6 papers indexed
Xianghong Fang, Litao Guo, Hengchao Chen, Yuxuan Zhang, XiaofanXia, Dingjie Song, et al.
The effectiveness of modern visual representation learning and autoregressive models critically depends on vector quantization (VQ), which discretizes continuous feature representations using a learnable codebook. Despite its widespread use, existing VQ methods often suffer from…
Runzhe Liu, Biquan Bie, Zihao Wang, Yuchao Ma, Yexin Liu, Xinghai Li, et al.
The application of lightweight Large Language Models in rule-based scientific domains remains severely limited by their tendency to mimic linguistic patterns rather than reproduce axiomatic reasoning, causing frequent hallucinations. Here, we show that G-Frame, an adaptive multi-…
Guijia Zhang, Yuxun Chen, Yuheng Qi, Harry Yang
Multimodal GUI agents read an interface through two redundant channels: the rendered pixels of a screenshot and a serialized structure such as a document object model or accessibility tree. Before acting, an agent forms a belief about the current interface state, but existing ben…
Dengyang Jiang, Mengmeng Wang, Harry Yang, Jingdong Wang
Representation alignment has become an effective way to accelerate diffusion transformer training and improve generation quality. Recent self-alignment methods, such as SRA and Self-Flow, further remove the dependency on external pretrained encoders by constructing alignment with…
Changsheng Lu, Yuxin Chen, Haokun Gui, Rong Wang, Jie Yang, Harry Yang, et al.
With the emergence of various pre-trained vision and language models, computer vision is shifting from narrow-domain to open-domain recognition. The construction of a more powerful yet general keypoint detection (GKD) model to support diverse tasks has become increasingly importa…
Yaofu Liu, Wanli Lan, Jinxi Li, Binhang Yuan, Harry Yang
In $\textbf{DiT-based video generation models equipped with 3D Rotary Position Embeddings (3D RoPE)}$, the attention mechanism remains a primary computational bottleneck due to its quadratic complexity with respect to sequence length. While quantized $\textbf{FlashAttention}$ off…