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Found 15 papers, 5 papers with code
MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
Large Vision-Language Models (LVLMs) show significant strides in general-purpose multimodal applications such as visual dialogue and embodied navigation.
MCM: Multi-condition Motion Synthesis Framework
Conditional human motion synthesis (HMS) aims to generate human motion sequences that conform to specific conditions.
Ranked #2 on
Motion Synthesis
on HumanML3D
Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model
Ctrl-Adapter provides diverse capabilities including image control, video control, video control with sparse frames, multi-condition control, compatibility with different backbones, adaptation to unseen control conditions, and video editing.
EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents
Instead of directly employing LLMs as agents, can we use LLMs' reasoning capabilities to adaptively create training environments to help smaller embodied RL agents learn useful skills that they are weak at?
DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning
In the first stage, we use LLMs to generate and iteratively refine 'diagram plans' (in a planner-auditor feedback loop) which describe all the entities (objects and text labels), their relationships (arrows or lines), and their bounding box layouts.
VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning
Our experiments demonstrate that VideoDirectorGPT framework substantially improves layout and movement control in both single- and multi-scene video generation and can generate multi-scene videos with visual consistency across scenes, while achieving competitive performance with SOTAs in open-domain single-scene T2V generation.
Supervised Masked Knowledge Distillation for Few-Shot Transformers
Vision Transformers (ViTs) emerge to achieve impressive performance on many data-abundant computer vision tasks by capturing long-range dependencies among local features.
Efficient Graph Field Integrators Meet Point Clouds
We present two new classes of algorithms for efficient field integration on graphs encoding point clouds.
Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson's Disease
To this end, we propose an end-to-end deep learning framework based on self-supervised learning for the segmentation and quantification of dopaminergic neurons in PD animal models.
TANDEM3D: Active Tactile Exploration for 3D Object Recognition
In this work, we propose TANDEM3D, a method that applies a co-training framework for exploration and decision making to 3D object recognition with tactile signals.
Hybrid Random Features
We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel estimation to provide most accurate approximation in the defined regions of interest.
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
In this paper we provide, to the best of our knowledge, the first comprehensive approach for incorporating various masking mechanisms into Transformers architectures in a scalable way.
SN 2017hpa: A Nearby Carbon-Rich Type Ia Supernova with a Large Velocity Gradient
We present extensive, well-sampled optical and ultraviolet photometry and optical spectra of the Type Ia supernova (SN Ia) 2017hpa.
High Energy Astrophysical Phenomena Solar and Stellar Astrophysics
The Tsinghua University-Ma Huateng Telescopes for Survey: Overview and Performance of the System
The TMTS system can have a FoV of about 9 deg2 when monitoring the sky with two bands (i. e., SDSS g and r filters) at the same time, and a maximum FoV of ~18 deg2 when four telescopes monitor different sky areas in monochromatic filter mode.
Instrumentation and Methods for Astrophysics
Demystifying Orthogonal Monte Carlo and Beyond
Orthogonal Monte Carlo (OMC) is a very effective sampling algorithm imposing structural geometric conditions (orthogonality) on samples for variance reduction.
