Search Results for author:
Found 22 papers, 5 papers with code
Dynamic Prompt Optimizing for Text-to-Image Generation
Users assign weights or alter the injection time steps of certain words in the text prompts to improve the quality of generated images.
StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models
The ability to fine-tune generative models for text-to-image generation tasks is crucial, particularly facing the complexity involved in accurately interpreting and visualizing textual inputs.
Learning and Evaluating Human Preferences for Conversational Head Generation
In this paper, we propose a novel learning-based evaluation metric named Preference Score (PS) for fitting human preference according to the quantitative evaluations across different dimensions.
Interactive Conversational Head Generation
Based on ViCo and ViCo-X, we define three novel tasks targeting the interaction modeling during the face-to-face conversation: 1) responsive listening head generation making listeners respond actively to the speaker with non-verbal signals, 2) expressive talking head generation guiding speakers to be aware of listeners' behaviors, and 3) conversational head generation to integrate the talking/listening ability in one interlocutor.
Deep Equilibrium Multimodal Fusion
Multimodal fusion integrates the complementary information present in multiple modalities and has gained much attention recently.
Visual-Aware Text-to-Speech
Dynamically synthesizing talking speech that actively responds to a listening head is critical during the face-to-face interaction.
Learning cross space mapping via DNN using large scale click-through logs
The image and query are mapped to a common vector space via these two parts respectively, and image-query similarity is naturally defined as an inner product of their mappings in the space.
Visualizing and Understanding Patch Interactions in Vision Transformer
Vision Transformer (ViT) has become a leading tool in various computer vision tasks, owing to its unique self-attention mechanism that learns visual representations explicitly through cross-patch information interactions.
Freeform Body Motion Generation from Speech
Motivated by studies in linguistics, we decompose the co-speech motion into two complementary parts: pose modes and rhythmic dynamics.
Responsive Listening Head Generation: A Benchmark Dataset and Baseline
Automatically synthesizing listening behavior that actively responds to a talking head, is critical to applications such as digital human, virtual agents and social robots.
Directional Self-supervised Learning for Heavy Image Augmentations
Specifically, we adapt heavy augmentation policies after the views lightly augmented by standard augmentations, to generate harder view (HV).
Augmentation Pathways Network for Visual Recognition
Experimental results on ImageNet demonstrate the compatibility and effectiveness on a much wider range of augmentations, while consuming fewer parameters and lower computational costs at inference time.
Exploiting Relationship for Complex-scene Image Generation
Our method tends to synthesize plausible layouts and objects, respecting the interplay of multiple objects in an image.
Products-10K: A Large-scale Product Recognition Dataset
With the rapid development of electronic commerce, the way of shopping has experienced a revolutionary evolution.
Look-into-Object: Self-supervised Structure Modeling for Object Recognition
Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category.
Ranked #17 on
Fine-Grained Image Classification
on CUB-200-2011
Relationship-Aware Spatial Perception Fusion for Realistic Scene Layout Generation
In our framework, a spatial constraint module is designed to fit reasonable scaling and spatial layout of object pairs with considering relationship between them.
VrR-VG: Refocusing Visually-Relevant Relationships
Relationships encode the interactions among individual instances, and play a critical role in deep visual scene understanding.
Deep Attention Neural Tensor Network for Visual Question Answering
First, we model one of the pairwise interaction (e. g., image and question) by bilinear features, which is further encoded with the third dimension (e. g., answer) to be a triplet by bilinear tensor product.
Automatic Dataset Augmentation
Large scale image dataset and deep convolutional neural network (DCNN) are two primary driving forces for the rapid progress made in generic object recognition tasks in recent years.
Visualizing and Comparing Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have achieved comparable error rates to well-trained human on ILSVRC2014 image classification task.
Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data
Image retrieval refers to finding relevant images from an image database for a query, which is considered difficult for the gap between low-level representation of images and high-level representation of queries.
