Search Results for author:
Found 22 papers, 13 papers with code
Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset
Recent advancements in Large Multimodal Models (LMMs) have shown promising results in mathematical reasoning within visual contexts, with models approaching human-level performance on existing benchmarks such as MathVista.
Ranked #1 on
Multimodal Reasoning
on MATH-V
(using extra training data)
GroundingGPT:Language Enhanced Multi-modal Grounding Model
Beyond capturing global information like other multi-modal models, our proposed model excels at tasks demanding a detailed understanding of local information within the input.
TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise
Based on TeacherLM-7. 1B, we augmented 58 NLP datasets and taught various student models with different parameters from OPT and BLOOM series in a multi-task setting.
Turning hazardous volatile matter compounds into fuel by catalytic steam reforming: An evolutionary machine learning approach
Literature is used to compile a database covering a variety of catalyst characteristics and reaction conditions.
Retrieving-to-Answer: Zero-Shot Video Question Answering with Frozen Large Language Models
Video Question Answering (VideoQA) has been significantly advanced from the scaling of recent Large Language Models (LLMs).
Ranked #3 on
Temporal/Casual QA
on NExT-QA
(using extra training data)
Using evolutionary machine learning to characterize and optimize co-pyrolysis of biomass feedstocks and polymeric wastes
The input features are constructed using an innovative approach to reflect the physics of the process.
VideoLLM: Modeling Video Sequence with Large Language Models
Building upon this insight, we propose a novel framework called VideoLLM that leverages the sequence reasoning capabilities of pre-trained LLMs from natural language processing (NLP) for video sequence understanding.
Personalize Segment Anything Model with One Shot
Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models.
Ranked #1 on
Personalized Segmentation
on PerSeg
SparseMAE: Sparse Training Meets Masked Autoencoders
In this paper, we aim to reduce model complexity from large vision transformers pretrained by MAE with assistant of sparse training.
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
Specifically, InternVideo efficiently explores masked video modeling and video-language contrastive learning as the pretraining objectives, and selectively coordinates video representations of these two complementary frameworks in a learnable manner to boost various video applications.
Ranked #1 on
Action Recognition
on Something-Something V1
(using extra training data)
InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges
In this report, we present our champion solutions to five tracks at Ego4D challenge.
Ranked #1 on
State Change Object Detection
on Ego4D
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
This has led to a new research direction in parameter-efficient transfer learning.
Ranked #23 on
Action Recognition
on Something-Something V2
(using extra training data)
EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers
In this work, pushing further along this under-studied direction we introduce EdgeViTs, a new family of light-weight ViTs that, for the first time, enable attention-based vision models to compete with the best light-weight CNNs in the tradeoff between accuracy and on-device efficiency.
1st place solution for AVA-Kinetics Crossover in AcitivityNet Challenge 2020
This technical report introduces our winning solution to the spatio-temporal action localization track, AVA-Kinetics Crossover, in ActivityNet Challenge 2020.
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
We propose to explicitly model the Actor-Context-Actor Relation, which is the relation between two actors based on their interactions with the context.
Ranked #2 on
Action Recognition
on AVA v2.1
Video Generation from Single Semantic Label Map
This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process.
Unsupervised Bi-directional Flow-based Video Generation from one Snapshot
Imagining multiple consecutive frames given one single snapshot is challenging, since it is difficult to simultaneously predict diverse motions from a single image and faithfully generate novel frames without visual distortions.
Online Detection of Action Start in Untrimmed, Streaming Videos
We aim to tackle a novel task in action detection - Online Detection of Action Start (ODAS) in untrimmed, streaming videos.
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples.
Shallow and Deep Convolutional Networks for Saliency Prediction
The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles.
End-to-end Convolutional Network for Saliency Prediction
The prediction of saliency areas in images has been traditionally addressed with hand crafted features based on neuroscience principles.
