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Found 31 papers, 12 papers with code
Boosting Diffusion Models with Moving Average Sampling in Frequency Domain
Diffusion models have recently brought a powerful revolution in image 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.
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.
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.
Dual Vision Transformer
Dual-ViT is henceforth able to reduce the computational complexity without compromising much accuracy.
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 #212 on
Image Classification
on ImageNet
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.
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.
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.
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.
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.
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 #288 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.
Pre-training for Video Captioning Challenge 2020 Summary
The Pre-training for Video Captioning Challenge 2020 Summary: results and challenge participants' technical reports.
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.
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.
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.
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.
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.
Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning
Moreover, the inherently recurrent dependency in RNN prevents parallelization within a sequence during training and therefore limits the computations.
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.
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.
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.
