2.4k citations · 5k across the 43 of their papers we have counts for
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VLG: General Video Recognition with Web Textual Knowledge
Jintao Lin, Zhaoyang Liu, Wenhai Wang +2
Video recognition in an open and dynamic world is quite challenging, as we need to handle different settings such as close-set, long-tail, few-shot and open-set. By leveraging sema…
Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks
Hao Li, Jinguo Zhu, Xiaohu Jiang +8
Despite the remarkable success of foundation models, their task-specific fine-tuning paradigm makes them inconsistent with the goal of general perception modeling. The key to elimi…
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
Wenhai Wang, Jifeng Dai, Zhe Chen +9
Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early sta…
On Efficient Reinforcement Learning for Full-length Game of StarCraft II
Ruo-Ze Liu, Zhen-Jia Pang, Zhou-Yu Meng +3
StarCraft II (SC2) poses a grand challenge for reinforcement learning (RL), of which the main difficulties include huge state space, varying action space, and a long time horizon.…
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
Hongyang Li, Chonghao Sima, Jifeng Dai +19
Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia. Conventional approaches…
Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation
Guangchen Shi, Yirui Wu, Jun Liu +3
Incremental few-shot semantic segmentation (IFSS) targets at incrementally expanding model's capacity to segment new class of images supervised by only a few samples. However, feat…
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
Jinguo Zhu, Xizhou Zhu, Wenhai Wang +4
To build an artificial neural network like the biological intelligence system, recent works have unified numerous tasks into a generalist model, which can process various tasks wit…
Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality
Xiang Li, Wenhai Wang, Lingfeng Yang +1
Masked AutoEncoder (MAE) has recently led the trends of visual self-supervision area by an elegant asymmetric encoder-decoder design, which significantly optimizes both the pre-tra…
Vision Transformer Adapter for Dense Predictions
Zhe Chen, Yuchen Duan, Wenhai Wang +4
This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT). Unlike recently advanced variants that incorporate vision-specific inductiv…
WegFormer: Transformers for Weakly Supervised Semantic Segmentation
Chunmeng Liu, Enze Xie, Wenjia Wang +3
Although convolutional neural networks (CNNs) have achieved remarkable progress in weakly supervised semantic segmentation (WSSS), the effective receptive field of CNN is insuffici…
BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers
Zhiqi Li, Wenhai Wang, Hongyang Li +5
3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new f…