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Found 19 papers, 9 papers with code
RegionGPT: Towards Region Understanding Vision Language Model
Vision language models (VLMs) have experienced rapid advancements through the integration of large language models (LLMs) with image-text pairs, yet they struggle with detailed regional visual understanding due to limited spatial awareness of the vision encoder, and the use of coarse-grained training data that lacks detailed, region-specific captions.
Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modeling
For the first stage, we propose a diffusion-based motion field predictor, which focuses on deducing the trajectories of the reference image's pixels.
Resilient Practical Test-Time Adaptation: Soft Batch Normalization Alignment and Entropy-driven Memory Bank
Test-time domain adaptation effectively adjusts the source domain model to accommodate unseen domain shifts in a target domain during inference.
CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields
Neural Radiance Fields (NeRF) have the potential to be a major representation of media.
TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses
3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots.
VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation
We first propose a TRi-frame Optical Flow (TROF) module that estimates bi-directional optical flows for the center frame in a three-frame manner.
FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation
FlowFormer introduces a transformer architecture into optical flow estimation and achieves state-of-the-art performance.
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
Physics-informed neural networks (PINNs) have attracted significant attention for solving partial differential equations (PDEs) in recent years because they alleviate the curse of dimensionality that appears in traditional methods.
Hard Gate Knowledge Distillation -- Leverage Calibration for Robust and Reliable Language Model
The question is answered in our work with the concept of model calibration; we view a teacher model not only as a source of knowledge but also as a gauge to detect miscalibration of a student.
Adaptive Label Smoothing with Self-Knowledge in Natural Language Generation
Furthermore, inspired by recent work in bridging label smoothing and knowledge distillation, our work utilizes self-knowledge as a prior label distribution in softening target labels, and presents theoretical support for the regularization effect by knowledge distillation and the dynamic smoothing parameter.
NeuralMarker: A Framework for Learning General Marker Correspondence
We tackle the problem of estimating correspondences from a general marker, such as a movie poster, to an image that captures such a marker.
Learning Degradation Representations for Image Deblurring
With the integration, MSDI-Net can handle various and complicated blurry patterns adaptively.
Ranked #13 on
Image Deblurring
on GoPro
A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift
In this study, we propose a simple yet effective framework for video restoration.
Ranked #1 on
Deblurring
on GoPro
(using extra training data)
MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection
Accurate and reliable 3D detection is vital for many applications including autonomous driving vehicles and service robots.
FlowFormer: A Transformer Architecture for Optical Flow
We introduce optical Flow transFormer, dubbed as FlowFormer, a transformer-based neural network architecture for learning optical flow.
Ranked #1 on
Optical Flow Estimation
on Sintel-final
Adaptive Label Smoothing with Self-Knowledge
Overconfidence has been shown to impair generalization and calibration of a neural network.
Self-distillation with Batch Knowledge Ensembling Improves ImageNet Classification
In this way, our BAKE framework achieves online knowledge ensembling across multiple samples with only a single network.
LIFE: Lighting Invariant Flow Estimation
However, local image contents are inevitably ambiguous and error-prone during the cross-image feature matching process, which hinders downstream tasks.
Understanding Top-k Sparsification in Distributed Deep Learning
Distributed stochastic gradient descent (SGD) algorithms are widely deployed in training large-scale deep learning models, while the communication overhead among workers becomes the new system bottleneck.
