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Works35 from public data
- Multi-Task Collaborative Network for Joint Referring Expression Comprehension and Segmentation385
A novel Multi-task Collaborative Network (MCN) is proposed to achieve a joint learning of REC and RES for the first time and addresses a key challenge in this multi-task setup, i.e., the prediction conflict.
- DriveMLM: aligning multi-modal large language models with behavioral planning states for autonomous driving319
DriveMLM is introduced, an LLM-based AD framework that can perform close-loop autonomous driving in realistic simulators and can plug-and-play in existing AD systems such as Autopilot and Apollo for close-loop driving.
- SeqTR: A Simple Yet Universal Network for Visual Grounding247
Experiments on five benchmark datasets demonstrate that the proposed SeqTR outperforms (or is on par with) the existing state-of-the-arts, proving that a simple yet universal approach for visual grounding is indeed feasible.
- Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network213
This paper introduces a Global Enhanced Transformer (termed GET) to enable the extraction of a more comprehensive global representation, and then adaptively guide the decoder to generate high-quality captions.
- Cascade Grouped Attention Network for Referring Expression Segmentation160
A Cascade Grouped Attention Network with two innovative designs: Cascade Grouping Attention (CGA) and Instance-level Attention (ILA) loss that can achieve the high efficiency of one-stage RES while possessing a strong reasoning ability comparable to the two-stage methods.
- Towards Efficient Visual Adaption via Structural Re-parameterization124
It is proved that common adaptation modules can also be seamlessly integrated into most giant vision models via the authors' structural re-parameterization, thereby achieving zero-cost during inference.
- Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models120
This work proposes a novel and efficient method for MLLMs, termed Mixture-of-Resolution Adaptation (MRA), which adopts two visual pathways for images with different resolutions, where high-resolution visual information is embedded into the low-resolution pathway via the novel mixture-of-resolution adapters (MR-Adapters).
- Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training99
The core idea is to embed a new visual parameter space into a pre-trained LLM, thereby stably learning visual knowledge from noisy data while freezing the LLM, and proposes an innovative pre-training strategy to maximize the visual capability of Mono-InternVL, namely Endogenous Visual Pre-training (EViP).
- Active Teacher for Semi-Supervised Object Detection98
The experimental results not only validate the superior performance gain of Active Teacher over the compared methods, but also show that it enables the baseline network, i.e., Faster-RCNN, to achieve 100% supervised performance with much less label expenditure,i.e. 40% labeled examples on MS-COCO.
- A Real-Time Global Inference Network for One-Stage Referring Expression Comprehension86
The proposed RealGIN outperforms most existing methods and achieves very competitive performances against the most advanced one, i.e., MAttNet, under the same hardware, and can boost the processing speed by 10–20 times over the existing methods.
- Towards Lightweight Transformer Via Group-Wise Transformation for Vision-and-Language Tasks66
WL-Transformer applies Group-wise Transformation to reduce both the parameters and computations of Transformer, while also preserving its two main properties, i.e., the efficient attention modeling on diverse subspaces of MHA, and the expanding-scaling feature transformation of FFN.
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- ChatRex: Taming Multimodal LLM for Joint Perception and Understanding39
ChatRex is introduced, an MLLM with a decoupled perception design that feeds the output boxes from a universal proposal network into the LLM, allowing it to output the corresponding box indices to represent its detection results, turning the regression task into a retrieval-based task that LLM handles more proficiently.
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- Towards Language-Guided Visual Recognition via Dynamic Convolutions34
The experimental results not only show the competitive or better performance of LaConvNet against existing multi-modal networks, but also witness the merits of LaConvNet as an unified structure, including compact network, low computational cost and high generalization ability.
- K-armed Bandit based Multi-Modal Network Architecture Search for Visual Question Answering26
The experimental results show that with the help of KAB-NAS, AGAN can achieve the state-of-the-art performance on both benchmark datasets with much fewer parameters and computations.
- 3D-GRES: Generalized 3D Referring Expression Segmentation25
The Multi-Query Decoupled Interaction Network (MDIN) is proposed, designed to break down multi-object segmentation tasks into simpler, individual segmentations, charting a new path for intricate multi-object 3D scene comprehension.
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- SpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models in Compositional Spatial Intelligence23
It is found that even the most advanced MLLM still lags behind humans by large margins on SpaCE-10 and a novel hierarchical annotation pipeline is proposed to generate high-quality and diverse question-answer (QA) pairs.
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- A Survivor in the Era of Large-Scale Pretraining: An Empirical Study of One-Stage Referring Expression Comprehension16
A simple yet strong model called SimREC, which achieves new state-of-the-art performance on these benchmarks, and can achieve better performance than a set of large-scale pre-trained models, e.g., UNITER and VILLA, portraying the special role of REC in existing V&L research.
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- Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding11
This work proposes a novel network architecture, called Parameter-Inverted Image Pyramid Networks (PIIP), which uses pretrained models (ViTs or CNNs) as branches to process multi-scale images, where images of higher resolutions are processed by smaller network branches to balance computational cost and performance.
- Fast Text-to-3D-Aware Face Generation and Manipulation via Direct Cross-modal Mapping and Geometric Regularization11
This paper proposes an End-to-End Efficient and Effective network for fast and accurate T3D face generation and manipulation, termed $E^3$-FaceNet, which resorts to a direct mapping from text instructions to 3D-aware visual space.
- Towards End-to-end Semi-supervised Learning for One-stage Object Detection11
A novel teacher-student learning recipe called OneTeacher with two innovative designs, namely Multi-view Pseudo-label Refinement (MPR) and Decoupled Semi-supervised Optimization (DSO), which improves the quality of pseudo-labels via augmented-view refinement and global-view filtering and DSO handles the joint optimization conflicts via structure tweaks and task-specific pseudo-labeling.
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- QueryMatch: A Query-based Contrastive Learning Framework for Weakly Supervised Visual Grounding8
A novel query-based one-stage framework for weakly supervised visual grounding, namely QueryMatch, and an innovative strategy for effective weakly supervised learning, namely Active Query Selection (AQS), which aims to enhance the effectiveness of query-based contrastive learning by actively selecting high-quality query features.
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- ViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution6
This work proposes Visual Consistency Learning (ViCO), a novel training algorithm that enables the model to represent images of varying semantic complexities using different numbers of vision tokens using different numbers of MLP connectors.
- Deep Instruction Tuning for Segment Anything Model4
It is argued that deep text instruction tuning is key to mitigate such shortcoming caused by the shallow fusion scheme in its default light-weight mask decoder, and two simple yet effective deep instruction tuning (DIT) methods for SAM are proposed, one is end-to-end and the other is layer-wise.
- APL: Anchor-Based Prompt Learning for One-Stage Weakly Supervised Referring Expression Comprehension3
- Towards Omni-supervised Referring Expression Segmentation2
Omni-RES is a novel learning task called Omni-supervised Referring Expression Segmentation (Omni-RES) which leverages unlabeled, fully labeled, and weakly labeled data, such as referring points or bounding boxes, for efficient RES training.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. 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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