Search Results for author:
Found 15 papers, 9 papers with code
RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis
Robotic behavior synthesis, the problem of understanding multimodal inputs and generating precise physical control for robots, is an important part of Embodied AI.
Ranked #72 on
Visual Question Answering
on MM-Vet
GenTron: Delving Deep into Diffusion Transformers for Image and Video Generation
In this study, we explore Transformer-based diffusion models for image and video generation.
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing
In this paper, for the first time, we introduce optical flow into the attention module in the diffusion model's U-Net to address the inconsistency issue for text-to-video editing.
Enhancing Your Trained DETRs with Box Refinement
We hope our work will bring the attention of the detection community to the localization bottleneck of current DETR-like models and highlight the potential of the RefineBox framework.
GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest
Before sending to LLM, the reference is replaced by RoI features and interleaved with language embeddings as a sequence.
Ranked #1 on
Visual Question Answering (VQA)
on VCR (Q-AR) test
Going Denser with Open-Vocabulary Part Segmentation
In this paper, we propose a detector with the ability to predict both open-vocabulary objects and their part segmentation.
InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language
Different from existing interactive systems that rely on pure language, by incorporating pointing instructions, the proposed iGPT significantly improves the efficiency of communication between users and chatbots, as well as the accuracy of chatbots in vision-centric tasks, especially in complicated visual scenarios where the number of objects is greater than 2.
Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning
We evaluate our soft neighbor contrastive learning method (SNCLR) on standard visual recognition benchmarks, including image classification, object detection, and instance segmentation.
CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer
In visual control, learning transferable state representation that can transfer between different control tasks is important to reduce the training sample size.
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
To address this challenge, we propose an effective adaptation approach for Transformer, namely AdaptFormer, which can adapt the pre-trained ViTs into many different image and video tasks efficiently.
Towards High-Quality Temporal Action Detection with Sparse Proposals
Towards high-quality temporal action detection, we introduce Sparse Proposals to interact with the hierarchical features.
CycleMLP: A MLP-like Architecture for Dense Prediction
We build a family of models which surpass existing MLPs and even state-of-the-art Transformer-based models, e. g., Swin Transformer, while using fewer parameters and FLOPs.
Ranked #15 on
Semantic Segmentation
on DensePASS
Watch Only Once: An End-to-End Video Action Detection Framework
WOO takes a unified video backbone to simultaneously extract features for actor location and action classification.
Efficient Differentiable Neural Architecture Search with Meta Kernels
We demonstrate the effectiveness of our search strategy by conducting extensive experiments.
