Trending Research
CursorCore: Assist Programming through Aligning Anything
In this work, we propose a new conversational framework that comprehensively integrates these information sources, collect data to train our models and evaluate their performance.
"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models
We hope that our study can facilitate the research community and LLM vendors in promoting safer and regulated LLMs.
MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code
Training several popular base models with this corpus significantly improves their mathematical abilities, leading to the creation of the MathCoder2 family of models.
NTK-Guided Few-Shot Class Incremental Learning
Through the combined effects of these measures, our network acquires robust NTK properties, ensuring optimal convergence and stability of the NTK matrix and minimizing the NTK-related generalization loss, significantly enhancing its theoretical generalization.
class-incremental learning
Few-Shot Class-Incremental Learning
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Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation
Alongside PhyGenBench, we propose a novel evaluation framework called PhyGenEval.
ToolGen: Unified Tool Retrieval and Calling via Generation
As large language models (LLMs) advance, their inability to autonomously execute tasks by directly interacting with external tools remains a critical limitation.
QT-DoG: Quantization-aware Training for Domain Generalization
In this work, we propose Quantization-aware Training for Domain Generalization (QT-DoG) and demonstrate that weight quantization effectively leads to flatter minima in the loss landscape, thereby enhancing domain generalization.
Ranked #14 on
Domain Generalization
on TerraIncognita
Conditional Image Synthesis with Diffusion Models: A Survey
zju-pi/awesome-conditional-diffusion-models
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In this survey, we categorize existing works based on how conditions are integrated into the two fundamental components of diffusion-based modeling, i. e., the denoising network and the sampling process.
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
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
It can be used to obtain complete information, so that train-from-scratch models can achieve better results than state-of-the-art models pre-trained using large datasets, the comparison results are shown in Figure 1.
Ranked #7 on
Real-Time Object Detection
on MS COCO
