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Found 13 papers, 10 papers with code
VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos
Recently, many long video-language understanding approaches have leveraged the reasoning capabilities of Large Language Models (LLMs) to perform long video QA, transforming videos into densely sampled frame captions, and asking LLMs to respond to text queries over captions.
Ranked #2 on
Zero-Shot Video Question Answer
on IntentQA
DAM: Dynamic Adapter Merging for Continual Video QA Learning
Our DAM model outperforms prior state-of-the-art continual learning approaches by 9. 1% while exhibiting 1. 9% less forgetting on 6 VidQA datasets spanning various domains.
Large Language Models in Cybersecurity: State-of-the-Art
The rise of Large Language Models (LLMs) has revolutionized our comprehension of intelligence bringing us closer to Artificial Intelligence.
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
Unified Coarse-to-Fine Alignment for Video-Text Retrieval
Specifically, our model captures the cross-modal similarity information at different granularity levels.
Ranked #11 on
Video Retrieval
on MSR-VTT
LoCoNet: Long-Short Context Network for Active Speaker Detection
These two contexts are complementary to each other and can help infer the active speaker.
VindLU: A Recipe for Effective Video-and-Language Pretraining
Furthermore, our model also obtains state-of-the-art video question-answering results on ActivityNet-QA, MSRVTT-QA, MSRVTT-MC and TVQA.
Ranked #2 on
Video Retrieval
on Condensed Movies
(using extra training data)
TALLFormer: Temporal Action Localization with a Long-memory Transformer
To address these issues, we propose TALLFormer, a memory-efficient and end-to-end trainable Temporal Action Localization Transformer with Long-term memory.
Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models
We propose a memory efficient method, named Stochastic Backpropagation (SBP), for training deep neural networks on videos.
Using Single-Trial Representational Similarity Analysis with EEG to track semantic similarity in emotional word processing
Electroencephalography (EEG) is a powerful non-invasive brain imaging technique with a high temporal resolution that has seen extensive use across multiple areas of cognitive science research.
Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload
The use of deep learning models for forecasting the resource consumption patterns of SQL queries have recently been a popular area of study.
Learning Directional Feature Maps for Cardiac MRI Segmentation
Cardiac MRI segmentation plays a crucial role in clinical diagnosis for evaluating personalized cardiac performance parameters.
Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy
To better unveil this implicit relationship and thus facilitate metamaterial design, we propose to represent metamaterials and model the inverse design problem in a probabilistically generative manner.
Optics
