Search Results for author:
Found 12 papers, 6 papers with code
Delta-WKV: A Novel Meta-in-Context Learner for MRI Super-Resolution
Magnetic Resonance Imaging (MRI) Super-Resolution (SR) addresses the challenges such as long scan times and expensive equipment by enhancing image resolution from low-quality inputs acquired in shorter scan times in clinical settings.
RWKV-UI: UI Understanding with Enhanced Perception and Reasoning
Additionally, we design a visual prompt based on the Chain-of-Thought(CoT) mechanism, which enhances the model's ability to understand and reason about webpage content through reasoning chains.
ImDy: Human Inverse Dynamics from Imitated Observations
In light of this, we devise an efficient data collection pipeline with state-of-the-art motion imitation algorithms and physics simulators, resulting in a large-scale human inverse dynamics benchmark as Imitated Dynamics (ImDy).
VisualRWKV-HD and UHD: Advancing High-Resolution Processing for Visual Language Models
Accurately understanding complex visual information is crucial for visual language models (VLMs).
Enhancing and Accelerating Large Language Models via Instruction-Aware Contextual Compression
Large Language Models (LLMs) have garnered widespread attention due to their remarkable performance across various tasks.
VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models
Visual Language Models (VLMs) have rapidly progressed with the recent success of large language models.
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture.
RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks
The combination of competitive performance, low latency, and efficient memory usage positions RWKV-TS as a promising avenue for future research in time series tasks.
Revisit Human-Scene Interaction via Space Occupancy
High-quality data with simultaneously captured human and 3D environments is hard to acquire, resulting in limited data diversity and complexity.
RWKV: Reinventing RNNs for the Transformer Era
This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.
Ranked #22 on
Natural Language Inference
on WNLI
BagFormer: Better Cross-Modal Retrieval via bag-wise interaction
In the field of cross-modal retrieval, single encoder models tend to perform better than dual encoder models, but they suffer from high latency and low throughput.
Sams-Net: A Sliced Attention-based Neural Network for Music Source Separation
Convolutional Neural Network (CNN) or Long short-term memory (LSTM) based models with the input of spectrogram or waveforms are commonly used for deep learning based audio source separation.
Ranked #23 on
Music Source Separation
on MUSDB18
