Search Results for author:
Found 20 papers, 8 papers with code
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding
However, existing LLM-based large multimodal models (e. g., Video-LLaMA, VideoChat) can only take in a limited number of frames for short video understanding.
Ranked #1 on
Video Classification
on COIN
OmniVid: A Generative Framework for Universal Video Understanding
The core of video understanding tasks, such as recognition, captioning, and tracking, is to automatically detect objects or actions in a video and analyze their temporal evolution.
To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning
Existing visual instruction tuning methods typically prompt large language models with textual descriptions to generate instruction-following data.
Ranked #33 on
Visual Question Answering
on MM-Vet
Chop & Learn: Recognizing and Generating Object-State Compositions
Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions.
Towards Scalable Neural Representation for Diverse Videos
Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images, and have been recently applied to encode videos (e. g., NeRV, E-NeRV).
Align and Attend: Multimodal Summarization with Dual Contrastive Losses
The goal of multimodal summarization is to extract the most important information from different modalities to form output summaries.
Ranked #3 on
Supervised Video Summarization
on SumMe
Extractive Text Summarization
Supervised Video Summarization
CNeRV: Content-adaptive Neural Representation for Visual Data
We match the performance of NeRV, a state-of-the-art implicit neural representation, on the reconstruction task for frames seen during training while far surpassing for frames that are skipped during training (unseen images).
Learning Semantic Correspondence with Sparse Annotations
In this paper, we aim to address the challenge of label sparsity in semantic correspondence by enriching supervision signals from sparse keypoint annotations.
ColdGuess: A General and Effective Relational Graph Convolutional Network to Tackle Cold Start Cases
It outperforms the lightgbm2 by up to 34 pcp ROC-AUC in a cold start case when a new seller sells a new product .
ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization
Without the boundary information of action segments, existing methods mostly rely on multiple instance learning (MIL), where the predictions of unlabeled instances (i. e., video snippets) are supervised by classifying labeled bags (i. e., untrimmed videos).
NeRV: Neural Representations for Videos
In contrast, with NeRV, we can use any neural network compression method as a proxy for video compression, and achieve comparable performance to traditional frame-based video compression approaches (H. 264, HEVC \etc).
Ranked #6 on
Video Reconstruction
on UVG
Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection
With rapidly evolving internet technologies and emerging tools, sports related videos generated online are increasing at an unprecedentedly fast pace.
GAN-Based Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback
Deep reinforcement learning (DRL) has achieved great successes in many simulated tasks.
GTA: Global Temporal Attention for Video Action Understanding
To this end, we introduce Global Temporal Attention (GTA), which performs global temporal attention on top of spatial attention in a decoupled manner.
Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle
In this paper, we proposed a deep interactive reinforcement learning method for path following of AUV by combining the advantages of deep reinforcement learning and interactive RL.
Improving Interactive Reinforcement Agent Planning with Human Demonstration
Our results show that learning from demonstration can allow a TAMER agent to learn a roughly optimal policy up to the deepest search and encourage the agent to explore along the optimal path.
HSR: L1/2 Regularized Sparse Representation for Fast Face Recognition using Hierarchical Feature Selection
By employing hierarchical feature selection, we can compress the scale and dimension of global dictionary, which directly contributes to the decrease of computational cost in sparse representation that our approach is strongly rooted in.
Robust OS-ELM with a novel selective ensemble based on particle swarm optimization
Second, an adaptive selective ensemble framework for online learning is designed to balance the robustness and complexity of the algorithm.
LARSEN-ELM: Selective Ensemble of Extreme Learning Machines using LARS for Blended Data
Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data.
RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement
It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.
