Trending Research
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation.
StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text
picsart-ai-research/streamingt2v
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To overcome these limitations, we introduce StreamingT2V, an autoregressive approach for long video generation of 80, 240, 600, 1200 or more frames with smooth transitions.
SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap
This tracking and identification process is crucial for reconstructing the game state, defined by the athletes' positions and identities on a 2D top-view of the pitch, (i. e. a minimap).
Learning with 3D rotations, a hitchhiker's guide to SO(3)
martius-lab/hitchhiking-rotations
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Many settings in machine learning require the selection of a rotation representation.
InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation
Tuning-free diffusion-based models have demonstrated significant potential in the realm of image personalization and customization.
Probing the 3D Awareness of Visual Foundation Models
mbanani/probe3d
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Given that such models can classify, delineate, and localize objects in 2D, we ask whether they also represent their 3D structure?
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
We study the use of large language model-based agents for interacting with software via web browsers.
MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators
Recent advances in Text-to-Video generation (T2V) have achieved remarkable success in synthesizing high-quality general videos from textual descriptions.
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
JLDeng/SCNN
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The core issue in MTS forecasting is how to effectively model complex spatial-temporal patterns.
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Time Series Forecasting
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Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed
Furthermore, we find spatial variance exists in LoFTR's fine correlation module, which is adverse to matching accuracy.
