Search Results for author:
Found 6 papers, 4 papers with code
CASEIN: Cascading Explicit and Implicit Control for Fine-grained Emotion Intensity Regulation
However, these high-level semantic probabilities are often inaccurate and unsmooth at the phoneme level, leading to bias in learning.
COOP: Decoupling and Coupling of Whole-Body Grasping Pose Generation
To address this issue, we propose a novel framework called COOP (DeCOupling and COupling of Whole-Body GrasPing Pose Generation) to synthesize life-like whole-body poses that cover the widest range of human grasping capabilities.
ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration
Nevertheless, most existing VLP approaches have not fully utilized the intrinsic knowledge within the image-text pairs, which limits the effectiveness of the learned alignments and further restricts the performance of their models.
Deep Multimodal Neural Architecture Search
Most existing works focus on a single task and design neural architectures manually, which are highly task-specific and hard to generalize to different tasks.
Ranked #19 on
Visual Question Answering (VQA)
on VQA v2 test-std
Multimodal Unified Attention Networks for Vision-and-Language Interactions
Learning an effective attention mechanism for multimodal data is important in many vision-and-language tasks that require a synergic understanding of both the visual and textual contents.
Deep Modular Co-Attention Networks for Visual Question Answering
In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth.
Ranked #5 on
Question Answering
on SQA3D
