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Found 27 papers, 7 papers with code
TransMamba: Fast Universal Architecture Adaption from Transformers to Mamba
Nevertheless, training specialized subquadratic architectures from scratch for certain tasks is both resource-intensive and time-consuming.
WonderHuman: Hallucinating Unseen Parts in Dynamic 3D Human Reconstruction
In this paper, we present WonderHuman to reconstruct dynamic human avatars from a monocular video for high-fidelity novel view synthesis.
ComposeAnyone: Controllable Layout-to-Human Generation with Decoupled Multimodal Conditions
Building on the success of diffusion models, significant advancements have been made in multimodal image generation tasks.
CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On with Temporal Concatenation
Comprehensive experiments demonstrate that CatV2TON outperforms existing methods in both image and video try-on tasks, offering a versatile and reliable solution for realistic virtual try-ons across diverse scenarios.
RMAvatar: Photorealistic Human Avatar Reconstruction from Monocular Video Based on Rectified Mesh-embedded Gaussians
We introduce RMAvatar, a novel human avatar representation with Gaussian splatting embedded on mesh to learn clothed avatar from a monocular video.
LiTformer: Efficient Modeling and Analysis of High-Speed Link Transmitters Using Non-Autoregressive Transformer
High-speed serial links are fundamental to energy-efficient and high-performance computing systems such as artificial intelligence, 5G mobile and automotive, enabling low-latency and high-bandwidth communication.
A Survey of Foundation Models for Music Understanding
Music is essential in daily life, fulfilling emotional and entertainment needs, and connecting us personally, socially, and culturally.
CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
Virtual try-on methods based on diffusion models achieve realistic try-on effects but often replicate the backbone network as a ReferenceNet or use additional image encoders to process condition inputs, leading to high training and inference costs.
OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion
To address these challenges, we propose a novel unified open-vocabulary detection method called OV-DINO, which is pre-trained on diverse large-scale datasets with language-aware selective fusion in a unified framework.
Ranked #5 on
Zero-Shot Object Detection
on MSCOCO
(AP metric, using extra
training data)
SurgicalGaussian: Deformable 3D Gaussians for High-Fidelity Surgical Scene Reconstruction
However, based on implicit representation, NeRFs struggle to capture the intricate details of objects in the scene and cannot achieve real-time rendering.
ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving
ConsistentID comprises two key components: a multimodal facial prompt generator that combines facial features, corresponding facial descriptions and the overall facial context to enhance precision in facial details, and an ID-preservation network optimized through the facial attention localization strategy, aimed at preserving ID consistency in facial regions.
DRSM: efficient neural 4d decomposition for dynamic reconstruction in stationary monocular cameras
With the popularity of monocular videos generated by video sharing and live broadcasting applications, reconstructing and editing dynamic scenes in stationary monocular cameras has become a special but anticipated technology.
UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning
Recent advances in vision-language pre-training have enabled machines to perform better in multimodal object discrimination (e. g., image-text semantic alignment) and image synthesis (e. g., text-to-image generation).
Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval
Our goal in this research is to study a more realistic environment in which we can conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories.
Worst-Case Dynamic Power Distribution Network Noise Prediction Using Convolutional Neural Network
Worst-case dynamic PDN noise analysis is an essential step in PDN sign-off to ensure the performance and reliability of chips.
elBERto: Self-supervised Commonsense Learning for Question Answering
Commonsense question answering requires reasoning about everyday situations and causes and effects implicit in context.
M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining
Despite the potential of multi-modal pre-training to learn highly discriminative feature representations from complementary data modalities, current progress is being slowed by the lack of large-scale modality-diverse datasets.
Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining
In this paper, we investigate a more realistic setting that aims to perform weakly-supervised multi-modal instance-level product retrieval among fine-grained product categories.
Pinpointing the Memory Behaviors of DNN Training
The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators.
A Unified Joint Maximum Mean Discrepancy for Domain Adaptation
From the revealed unified JMMD, we illustrate that JMMD degrades the feature-label dependence (discriminability) that benefits to classification, and it is sensitive to the label distribution shift when the label kernel is the weighted class conditional one.
Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection
We propose to dynamically learn the collaborative similarity structure, and further integrate it with the ultimate feature selection into a unified framework.
Understanding over-parameterized deep networks by geometrization
This can be regarded as a strong support of our proposal that geometrization is not only the bible for physics, it is also the key idea to understand deep learning systems.
Background subtraction on depth videos with convolutional neural networks
Background subtraction is a significant component of computer vision systems.
Geometrization of deep networks for the interpretability of deep learning systems
By comparing the geometry of image matching and deep networks, we show that geometrization of deep networks can be used to understand existing deep learning systems and it may also help to solve the interpretability problem of deep learning systems.
Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge
By analyzing the characteristics of layers in DNNs, an auto-tuning neural network quantization framework for collaborative inference is proposed.
Demystifying AlphaGo Zero as AlphaGo GAN
The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a worldwide discussion of the future of our human society with a mixed mood of hope, anxiousness, excitement and fear.
How deep learning works --The geometry of deep learning
Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective.
