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Found 29 papers, 8 papers with code
Scene Adaptive Sparse Transformer for Event-based Object Detection
However, they display inadequate sparsity and adaptability when applied to event-based object detection, since these approaches cannot balance the fine granularity of token-level sparsification and the efficiency of window-based Transformers, leading to reduced performance and efficiency.
Event-assisted Low-Light Video Object Segmentation
In the realm of video object segmentation (VOS), the challenge of operating under low-light conditions persists, resulting in notably degraded image quality and compromised accuracy when comparing query and memory frames for similarity computation.
Graph Relation Distillation for Efficient Biomedical Instance Segmentation
IGD constructs a graph representing instance features and relations, transferring these two types of knowledge by enforcing instance graph consistency.
MicroCinema: A Divide-and-Conquer Approach for Text-to-Video Generation
We present MicroCinema, a straightforward yet effective framework for high-quality and coherent text-to-video generation.
Panacea: Panoramic and Controllable Video Generation for Autonomous Driving
This work notably propels the field of autonomous driving by effectively augmenting the training dataset used for advanced BEV perception techniques.
GET: Group Event Transformer for Event-Based Vision
Event cameras are a type of novel neuromorphic sen-sor that has been gaining increasing attention.
EGVD: Event-Guided Video Deraining
In this paper, we approach video deraining by employing an event camera.
Deep Multi-Threshold Spiking-UNet for Image Processing
Furthermore, we adopt a flow-based training method to fine-tune the converted models, reducing time steps while preserving performance.
Image Captioning with Multi-Context Synthetic Data
This potential can be harnessed to create synthetic image-text pairs for training captioning models.
Paint by Example: Exemplar-based Image Editing with Diffusion Models
Language-guided image editing has achieved great success recently.
Semantics-Preserving Sketch Embedding for Face Generation
With a semantic feature matching loss for effective semantic supervision, our sketch embedding precisely conveys the semantics in the input sketches to the synthesized images.
Exploiting Rigidity Constraints for LiDAR Scene Flow Estimation
Previous LiDAR scene flow estimation methods, especially recurrent neural networks, usually suffer from structure distortion in challenging cases, such as sparse reflection and motion occlusions.
Dual Progressive Prototype Network for Generalized Zero-Shot Learning
This enables DPPN to produce visual representations with accurate attribute localization ability, which benefits the semantic-visual alignment and representation transferability.
Adaptive Domain-Specific Normalization for Generalizable Person Re-Identification
It describes unseen target domain as a combination of the known source ones, and explicitly learns domain-specific representation with target distribution to improve the model's generalization by a meta-learning pipeline.
Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning
First, DCEN leverages task labels to cluster representations of the same semantic category by cross-modal contrastive learning and exploring semantic-visual complementarity.
VAE^2: Preventing Posterior Collapse of Variational Video Predictions in the Wild
We believe that VAE$^2$ is also applicable to other stochastic sequence prediction problems where training data are lack of stochasticity.
Generating Comprehensive Data with Protocol Fuzzing for Applying Deep Learning to Detect Network Attacks
Network attacks have become a major security concern for organizations worldwide and have also drawn attention in the academics.
Cryptography and Security
Spatiotemporal Fusion in 3D CNNs: A Probabilistic View
Based on the probability space, we further generate new fusion strategies which achieve the state-of-the-art performance on four well-known action recognition datasets.
Tracking by Instance Detection: A Meta-Learning Approach
We propose a principled three-step approach to build a high-performance tracker.
Posterior-Guided Neural Architecture Search
Accordingly, a hybrid network representation is presented which enables us to leverage the Variational Dropout so that the approximation of the posterior distribution becomes fully gradient-based and highly efficient.
Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation
The proposed algorithm is empirically on two datasets with different deep neural networks.
Quality-Gated Convolutional LSTM for Enhancing Compressed Video
The past decade has witnessed great success in applying deep learning to enhance the quality of compressed video.
Temporal-Spatial Mapping for Action Recognition
We propose a video level 2D feature representation by transforming the convolutional features of all frames to a 2D feature map, referred to as VideoMap.
Ranked #51 on
Action Recognition
on UCF101
MiCT: Mixed 3D/2D Convolutional Tube for Human Action Recognition
Recent attempts use 3D convolutional neural networks (CNNs) to explore spatio-temporal information for human action recognition.
Photo Stylistic Brush: Robust Style Transfer via Superpixel-Based Bipartite Graph
With the rapid development of social network and multimedia technology, customized image and video stylization has been widely used for various social-media applications.
TenSR: Multi-Dimensional Tensor Sparse Representation
In this paper, we propose a new sparse model TenSR based on tensor for MD data representation along with the corresponding MD sparse coding and MD dictionary learning algorithms.
MARLow: A Joint Multiplanar Autoregressive and Low-Rank Approach for Image Completion
In this paper, we propose a novel multiplanar autoregressive (AR) model to exploit the correlation in cross-dimensional planes of a similar patch group collected in an image, which has long been neglected by previous AR models.
Separable Kernel for Image Deblurring
In this paper, we deal with the image deblurring problem in a completely new perspective by proposing separable kernel to represent the inherent properties of the camera and scene system.
CID: Combined Image Denoising in Spatial and Frequency Domains Using Web Images
Second, to handle heavy noise, we further propose using the denoising image to improve image registration of the retrieved Web images, 3D cube building, and the estimation of filtering parameters in the frequency domain.
