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Found 6 papers, 4 papers with code
TDViT: Temporal Dilated Video Transformer for Dense Video Tasks
Deep video models, for example, 3D CNNs or video transformers, have achieved promising performance on sparse video tasks, i. e., predicting one result per video.
Efficient One-stage Video Object Detection by Exploiting Temporal Consistency
Based on the analysis, we present a simple yet efficient framework to address the computational bottlenecks and achieve efficient one-stage VOD by exploiting the temporal consistency in video frames.
Spatio-temporal Prompting Network for Robust Video Feature Extraction
Then, these video prompts are prepended to the patch embeddings of the current frame as the updated input for video feature extraction.
MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection
However, we argue that these memory structures are not efficient or sufficient because of two implied operations: (1) concatenating all features in memory for enhancement, leading to a heavy computational cost; (2) frame-wise memory updating, preventing the memory from capturing more temporal information.
Neural Sign Actors: A diffusion model for 3D sign language production from text
Sign Languages (SL) serve as the primary mode of communication for the Deaf and Hard of Hearing communities.
Focus On What's Important: Self-Attention Model for Human Pose Estimation
In this work, we proposed a convolutional network architecture combined with the novel attention model.
