Search Results for author:
Found 21 papers, 12 papers with code
StreamChat: Chatting with Streaming Video
This paper presents StreamChat, a novel approach that enhances the interaction capabilities of Large Multimodal Models (LMMs) with streaming video content.
MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment
This paper introduces MM-Instruct, a large-scale dataset of diverse and high-quality visual instruction data designed to enhance the instruction-following capabilities of large multimodal models (LMMs).
Ranked #135 on
Visual Question Answering
on MM-Vet
Instruction-Guided Visual Masking
To achieve more accurate and nuanced multimodal instruction following, we introduce Instruction-guided Visual Masking (IVM), a new versatile visual grounding model that is compatible with diverse multimodal models, such as LMM and robot model.
Ranked #1 on
Visual Question Answering
on V*bench
Enhancing Vision-Language Model with Unmasked Token Alignment
Contrastive pre-training on image-text pairs, exemplified by CLIP, becomes a standard technique for learning multi-modal visual-language representations.
GLID: Pre-training a Generalist Encoder-Decoder Vision Model
This paper proposes a GeneraLIst encoder-Decoder (GLID) pre-training method for better handling various downstream computer vision tasks.
DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning
Multimodal pretraining is an effective strategy for the trinity of goals of representation learning in autonomous robots: 1) extracting both local and global task progressions; 2) enforcing temporal consistency of visual representation; 3) capturing trajectory-level language grounding.
EasyDrag: Efficient Point-based Manipulation on Diffusion Models
Generative models are gaining increasing popularity and the demand for precisely generating images is on the rise.
GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D Understanding
In this paper, we propose Geometry Enhanced Masked Image Modeling (GeoMIM) to transfer the knowledge of the LiDAR model in a pretrain-finetune paradigm for improving the multi-view camera-based 3D detection.
TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers
In this paper, we propose a novel data augmentation technique TokenMix to improve the performance of vision transformers.
UniNet: Unified Architecture Search with Convolution, Transformer, and MLP
Finally, we integrate configurable operators and DSMs into a unified search space and search with a Reinforcement Learning-based search algorithm to fully explore the optimal combination of the operators.
Ranked #12 on
Neural Architecture Search
on ImageNet
MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers
In this paper, we propose Mixed and Masked AutoEncoder (MixMAE), a simple but efficient pretraining method that is applicable to various hierarchical Vision Transformers.
Ranked #2 on
Image Classification
on Places205
Meta Knowledge Distillation
Recent studies pointed out that knowledge distillation (KD) suffers from two degradation problems, the teacher-student gap and the incompatibility with strong data augmentations, making it not applicable to training state-of-the-art models, which are trained with advanced augmentations.
Ranked #144 on
Image Classification
on ImageNet
INTERN: A New Learning Paradigm Towards General Vision
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.
UniNet: Unified Architecture Search with Convolution, Transformer, and MLP
Recently, transformer and multi-layer perceptron (MLP) architectures have achieved impressive results on various vision tasks.
Ranked #254 on
Image Classification
on ImageNet
FNAS: Uncertainty-Aware Fast Neural Architecture Search
Further, an architecture knowledge pool together with a block similarity function is proposed to utilize parameter knowledge and reduces the searching time by 2 times.
Divisors computing minimal log discrepancies on lc surfaces
Let $(X\ni x, B)$ be an lc surface germ.
Algebraic Geometry
Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning
Further, a life-long knowledge pool together with a block similarity function is proposed to utilize the lifelong parameter knowledge and reduces the searching time by 2 times.
Effective birationality for sub-pairs with real coefficients
For $\epsilon$-lc Fano type varieties $X$ of dimension $d$ and a given finite set $\Gamma$, we show that there exists a positive integer $m_0$ which only depends on $\epsilon, d$ and $\Gamma$, such that both $|-mK_X-\sum_i\lceil mb_i\rceil B_i|$ and $|-mK_X-\sum_i\lfloor mb_i\rfloor B_i|$ define birational maps for any $m\ge m_0$ provided that $B_i$ are pseudo-effective Weil divisors, $b_i\in\Gamma$, and $-(K_X+\sum_ib_iB_i)$ is big.
Algebraic Geometry
Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images
Though face rotation has achieved rapid progress in recent years, the lack of high-quality paired training data remains a great hurdle for existing methods.
Learning Where to Focus for Efficient Video Object Detection
Transferring existing image-based detectors to the video is non-trivial since the quality of frames is always deteriorated by part occlusion, rare pose, and motion blur.
Ranked #24 on
Video Object Detection
on ImageNet VID
Towards Flops-constrained Face Recognition
Large scale face recognition is challenging especially when the computational budget is limited.
