Search Results for author:
Found 52 papers, 32 papers with code
More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning
Since those regularization strategies are mostly associated with classifier outputs, we propose a MUlti-Classifier (MUC) incremental learning paradigm that integrates an ensemble of auxiliary classifiers to estimate more effective regularization constraints.
MoTrans: Customized Motion Transfer with Text-driven Video Diffusion Models
They often fail to effectively decouple motion and the appearance in the limited reference videos, thereby weakening the modeling capability of motion patterns.
DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image Inpainting
Extensive experiments demonstrate that DreamMix effectively balances identity preservation and attribute editability across various application scenarios, including object insertion, attribute editing, and small object inpainting.
OASIS: Open Agent Social Interaction Simulations with One Million Agents
There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i. e., X, Reddit) with more realistic large language model (LLM) agents, thereby allowing for a more nuanced study of complex systems.
EvSign: Sign Language Recognition and Translation with Streaming Events
Sign language is one of the most effective communication tools for people with hearing difficulties.
SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning
Parameter-efficient transfer learning (PETL) has emerged as a flourishing research field for adapting large pre-trained models to downstream tasks, greatly reducing trainable parameters while grappling with memory challenges during fine-tuning.
EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Images
Capitalizing on the high temporal resolution and dynamic range offered by the event camera, we leverage the event streams to explicitly model the formation process of motion-blurred images and guide the deblurring reconstruction of 3D-GS.
CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models
In this work, we propose CharacterFactory, a framework that allows sampling new characters with consistent identities in the latent space of GANs for diffusion models.
Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases
Moreover, with our CODA-LM, we build CODA-VLM, a new driving LVLM surpassing all open-sourced counterparts on CODA-LM.
StableIdentity: Inserting Anybody into Anywhere at First Sight
We believe that the proposed StableIdentity is an important step to unify image, video, and 3D customized generation models.
TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion Models
Despite remarkable achievements in video synthesis, achieving granular control over complex dynamics, such as nuanced movement among multiple interacting objects, still presents a significant hurdle for dynamic world modeling, compounded by the necessity to manage appearance and disappearance, drastic scale changes, and ensure consistency for instances across frames.
GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models
Extensive experiments have shown that GenTKG outperforms conventional methods of temporal relational forecasting with low computation resources using extremely limited training data as few as 16 samples.
UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
Parameter-efficient transfer learning (PETL), i. e., fine-tuning a small portion of parameters, is an effective strategy for adapting pre-trained models to downstream domains.
Neural Image Re-Exposure
In this work, we aim to re-expose the captured photo in post-processing to provide a more flexible way of addressing those issues within a unified framework.
Ranked #5 on
Deblurring
on GoPro
(using extra training data)
Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion
We train our model with a masking strategy to convert TKGC task into a masked token prediction task, which can leverage the semantic information in pre-trained language models.
GM-NeRF: Learning Generalizable Model-based Neural Radiance Fields from Multi-view Images
The results demonstrate that our approach outperforms state-of-the-art methods in terms of novel view synthesis and geometric reconstruction.
Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation
To fully exploit information with event streams to detect objects, a dual-memory aggregation network (DMANet) is proposed to leverage both long and short memory along event streams to aggregate effective information for object detection.
Compression-Aware Video Super-Resolution
Videos stored on mobile devices or delivered on the Internet are usually in compressed format and are of various unknown compression parameters, but most video super-resolution (VSR) methods often assume ideal inputs resulting in large performance gap between experimental settings and real-world applications.
AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement
They adopt a sub-optimal uniform sampling point allocation, limiting the expressiveness of the learned LUTs since the (tri-)linear interpolation between uniform sampling points in the LUT transform might fail to model local non-linearities of the color transform.
Ranked #2 on
Photo Retouching
on MIT-Adobe 5k
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling
Instead of directly feeding consecutive frames into a VSR model, we propose to compute the temporal difference between frames and divide those pixels into two subsets according to the level of difference.
FoV-Net: Field-of-View Extrapolation Using Self-Attention and Uncertainty
The ability to make educated predictions about their surroundings, and associate them with certain confidence, is important for intelligent systems, like autonomous vehicles and robots.
TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation
Since event camera is a novel sensor, its potential has not been fulfilled due to the lack of processing algorithms.
Learning Enriched Illuminants for Cross and Single Sensor Color Constancy
Color constancy aims to restore the constant colors of a scene under different illuminants.
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation
One popular solution to this challenging task is self-training, which selects high-scoring predictions on target samples as pseudo labels for training.
Ranked #9 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
Transformer-based Network for RGB-D Saliency Detection
TFFM conducts a sufficient feature fusion by integrating features from multiple scales and two modalities over all positions simultaneously.
Motion Deblurring with Real Events
In this paper, we propose an end-to-end learning framework for event-based motion deblurring in a self-supervised manner, where real-world events are exploited to alleviate the performance degradation caused by data inconsistency.
Wavelet-Based Network For High Dynamic Range Imaging
The frequency-guided upsampling module reconstructs details from multiple frequency-specific components with rich details.
T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation
Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help improve performance on target domain.
Ranked #2 on
Unsupervised Domain Adaptation
on PACS
Animatable Neural Radiance Fields from Monocular RGB Videos
We present animatable neural radiance fields (animatable NeRF) for detailed human avatar creation from monocular videos.
Multi-Target Domain Adaptation with Collaborative Consistency Learning
To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights.
Ranked #4 on
Domain Adaptation
on GTAV to Cityscapes+Mapillary
Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation
To address the task of SSDA, a novel framework based on dual-level domain mixing is proposed.
Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation
Multi-source unsupervised domain adaptation~(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain.
Ranked #1 on
Domain Adaptation
on GTA5+Synscapes to Cityscapes
Multi-Source Unsupervised Domain Adaptation
Semantic Segmentation
+1
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
In this paper, we present a very simple yet effective method named Neighbor2Neighbor to train an effective image denoising model with only noisy images.
Revisiting Temporal Modeling for Video Super-resolution
Video super-resolution plays an important role in surveillance video analysis and ultra-high-definition video display, which has drawn much attention in both the research and industrial communities.
Ranked #1 on
Video Super-Resolution
on SPMCS - 4x upscaling
Video Super-Resolution with Recurrent Structure-Detail Network
Most video super-resolution methods super-resolve a single reference frame with the help of neighboring frames in a temporal sliding window.
Video Super-resolution with Temporal Group Attention
Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention.
Learning to Select Base Classes for Few-shot Classification
Few-shot learning has attracted intensive research attention in recent years.
Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem
This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints.
MMD GAN with Random-Forest Kernels
In this paper, we propose a novel kind of kernel, random forest kernel, to enhance the empirical performance of MMD GAN.
Unsupervised Image Super-Resolution with an Indirect Supervised Path
The task of single image super-resolution (SISR) aims at reconstructing a high-resolution (HR) image from a low-resolution (LR) image.
Efficient Residual Dense Block Search for Image Super-Resolution
Focusing on this issue, we propose an efficient residual dense block search algorithm with multiple objectives to hunt for fast, lightweight and accurate networks for image super-resolution.
A continual learning survey: Defying forgetting in classification tasks
Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase.
Co-Evolutionary Compression for Unpaired Image Translation
Generative adversarial networks (GANs) have been successfully used for considerable computer vision tasks, especially the image-to-image translation.
Video Generation from Single Semantic Label Map
This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process.
Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency
Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.
Super-Resolution with Deep Adaptive Image Resampling
In this work, we revisit the more traditional interpolation-based methods, that were popular before, now with the help of deep learning.
Error Correction for Dense Semantic Image Labeling
Pixelwise semantic image labeling is an important, yet challenging, task with many applications.
Pose Guided Person Image Generation
This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose.
Ranked #3 on
Gesture-to-Gesture Translation
on Senz3D
Dynamic Filter Networks
In a traditional convolutional layer, the learned filters stay fixed after training.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Guiding the Long-Short Term Memory Model for Image Caption Generation
In this work we focus on the problem of image caption generation.
Towards Automatic Image Editing: Learning to See another You
Learning the distribution of images in order to generate new samples is a challenging task due to the high dimensionality of the data and the highly non-linear relations that are involved.
Guiding Long-Short Term Memory for Image Caption Generation
In this work we focus on the problem of image caption generation.
