Search Results for author:
Found 19 papers, 15 papers with code
PAPM: A Physics-aware Proxy Model for Process Systems
In the context of proxy modeling for process systems, traditional data-driven deep learning approaches frequently encounter significant challenges, such as substantial training costs induced by large amounts of data, and limited generalization capabilities.
Revisiting Neural Networks for Continual Learning: An Architectural Perspective
This paper seeks to bridge this gap between network architecture design and CL, and to present a holistic study on the impact of network architectures on CL.
Make Continual Learning Stronger via C-Flat
A general framework of C-Flat applied to all CL categories and a thorough comparison with loss minima optimizer and flat minima based CL approaches is presented in this paper, showing that our method can boost CL performance in almost all cases.
LUM-ViT: Learnable Under-sampling Mask Vision Transformer for Bandwidth Limited Optical Signal Acquisition
To tackle this hurdle, we introduce a novel approach leveraging pre-acquisition modulation to reduce the acquisition volume.
A Recipe for Scaling up Text-to-Video Generation with Text-free Videos
Following such a pipeline, we study the effect of doubling the scale of training set (i. e., video-only WebVid10M) with some randomly collected text-free videos and are encouraged to observe the performance improvement (FID from 9. 67 to 8. 19 and FVD from 484 to 441), demonstrating the scalability of our approach.
Ranked #7 on
Text-to-Video Generation
on MSR-VTT
InstructVideo: Instructing Video Diffusion Models with Human Feedback
To tackle this problem, we propose InstructVideo to instruct text-to-video diffusion models with human feedback by reward fine-tuning.
DreamVideo: Composing Your Dream Videos with Customized Subject and Motion
In motion learning, we architect a motion adapter and fine-tune it on the given videos to effectively model the target motion pattern.
I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models
By this means, I2VGen-XL can simultaneously enhance the semantic accuracy, continuity of details and clarity of generated videos.
From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation
In medical image segmentation, domain generalization poses a significant challenge due to domain shifts caused by variations in data acquisition devices and other factors.
Few-shot Action Recognition with Captioning Foundation Models
In this paper, we develop an effective plug-and-play framework called CapFSAR to exploit the knowledge of multimodal models without manually annotating text.
RLIPv2: Fast Scaling of Relational Language-Image Pre-training
In this paper, we propose RLIPv2, a fast converging model that enables the scaling of relational pre-training to large-scale pseudo-labelled scene graph data.
Ranked #1 on
Zero-Shot Human-Object Interaction Detection
on HICO-DET
(using extra training data)
ModelScope Text-to-Video Technical Report
This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (i. e., Stable Diffusion).
Ranked #9 on
Text-to-Video Generation
on MSR-VTT
VideoComposer: Compositional Video Synthesis with Motion Controllability
The pursuit of controllability as a higher standard of visual content creation has yielded remarkable progress in customizable image synthesis.
Ranked #5 on
Text-to-Video Generation
on EvalCrafter Text-to-Video (ECTV) Dataset
(using extra training data)
Refined Response Distillation for Class-Incremental Player Detection
Furthermore, we present the NBA-IOD and Volleyball-IOD datasets as the benchmark and investigate the IOD tasks of the players systematically.
Progressive Learning without Forgetting
Learning from changing tasks and sequential experience without forgetting the obtained knowledge is a challenging problem for artificial neural networks.
RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection
The task of Human-Object Interaction (HOI) detection targets fine-grained visual parsing of humans interacting with their environment, enabling a broad range of applications.
Ranked #17 on
Human-Object Interaction Detection
on HICO-DET
Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation
In this paper, we propose a response-based incremental distillation method, dubbed Elastic Response Distillation (ERD), which focuses on elastically learning responses from the classification head and the regression head.
Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics
Specifically, We propose to utilize a Verb Semantic Model (VSM) and use semantic aggregation to profit from this object-guided hierarchy.
Ranked #5 on
Human-Object Interaction Detection
on V-COCO
Spatio-Temporal Dynamic Inference Network for Group Activity Recognition
Within each interaction field, we apply DR to predict the relation matrix and DW to predict the dynamic walk offsets in a joint-processing manner, thus forming a person-specific interaction graph.
Ranked #3 on
Group Activity Recognition
on Volleyball
