Search Results for author:
Found 21 papers, 8 papers with code
Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks
Solving image-to-3D from a single view is an ill-posed problem, and current neural reconstruction methods addressing it through diffusion models still rely on scene-specific optimization, constraining their generalization capability.
GenTron: Delving Deep into Diffusion Transformers for Image and Video Generation
In this study, we explore Transformer-based diffusion models for image and video generation.
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing
In this paper, for the first time, we introduce optical flow into the attention module in the diffusion model's U-Net to address the inconsistency issue for text-to-video editing.
Learning Garment DensePose for Robust Warping in Virtual Try-On
In this work, we propose a robust warping method for virtual try-on based on a learned garment DensePose which has a direct correspondence with the person's DensePose.
Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation
Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos.
Single Stage Multi-Pose Virtual Try-On
Key to our model is a parallel flow estimation module that predicts the flow fields for both person and garment images conditioned on the target pose.
Prediction Calibration for Generalized Few-shot Semantic Segmentation
To ensure that the fused scores are not biased to either the base or novel classes, a new Transformer-based calibration module is introduced.
Generalized Few-Shot Semantic Segmentation
Semantic Segmentation
UIGR: Unified Interactive Garment Retrieval
In this paper, we propose a Unified Interactive Garment Retrieval (UIGR) framework to unify TGR and VCR.
Style-Based Global Appearance Flow for Virtual Try-On
To achieve this, a key step is garment warping which spatially aligns the target garment with the corresponding body parts in the person image.
Ranked #1 on
Virtual Try-on
on VITON
Hybrid Graph Neural Networks for Few-Shot Learning
This is because they use an instance GNN as a label propagation/classification module, which is jointly meta-learned with a feature embedding network.
Text-Based Person Search with Limited Data
Firstly, to fully utilize the existing small-scale benchmarking datasets for more discriminative feature learning, we introduce a cross-modal momentum contrastive learning framework to enrich the training data for a given mini-batch.
Ranked #10 on
Text based Person Retrieval
on CUHK-PEDES
(using extra training data)
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer
A few-shot semantic segmentation model is typically composed of a CNN encoder, a CNN decoder and a simple classifier (separating foreground and background pixels).
Disentangled Lifespan Face Synthesis
The generated face image given a target age code is expected to be age-sensitive reflected by bio-plausible transformations of shape and texture, while being identity preserving.
Context-Aware Layout to Image Generation with Enhanced Object Appearance
We argue that these are caused by the lack of context-aware object and stuff feature encoding in their generators, and location-sensitive appearance representation in their discriminators.
Ranked #1 on
Layout-to-Image Generation
on COCO-Stuff 128x128
Image Captioning through Image Transformer
Inspired by the successes in text analysis and translation, previous work have proposed the \textit{transformer} architecture for image captioning.
Understanding and Visualizing Deep Visual Saliency Models
Our analyses reveal that: 1) some visual regions (e. g. head, text, symbol, vehicle) are already encoded within various layers of the network pre-trained for object recognition, 2) using modern datasets, we find that fine-tuning pre-trained models for saliency prediction makes them favor some categories (e. g. head) over some others (e. g. text), 3) although deep models of saliency outperform classical models on natural images, the converse is true for synthetic stimuli (e. g. pop-out search arrays), an evidence of significant difference between human and data-driven saliency models, and 4) we confirm that, after-fine tuning, the change in inner-representations is mostly due to the task and not the domain shift in the data.
Human Attention in Image Captioning: Dataset and Analysis
In this work, we present a novel dataset consisting of eye movements and verbal descriptions recorded synchronously over images.
Salient Region Segmentation
Early saliency models were based on low-level hand-crafted feature derived from insights gained in neuroscience and psychophysics.
Aggregated Sparse Attention for Steering Angle Prediction
In this paper, we apply the attention mechanism to autonomous driving for steering angle prediction.
What Catches the Eye? Visualizing and Understanding Deep Saliency Models
Deep convolutional neural networks have demonstrated high performances for fixation prediction in recent years.
Deep saliency: What is learnt by a deep network about saliency?
Moreover we argue that this transformation leads to the emergence of receptive fields conceptually similar to the centre-surround filters hypothesized by early research on visual saliency.
