Search Results for author:
Found 10 papers, 6 papers with code
Mining Inter-Video Proposal Relations for Video Object Detection
Recent studies have shown that, context aggregating information from proposals in different frames can clearly enhance the performance of video object detection.
Ranked #11 on
Video Object Detection
on ImageNet VID
LongVLM: Efficient Long Video Understanding via Large Language Models
In this way, we encode video representations that incorporate both local and global information, enabling the LLM to generate comprehensive responses for long-term videos.
Video Recognition in Portrait Mode
While existing datasets mainly comprise landscape mode videos, our paper seeks to introduce portrait mode videos to the research community and highlight the unique challenges associated with this video format.
Shot2Story20K: A New Benchmark for Comprehensive Understanding of Multi-shot Videos
A human need to capture both the event in every shot and associate them together to understand the story behind it.
Ranked #1 on
video narration captioning
on Shot2Story20K
Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition
To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts.
Mask Propagation for Efficient Video Semantic Segmentation
By reusing predictions from key frames, we circumvent the need to process a large volume of video frames individually with resource-intensive segmentors, alleviating temporal redundancy and significantly reducing computational costs.
HTML: Hybrid Temporal-scale Multimodal Learning Framework for Referring Video Object Segmentation
To tackle this problem, we propose a concise Hybrid Temporal-scale Multimodal Learning (HTML) framework, which can effectively align lingual and visual features to discover core object semantics in the video, by learning multimodal interaction hierarchically from different temporal scales.
Ranked #6 on
Referring Video Object Segmentation
on Refer-YouTube-VOS
(using extra training data)
An Efficient Spatio-Temporal Pyramid Transformer for Action Detection
The task of action detection aims at deducing both the action category and localization of the start and end moment for each action instance in a long, untrimmed video.
CLMFormer: Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System
To further enhance forecasting accuracy, we introduce a memory-driven decoder.
Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition
Learning spatial-temporal relation among multiple actors is crucial for group activity recognition.
