Trending Research
A visualization method for data domain changes in CNN networks and the optimization method for selecting thresholds in classification tasks
In recent years, Face Anti-Spoofing (FAS) has played a crucial role in preserving the security of face recognition technology.
PyGlove: Efficiently Exchanging ML Ideas as Code
We also perform a case study of a large codebase where PyGlove led to an 80% reduction in the number of lines of code.
PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation
We present PhysGen, a novel image-to-video generation method that converts a single image and an input condition (e. g., force and torque applied to an object in the image) to produce a realistic, physically plausible, and temporally consistent video.
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
Based on W2*A8 quantization configuration on LLaMA-7B model, it achieved a WikiText2 perplexity of 7. 59 (2. 17$\downarrow $ vs 9. 76 in AffineQuant).
Cross-video Identity Correlating for Person Re-identification Pre-training
For example, compared with the previous state-of-the-art~\cite{ISR}, CION with the same ResNet50-IBN achieves higher mAP of 93. 3\% and 74. 3\% on Market1501 and MSMT17, while only utilizing 8\% training samples.
WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild
In recent years, 3D hand pose estimation methods have garnered significant attention due to their extensive applications in human-computer interaction, virtual reality, and robotics.
Ranked #1 on
3D Hand Pose Estimation
on HO-3D v2
OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer
Recent advancements in Large Language Models (LLMs) have expanded their capabilities to multimodal contexts, including comprehensive video understanding.
VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models
microsoft/vptq
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Due to the redundancy in LLM weights, recent research has focused on pushing weight-only quantization to extremely low-bit (even down to 2 bits).
TensorIR: An Abstraction for Automatic Tensorized Program Optimization
mlc-ai/web-llm
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Finally, we build an end-to-end framework on top of our abstraction to automatically optimize deep learning models for given tensor computation primitives.
Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers
liruiw/HPT
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Previous robot learning methods often collect data to train with one specific embodiment for one task, which is expensive and prone to overfitting.
