Search Results for author:
Found 19 papers, 9 papers with code
MAGREF: Masked Guidance for Any-Reference Video Generation
Video generation has made substantial strides with the emergence of deep generative models, especially diffusion-based approaches.
CINEMA: Coherent Multi-Subject Video Generation via MLLM-Based Guidance
By leveraging MLLM to interpret subject relationships, our method facilitates scalability, enabling the use of large and diverse datasets for training.
VideoWorld: Exploring Knowledge Learning from Unlabeled Videos
This work explores whether a deep generative model can learn complex knowledge solely from visual input, in contrast to the prevalent focus on text-based models like large language models (LLMs).
I2VGuard: Safeguarding Images against Misuse in Diffusion-based Image-to-Video Models
This paper proposes a novel approach that applies imperceptible perturbations on images to degrade the quality of the generated videos, thereby protecting images from misuse in white-box image-to-video diffusion models.
DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning
Our method is compatible with a range of text encoders.
ARLON: Boosting Diffusion Transformers with Autoregressive Models for Long Video Generation
Text-to-video models have recently undergone rapid and substantial advancements.
I2V-Adapter: A General Image-to-Video Adapter for Diffusion Models
I2V-Adapter adeptly propagates the unnoised input image to subsequent noised frames through a cross-frame attention mechanism, maintaining the identity of the input image without any changes to the pretrained T2V model.
Hulk: A Universal Knowledge Translator for Human-Centric Tasks
Human-centric perception tasks, e. g., pedestrian detection, skeleton-based action recognition, and pose estimation, have wide industrial applications, such as metaverse and sports analysis.
Ranked #1 on
Pedestrian Image Caption
on CUHK-PEDES
StableVideo: Text-driven Consistency-aware Diffusion Video Editing
In this paper, we tackle this problem by introducing temporal dependency to existing text-driven diffusion models, which allows them to generate consistent appearance for the edited objects.
MovieChat: From Dense Token to Sparse Memory for Long Video Understanding
Recently, integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific pre-defined vision tasks.
Multiple-choice
Video-based Generative Performance Benchmarking (Consistency)
+11
Alignment-guided Temporal Attention for Video Action Recognition
Temporal modeling is crucial for various video learning tasks.
Rethinking Minimal Sufficient Representation in Contrastive Learning
It significantly improves the performance of several classic contrastive learning models in downstream tasks.
Cross-Stage Transformer for Video Learning
By inserting the proposed cross-stage mechanism in existing spatial and temporal transformer blocks, we build a separable transformer network for video learning based on ViT structure, in which self-attentions and features are progressively aggregated from one block to the next.
What Makes for Good Representations for Contrastive Learning
Therefore, we assume the task-relevant information that is not shared between views can not be ignored and theoretically prove that the minimal sufficient representation in contrastive learning is not sufficient for the downstream tasks, which causes performance degradation.
Self-Supervised Video Representation Learning with Meta-Contrastive Network
Our method contains two training stages based on model-agnostic meta learning (MAML), each of which consists of a contrastive branch and a meta branch.
Ranked #28 on
Self-Supervised Action Recognition
on HMDB51
SSAN: Separable Self-Attention Network for Video Representation Learning
However, spatial correlations and temporal correlations represent different contextual information of scenes and temporal reasoning.
In Defense of the Classification Loss for Person Re-Identification
The recent research for person re-identification has been focused on two trends.
An End-to-End Compression Framework Based on Convolutional Neural Networks
The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high-quality in the decoding end.
