Trending Research
Law of the Weakest Link: Cross Capabilities of Large Language Models
The development and evaluation of Large Language Models (LLMs) have largely focused on individual capabilities.
Archon: An Architecture Search Framework for Inference-Time Techniques
Additionally, efficiently and automatically searching the space of model choices, inference-time techniques, and their compositions is challenging due to the large design space.
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
qwenlm/qwen2-vl
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We present the Qwen2-VL Series, an advanced upgrade of the previous Qwen-VL models that redefines the conventional predetermined-resolution approach in visual processing.
Ranked #3 on
Temporal Relation Extraction
on Vinoground
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and operate at a high cost, hindering the development of larger capable forecasting models in real-world applications.
KISS-Matcher: Fast and Robust Point Cloud Registration Revisited
While global point cloud registration systems have advanced significantly in all aspects, many studies have focused on specific components, such as feature extraction, graph-theoretic pruning, or pose solvers.
Augmented Dual-Contrastive Aggregation Learning for Unsupervised Visible-Infrared Person Re-Identification
yangbincv/ADCA
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ACM MM 2022
Visible infrared person re-identification (VI-ReID) aims at searching out the corresponding infrared (visible) images from a gallery set captured by other spectrum cameras.
Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution
oryx-mllm/oryx
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Visual data comes in various forms, ranging from small icons of just a few pixels to long videos spanning hours.
Ranked #1 on
Video Question Answering
on Perception Test
StoryMaker: Towards Holistic Consistent Characters in Text-to-image Generation
However, the lack of holistic consistency in scenes with multiple characters hampers these methods' ability to create a cohesive narrative.
Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance
fudan-generative-vision/champ
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In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion guidance in curernt human generative techniques.
Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale
gair-nlp/prox
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Large language model pre-training has traditionally relied on human experts to craft heuristics for improving the corpora quality, resulting in numerous rules developed to date.
