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Claim this profileWorks135 from public data
- Deformable Convolutional Networks7,141
This work introduces two new modules to enhance the transformation modeling capability of CNNs, namely, deformable convolution and deformable RoI pooling, based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from the target tasks, without additional supervision.
- 3,149
- Deformable ConvNets V2: More Deformable, Better Results2,748
This work presents a reformulation of Deformable Convolutional Networks that improves its ability to focus on pertinent image regions, through increased modeling power and stronger training, and guides network training via a proposed feature mimicking scheme that helps the network to learn features that reflect the object focus and classification power of R-CNN features.
- InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models1,936
InternVL3 incorporates variable visual position encoding (V2PE) to support extended multimodal contexts, employs advanced post-training techniques such as supervised fine-tuning (SFT) and mixed preference optimization (MPO), and adopts test-time scaling strategies alongside an optimized training infrastructure.
- VL-BERT: Pre-training of Generic Visual-Linguistic Representations1,860
A new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT), which adopts the simple yet powerful Transformer model as the backbone, and extends it to take both visual and linguistic embedded features as input.
- Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling1,813
This work explores the relationship between model scaling and performance, systematically exploring the performance trends in vision encoders, language models, dataset sizes, and test-time configurations and hopes this model contributes to the open-source community by setting new standards for developing and applying multimodal AI systems.
- 1,776
- Planning-oriented Autonomous Driving1,690
This work introduces Unified Autonomous Driving (UniAD), a comprehensive framework up-to-date that incorporates full-stack driving tasks in one network and is exquisitely devised to leverage advantages of each module, and provide complementary feature abstractions for agent interaction from a global perspective.
- Relation Networks for Object Detection1,464
An object relation module is proposed that processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations, which gives rise to the first fully end-to-end object detector.
- 1,344
- How far are we to GPT-4V? Closing the gap to commercial multimodal models with open-source suites1,306
InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding, shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks.
- Instance-Aware Semantic Segmentation via Multi-task Network Cascades1,279
This paper presents Multitask Network Cascades for instance-aware semantic segmentation, which consists of three networks, respectively differentiating instances, estimating masks, and categorizing objects, and develops an algorithm for the nontrivial end-to-end training of this causal, cascaded structure.
- InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions1,217
This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs, and reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large- scale parameters from massive datalike ViTs.
- ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation1,154
This paper proposes to use scribbles to annotate images, and develops an algorithm to train convolutional networks for semantic segmentation supervised by scribbles, which shows excellent results on the PASCALCONTEXT dataset thanks to extra inexpensive scribble annotations.
- BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation1,125
This paper proposes a method that achieves competitive accuracy but only requires easily obtained bounding box annotations, and yields state-of-the-art results on PASCAL VOC 2012 and PASCal-CONTEXT.
- Fully Convolutional Instance-Aware Semantic Segmentation1,071
The first fully convolutional end-to-end solution for instance-aware semantic segmentation task, which achieves state-of-the-art performance in both accuracy and efficiency, wins the COCO 2016 segmentation competition by a large margin.
- Vision Transformer Adapter for Dense Predictions938
The ViT-Adapter is proposed, which allows plain ViT to achieve comparable performance to vision-specific transformers and facilitate future research and is verified on multiple dense prediction tasks, including object detection, instance segmentation, and semantic segmentation.
- Flow-Guided Feature Aggregation for Video Object Detection727
This work presents flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection that improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy.
- 668
- Deep Feature Flow for Video Recognition668
Deep feature flow is presented, a fast and accurate framework for video recognition that runs the expensive convolutional sub-network only on sparse key frames and propagates their deep feature maps to other frames via a flow field and achieves significant speedup as flow computation is relatively fast.
- Exploring Cross-Image Pixel Contrast for Semantic Segmentation596
This work raises a pixel-wise metric learning paradigm for semantic segmentation, by explicitly exploring the structures of labeled pixels, which were rarely explored before, and hopes this work will encourage the community to rethink the current de facto training paradigm in semantic segmentsation.
- An Empirical Study of Spatial Attention Mechanisms in Deep Networks596
An empirical study that ablates various spatial attention elements within a generalized attention formulation, encompassing the dominant Transformer attention as well as the prevalent deformable convolution and dynamic convolution modules, yields significant findings about spatial attention in deep networks, some of which run counter to conventional understanding.
- BEVFormer: Learning Bird’s-Eye-View Representation From LiDAR-Camera via Spatiotemporal Transformers593
This work presents a new framework termed BEVFormer, which learns unified BEV representations from multi-modality data with spatiotemporal transformers to support multiple autonomous driving perception tasks, achieving outstanding results across these tasks.
- BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision490
A novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and bet-suits modern image backbones and achieves new SoTA results on the large-scale nuScenes dataset.
- EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought459
This work introduces EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi- modal understanding and execution capabilities, and significantly enhances the success rate of the embodied control task by extracting more effective features.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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