bi-directional cross-modal attention module (BCAM) is built to learn the relationship between multi-modal features.
Ranked #11 on
Referring Expression Segmentation
on RefCOCO testB
