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Found 14 papers, 9 papers with code
InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding
We introduce InternVideo2, a new video foundation model (ViFM) that achieves the state-of-the-art performance in action recognition, video-text tasks, and video-centric dialogue.
Ranked #1 on
Audio Classification
on ESC-50
(using extra training data)
Self-Consistency Training for Hamiltonian Prediction
This merit addresses the data scarcity difficulty, and distinguishes the task from other property prediction formulations with unique benefits: (1) self-consistency training enables the model to be trained on a large amount of unlabeled data, hence substantially enhances generalization; (2) self-consistency training is more efficient than labeling data with DFT for supervised training, since it is an amortization of DFT calculation over a set of molecular structures.
Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology.
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
With the rapid development of Multi-modal Large Language Models (MLLMs), a number of diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities of these models.
Ranked #1 on
Zero-Shot Video Question Answer
on STAR Benchmark
Does AI for science need another ImageNet Or totally different benchmarks? A case study of machine learning force fields
This suite of metrics has demonstrated a better ability to assess a model's performance in real-world scientific applications, in contrast to traditional AI benchmarking methodologies.
Scaling Data Generation in Vision-and-Language Navigation
Recent research in language-guided visual navigation has demonstrated a significant demand for the diversity of traversable environments and the quantity of supervision for training generalizable agents.
ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments
To develop a robust VLN-CE agent, we propose a new navigation framework, ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments.
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)
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022
In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task.
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
1st Place Solutions for RxR-Habitat Vision-and-Language Navigation Competition (CVPR 2022)
Our model consists of three modules: the candidate waypoints predictor (CWP), the history enhanced planner and the tryout controller.
Bridging the Gap Between Learning in Discrete and Continuous Environments for Vision-and-Language Navigation
To bridge the discrete-to-continuous gap, we propose a predictor to generate a set of candidate waypoints during navigation, so that agents designed with high-level actions can be transferred to and trained in continuous environments.
Heterogeneous relational message passing networks for molecular dynamics simulations
With many frameworks based on message passing neural networks proposed to predict molecular and bulk properties, machine learning methods have tremendously shifted the paradigms of computational sciences underpinning physics, material science, chemistry, and biology.
Symmetry-adapted graph neural networks for constructing molecular dynamics force fields
Molecular dynamics is a powerful simulation tool to explore material properties.
