Search Results for author:
Found 75 papers, 32 papers with code
Unleashing Text-to-Image Diffusion Prior for Zero-Shot Image Captioning
Additionally, to facilitate the model training with synthetic data, a novel CLIP-weighted cross-entropy loss is devised to prioritize the high-quality image-text pairs over the low-quality counterparts.
Improving Text-guided Object Inpainting with Semantic Pre-inpainting
In this paper, we propose to decompose the typical single-stage object inpainting into two cascaded processes: 1) semantic pre-inpainting that infers the semantic features of desired objects in a multi-modal feature space; 2) high-fieldity object generation in diffusion latent space that pivots on such inpainted semantic features.
Improving Virtual Try-On with Garment-focused Diffusion Models
To address this, we shape a new Diffusion model, namely GarDiff, which triggers the garment-focused diffusion process with amplified guidance of both basic visual appearance and detailed textures (i. e., high-frequency details) derived from the given garment.
Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models
Despite having tremendous progress in image-to-3D generation, existing methods still struggle to produce multi-view consistent images with high-resolution textures in detail, especially in the paradigm of 2D diffusion that lacks 3D awareness.
DreamMesh: Jointly Manipulating and Texturing Triangle Meshes for Text-to-3D Generation
In the fine stage, DreamMesh jointly manipulates the mesh and refines the texture map, leading to high-quality triangle meshes with high-fidelity textured materials.
Prompt Refinement with Image Pivot for Text-to-Image Generation
Such a prompt refinement process is analogous to translating the prompt from "user languages" into "system languages".
Boosting Diffusion Models with Moving Average Sampling in Frequency Domain
Diffusion models have recently brought a powerful revolution in image generation.
TRIP: Temporal Residual Learning with Image Noise Prior for Image-to-Video Diffusion Models
Next, TRIP executes a residual-like dual-path scheme for noise prediction: 1) a shortcut path that directly takes image noise prior as the reference noise of each frame to amplify the alignment between the first frame and subsequent frames; 2) a residual path that employs 3D-UNet over noised video and static image latent codes to enable inter-frame relational reasoning, thereby easing the learning of the residual noise for each frame.
VP3D: Unleashing 2D Visual Prompt for Text-to-3D Generation
In this work, we introduce a novel Visual Prompt-guided text-to-3D diffusion model (VP3D) that explicitly unleashes the visual appearance knowledge in 2D visual prompt to boost text-to-3D generation.
SD-DiT: Unleashing the Power of Self-supervised Discrimination in Diffusion Transformer
Despite this progress, mask strategy still suffers from two inherent limitations: (a) training-inference discrepancy and (b) fuzzy relations between mask reconstruction & generative diffusion process, resulting in sub-optimal training of DiT.
HIRI-ViT: Scaling Vision Transformer with High Resolution Inputs
Instead, we present a new hybrid backbone with HIgh-Resolution Inputs (namely HIRI-ViT), that upgrades prevalent four-stage ViT to five-stage ViT tailored for high-resolution inputs.
Control3D: Towards Controllable Text-to-3D Generation
In particular, a 2D conditioned diffusion model (ControlNet) is remoulded to guide the learning of 3D scene parameterized as NeRF, encouraging each view of 3D scene aligned with the given text prompt and hand-drawn sketch.
ControlStyle: Text-Driven Stylized Image Generation Using Diffusion Priors
To achieve this, we present a new diffusion model (ControlStyle) via upgrading a pre-trained text-to-image model with a trainable modulation network enabling more conditions of text prompts and style images.
3DStyle-Diffusion: Pursuing Fine-grained Text-driven 3D Stylization with 2D Diffusion Models
In this work, we propose a new 3DStyle-Diffusion model that triggers fine-grained stylization of 3D meshes with additional controllable appearance and geometric guidance from 2D Diffusion models.
Transforming Radiance Field with Lipschitz Network for Photorealistic 3D Scene Stylization
Simply coupling NeRF with photorealistic style transfer (PST) will result in cross-view inconsistency and degradation of stylized view syntheses.
Modality-Agnostic Debiasing for Single Domain Generalization
We validate the superiority of our MAD in a variety of single-DG scenarios with different modalities, including recognition on 1D texts, 2D images, 3D point clouds, and semantic segmentation on 2D images.
Learning Neural Implicit Surfaces with Object-Aware Radiance Fields
Then, we build the geometric correspondence between 2D planes and 3D meshes by rasterization, and project the estimated object regions into 3D explicit object surfaces by aggregating the object information across multiple views.
ObjectFusion: Multi-modal 3D Object Detection with Object-Centric Fusion
Recent progress on multi-modal 3D object detection has featured BEV (Bird-Eye-View) based fusion, which effectively unifies both LiDAR point clouds and camera images in a shared BEV space.
Learning To Generate Language-Supervised and Open-Vocabulary Scene Graph Using Pre-Trained Visual-Semantic Space
Specifically, cheap scene graph supervision data can be easily obtained by parsing image language descriptions into semantic graphs.
HGNet: Learning Hierarchical Geometry From Points, Edges, and Surfaces
Next, as every two neighbor edges compose a surface, we obtain the edge-level representation of each anchor edge via surface-to-edge aggregation over all neighbor surfaces.
Semantic-Conditional Diffusion Networks for Image Captioning
The rich semantics are further regarded as semantic prior to trigger the learning of Diffusion Transformer, which produces the output sentence in a diffusion process.
3D Cascade RCNN: High Quality Object Detection in Point Clouds
Recent progress on 2D object detection has featured Cascade RCNN, which capitalizes on a sequence of cascade detectors to progressively improve proposal quality, towards high-quality object detection.
SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement
In this paper, we propose a novel deep architecture tailored for 3D point cloud applications, named as SPE-Net.
Dynamic Temporal Filtering in Video Models
The pre-determined kernel size severely limits the temporal receptive fields and the fixed weights treat each spatial location across frames equally, resulting in sub-optimal solution for long-range temporal modeling in natural scenes.
Out-of-Distribution Detection with Hilbert-Schmidt Independence Optimization
Existing works attempt to solve the problem by explicitly imposing uncertainty on classifiers when OOD inputs are exposed to the classifier during training.
Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning
Motivated by the wavelet theory, we construct a new Wavelet Vision Transformer (\textbf{Wave-ViT}) that formulates the invertible down-sampling with wavelet transforms and self-attention learning in a unified way.
Ranked #226 on
Image Classification
on ImageNet
Dual Vision Transformer
Dual-ViT is henceforth able to reduce the computational complexity without compromising much accuracy.
Comprehending and Ordering Semantics for Image Captioning
In this paper, we propose a new recipe of Transformer-style structure, namely Comprehending and Ordering Semantics Networks (COS-Net), that novelly unifies an enriched semantic comprehending and a learnable semantic ordering processes into a single architecture.
Stand-Alone Inter-Frame Attention in Video Models
In this paper, we present a new recipe of inter-frame attention block, namely Stand-alone Inter-Frame Attention (SIFA), that novelly delves into the deformation across frames to estimate local self-attention on each spatial location.
Ranked #14 on
Action Recognition
on Something-Something V1
Exploring Structure-aware Transformer over Interaction Proposals for Human-Object Interaction Detection
Such design decomposes the process of HOI set prediction into two subsequent phases, i. e., an interaction proposal generation is first performed, and then followed by transforming the non-parametric interaction proposals into HOI predictions via a structure-aware Transformer.
Ranked #3 on
Human-Object Interaction Detection
on V-COCO
Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation
This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track: The No Interaction track targets for learning policies from pre-collected demonstration trajectories.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Representing Videos as Discriminative Sub-graphs for Action Recognition
For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition.
Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training
Vision-language pre-training has been an emerging and fast-developing research topic, which transfers multi-modal knowledge from rich-resource pre-training task to limited-resource downstream tasks.
Smart Director: An Event-Driven Directing System for Live Broadcasting
To our best knowledge, our system is the first end-to-end automated directing system for multi-camera sports broadcasting, completely driven by the semantic understanding of sports events.
Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration
Our work reveals a structured shortcoming of the existing mainstream self-supervised learning methods.
Transferrable Contrastive Learning for Visual Domain Adaptation
From this point, we present a particular paradigm of self-supervised learning tailored for domain adaptation, i. e., Transferrable Contrastive Learning (TCL), which links the SSL and the desired cross-domain transferability congruently.
CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning
In this work, we quantitatively analyze the sub-text problem and present a simple yet effective design, COntrastive RElation (CORE) module, to mitigate that issue.
CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising
BERT-type structure has led to the revolution of vision-language pre-training and the achievement of state-of-the-art results on numerous vision-language downstream tasks.
A Style and Semantic Memory Mechanism for Domain Generalization
Correspondingly, we also propose a novel "jury" mechanism, which is particularly effective in learning useful semantic feature commonalities among domains.
Ranked #45 on
Domain Generalization
on PACS
X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
Nevertheless, there has not been an open-source codebase in support of training and deploying numerous neural network models for cross-modal analytics in a unified and modular fashion.
A Low Rank Promoting Prior for Unsupervised Contrastive Learning
In this paper, we construct a novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning, referred to as LORAC.
Contextual Transformer Networks for Visual Recognition
Such design fully capitalizes on the contextual information among input keys to guide the learning of dynamic attention matrix and thus strengthens the capacity of visual representation.
Ranked #309 on
Image Classification
on ImageNet
Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network
Despite having impressive vision-language (VL) pretraining with BERT-based encoder for VL understanding, the pretraining of a universal encoder-decoder for both VL understanding and generation remains challenging.
Joint Contrastive Learning with Infinite Possibilities
This paper explores useful modifications of the recent development in contrastive learning via novel probabilistic modeling.
SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning
In this paper, we compose a trilogy of exploring the basic and generic supervision in the sequence from spatial, spatiotemporal and sequential perspectives.
Pre-training for Video Captioning Challenge 2020 Summary
The Pre-training for Video Captioning Challenge 2020 Summary: results and challenge participants' technical reports.
Single Shot Video Object Detector
Single shot detectors that are potentially faster and simpler than two-stage detectors tend to be more applicable to object detection in videos.
Auto-captions on GIF: A Large-scale Video-sentence Dataset for Vision-language Pre-training
In this work, we present Auto-captions on GIF, which is a new large-scale pre-training dataset for generic video understanding.
Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation
A clustering branch is capitalized on to ensure that the learnt representation preserves such underlying structure by matching the estimated assignment distribution over clusters to the inherent cluster distribution for each target sample.
Learning a Unified Sample Weighting Network for Object Detection
To this end, we devise a general loss function to cover most region-based object detectors with various sampling strategies, and then based on it we propose a unified sample weighting network to predict a sample's task weights.
X-Linear Attention Networks for Image Captioning
Recent progress on fine-grained visual recognition and visual question answering has featured Bilinear Pooling, which effectively models the 2$^{nd}$ order interactions across multi-modal inputs.
Ranked #21 on
Image Captioning
on COCO Captions
Multi-Source Domain Adaptation and Semi-Supervised Domain Adaptation with Focus on Visual Domain Adaptation Challenge 2019
Semi-Supervised Domain Adaptation: For this task, we adopt a standard self-learning framework to construct a classifier based on the labeled source and target data, and generate the pseudo labels for unlabeled target data.
Deep Metric Learning with Density Adaptivity
The problem of distance metric learning is mostly considered from the perspective of learning an embedding space, where the distances between pairs of examples are in correspondence with a similarity metric.
Hierarchy Parsing for Image Captioning
It is always well believed that parsing an image into constituent visual patterns would be helpful for understanding and representing an image.
Mocycle-GAN: Unpaired Video-to-Video Translation
Unsupervised image-to-image translation is the task of translating an image from one domain to another in the absence of any paired training examples and tends to be more applicable to practical applications.
Relation Distillation Networks for Video Object Detection
In this paper, we introduce a new design to capture the interactions across the objects in spatio-temporal context.
daBNN: A Super Fast Inference Framework for Binary Neural Networks on ARM devices
It is always well believed that Binary Neural Networks (BNNs) could drastically accelerate the inference efficiency by replacing the arithmetic operations in float-valued Deep Neural Networks (DNNs) with bit-wise operations.
Convolutional Auto-encoding of Sentence Topics for Image Paragraph Generation
A valid question is how to encapsulate such gists/topics that are worthy of mention from an image, and then describe the image from one topic to another but holistically with a coherent structure.
vireoJD-MM at Activity Detection in Extended Videos
This notebook paper presents an overview and comparative analysis of our system designed for activity detection in extended videos (ActEV-PC) in ActivityNet Challenge 2019.
Trimmed Action Recognition, Dense-Captioning Events in Videos, and Spatio-temporal Action Localization with Focus on ActivityNet Challenge 2019
This notebook paper presents an overview and comparative analysis of our systems designed for the following three tasks in ActivityNet Challenge 2019: trimmed action recognition, dense-captioning events in videos, and spatio-temporal action localization.
Exploring Object Relation in Mean Teacher for Cross-Domain Detection
The whole architecture is then optimized with three consistency regularizations: 1) region-level consistency to align the region-level predictions between teacher and student, 2) inter-graph consistency for matching the graph structures between teacher and student, and 3) intra-graph consistency to enhance the similarity between regions of same class within the graph of student.
Pointing Novel Objects in Image Captioning
Image captioning has received significant attention with remarkable improvements in recent advances.
Transferrable Prototypical Networks for Unsupervised Domain Adaptation
Specifically, we present Transferrable Prototypical Networks (TPN) for adaptation such that the prototypes for each class in source and target domains are close in the embedding space and the score distributions predicted by prototypes separately on source and target data are similar.
Exploring Visual Relationship for Image Captioning
Technically, we build graphs over the detected objects in an image based on their spatial and semantic connections.
Memory Matching Networks for One-Shot Image Recognition
In this paper, we introduce the new ideas of augmenting Convolutional Neural Networks (CNNs) with Memory and learning to learn the network parameters for the unlabelled images on the fly in one-shot learning.
To Create What You Tell: Generating Videos from Captions
In this paper, we present a novel Temporal GANs conditioning on Captions, namely TGANs-C, in which the input to the generator network is a concatenation of a latent noise vector and caption embedding, and then is transformed into a frame sequence with 3D spatio-temporal convolutions.
Deep Semantic Hashing with Generative Adversarial Networks
Specifically, a novel deep semantic hashing with GANs (DSH-GANs) is presented, which mainly consists of four components: a deep convolution neural networks (CNN) for learning image representations, an adversary stream to distinguish synthetic images from real ones, a hash stream for encoding image representations to hash codes and a classification stream.
Jointly Localizing and Describing Events for Dense Video Captioning
A valid question is how to temporally localize and then describe events, which is known as "dense video captioning."
Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects
Image captioning often requires a large set of training image-sentence pairs.
Video Captioning with Transferred Semantic Attributes
Automatically generating natural language descriptions of videos plays a fundamental challenge for computer vision community.
Boosting Image Captioning with Attributes
Automatically describing an image with a natural language has been an emerging challenge in both fields of computer vision and natural language processing.
Semi-Supervised Domain Adaptation With Subspace Learning for Visual Recognition
In many real-world applications, we are often facing the problem of cross domain learning, i. e., to borrow the labeled data or transfer the already learnt knowledge from a source domain to a target domain.
Jointly Modeling Embedding and Translation to Bridge Video and Language
Our proposed LSTM-E consists of three components: a 2-D and/or 3-D deep convolutional neural networks for learning powerful video representation, a deep RNN for generating sentences, and a joint embedding model for exploring the relationships between visual content and sentence semantics.
