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- 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.
- 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.
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- VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows271
This work proposes a large-scale Visible-Event benchmark (termed VisEvent) and builds a simple but effective tracking algorithm by proposing a cross-modality transformer, to achieve more effective feature fusion between visible and event data.
- DDP: Diffusion Model for Dense Visual Prediction254
The method, called DDP, efficiently extends the denoising diffusion process into the modern perception pipeline and shows attractive properties such as dynamic inference and uncertainty awareness, in contrast to previous single-step discriminative methods.
- Contrastive Boundary Learning for Point Cloud Segmentation202
This paper proposes a novel contrastive boundary learning (CBL) framework for point cloud segmentation that enhances feature discrimination between points across boundaries by contrasting their representations with the assistance of scene contexts at multiple scales.
- Progressive LiDAR adaptation for road detection202
Comprehensive empirical studies on the well-known KITTI road detection benchmark demonstrate that PLARD takes advantage of both the visual and LiDAR information, achieving much more robust road detection even in challenging urban scenes.
- SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection180
This work proposes a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA), which is an easy-to-plug-in module and able to boost various point-based detectors, including single-stage and two-stage ones, and proposes a semantics-guided point sampling algorithm to help retain more important foreground points during down-sampling.
- Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures166
Vision-RWKV (VRWKV), a model adapted from the RWKV model used in the NLP field with necessary modifications for vision tasks, surpasses ViT's performance in image classification and has significantly faster speeds and lower memory usage processing high-resolution inputs.
- Pedestrian attribute recognition: A survey163
The background of pedestrian attribute recognition (PAR), including the fundamental concepts of pedestrian attributes and corresponding challenges are introduced, and existing benchmarks, including popular datasets and evaluation criterion are introduced.
- RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection155
A deep neural network that can detect both road and road boundary in a single process is introduced, called Road and road Boundary detection Network (RBNet), that first investigates the contextual relationship between the road structure and its boundary arrangement and then model them with a Bayesian network.
- TextFuseNet: Scene Text Detection with Richer Fused Features149
The proposed TextFuseNet can learn a more adequate description of arbitrary shapes texts, suppressing false positives and producing more accurate detection results, and can be trained with weak supervision for those datasets that lack character-level annotations.
- MAPF-LNS2: Fast Repairing for Multi-Agent Path Finding via Large Neighborhood Search147
This paper proposes a novel algorithm MAPF-LNS2 based on large neighborhood search for solving multi-Agent Path Finding efficiently that solves 80% of the random-scenario instances with the largest number of agents from the MAPF benchmark suite with a runtime limit of just 5 minutes, which, to the authors' knowledge, has not been achieved by any existing algorithms.
- The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World133
The All-Seeing model (ASM), a unified framework for panoptic visual recognition and understanding, is developed with open-ended language prompts and locations, which allows it to generalize to various vision and language tasks with remarkable zero-shot performance.
- Expression snippet transformer for robust video-based facial expression recognition119
This work designs an attention-augmented snippet feature extractor (AA-SFE) to enhance the encoding of subtle facial movements of each snippet by gradually attending to more salient information, and introduces a shuffled snippet order prediction (SSOP) head and a corresponding loss to improve the modeling of subtle motion changes across subsequent snippets.
- Context Refinement for Object Detection117
A context refinement algorithm that explores rich contextual information to better refine each proposed region and first identifies neighboring regions that may contain useful contexts and then perform refinement based on the extracted and unified contextual information.
- Automatic web spreadsheet data extraction117
A system that automatically extracts relational data from spreadsheets, thereby enabling relational spreadsheet integration and a novel view of how users organize their data in spreadsheets is presented.
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- Towards High Performance Human Keypoint Detection94
This paper develops a hard-negative person detection mining strategy and a joint-training strategy by exploiting abundant unlabeled data that enables CCM to learn discriminative features from massive diverse poses and presents several sub-pixel refinement techniques for postprocessing keypoint predictions to improve detection accuracy.
- A blockchain 3.0 paradigm for digital twins in construction project management89
This paper proposes ChainPM as a Blockchain 3.0 paradigm, which extends Blockchain 2.0 with innovative indexing, query, and smart contracts.
- MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer83
MM-Interleaved is an end-to-end generative model for interleaved image-text data that introduces a multi-scale and multi-image feature synchronizer module, allowing direct access to fine-grained image features in the previous context during the generation process.
- Having the Memory of an Elephant: Long-Term Retrieval and the Use of Analogues in Problem Solving.77
Comparisons of different versions of a target problem indicated that similarity of solution tool affected accessing, mapping, and executing components of problem solving, whereas similarity of goal object had only a moderate effect on accessing.
- A New Chaos-Based Color Image Encryption Scheme with an Efficient Substitution Keystream Generation Strategy66
Experimental results demonstrate that the proposed scheme has a satisfactory level of security and outperforms the conventional schemes in terms of computational efficiency.
- Dynamic Attention Guided Multi-Trajectory Analysis for Single Object Tracking56
This paper proposes to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy that contains multiple target templates, each of which provides its own attention for locating the target in the new frame.
- CLAMP: Prompt-based Contrastive Learning for Connecting Language and Animal Pose50
A novel prompt-based Contrastive learning scheme for connecting Language and AniMal Pose (CLAMP) effectively, which enables the first cross-modal animal pose estimation paradigm.
- Background–foreground interaction for moving object detection in dynamic scenes50
The interactive approach proposed in this paper improves the robustness of moving object detection, preventing deadlocks and degeneration in the models.
- A novel approach for the registration of 2D portal and 3D CT images for treatment setup verification in radiotherapy48
The minimax entropy algorithm proposed in this paper evaluates appropriate entropies in order to segment the portal image and to find the registration parameters iteratively, and can be used, in general, for registering a high resolution image to a low resolution image.
- Recurrent Glimpse-based Decoder for Detection with Transformer45
A novel REcurrent Glimpse-based decOder (REGO) based detection refinement that can easily help mitigate the difficulty of training for DETR methods and can be easily embedded in representative DETR variants while maintaining their fully end-to-end training and inference pipelines.
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- Recursive Context Routing for Object Detection40
A novel Recursive Context Routing (ReCoR) mechanism to encode contextual relationships and model contexts more effectively through a recursive structure, providing a more feasible and more comprehensive method to utilize complicated contexts and contextual relationships.
- DiagramFlyer40
This work proposes DiagramFlyer, a search engine for finding data-driven diagrams on the web that is able to "expand" queries to include not only exactly matching diagrams, but also diagrams that are likely to be related in terms of their production pipelines.
- Pose-disentangled Contrastive Learning for Self-supervised Facial Representation38
This work proposes a novel Pose-disentangled Contrastive Learning (PCL) method that learns pose-related information based on data augmentation of the same image, which would deliver more effective face-aware representation for various downstream tasks.
- SimDistill: Simulated Multi-Modal Distillation for BEV 3D Object Detection35
A Simulated multi-modal Distillation (SimDistill) method that supports intra-modal, cross-modal, and multi-modal fusion distillation simultaneously in the Bird's-eye-view space and can learn better feature representations for 3D object detection while maintaining a cost-effective camera-only deployment.
- A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy35
A new feature extraction method based on improved complementary ensemble empirical mode decomposition with adaptive noise, normalized mutual information (norMI), and multiscale improved permutation entropy (MIPE) that is more reliable and suitable for feature extraction of ship-radiated noise in practice.
- Sample-Cohesive Pose-Aware Contrastive Facial Representation Learning34
A Pose-disentangled Contrastive Facial Representation Learning (PCFRL) framework to enhance pose awareness for SFRL by explicitly disentangled the pose-aware features from non-pose face-aware features and introducing appropriate sample calibration schemes for better CL with the disentangled features.
- Noise-Resistant Multimodal Transformer for Emotion Recognition34
A novel paradigm that attempts to extract noise-resistant features in its pipeline and introduces a noise-aware learning scheme to effectively improve the robustness of multimodal emotion understanding against noisy information is presented.
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- Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-Based Beam Search28
A novel multi-agent reinforcement learning based beam search tracking strategy, termed BeamTracking, which is mainly inspired by the image captioning task, which takes an image as input and generates diverse descriptions using beam search algorithm.
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- Open-Set Video-based Facial Expression Recognition with Human Expression-sensitive Prompting18
This paper proposes a novel Human Expression-Sensitive Prompting (HESP) mechanism to significantly enhance CLIP's ability to model video-based facial expression details effectively, and demonstrates that HESP can significantly boost CLIP's performance and outperform other state-of-the-art open-set video understanding methods by a large margin.
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- Smile on the Face, Sadness in the Eyes: Bridging the Emotion Gap With a Multimodal Dataset of Eye and Facial Behaviors5
A comprehensive analysis of the importance of eye behaviors in ER is provided, advancing the study on addressing the gap between FER and ER for more robust ER performance.
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- Cooperative trajectory planning algorithm of USV-UAV with hull dynamic constraints1
A cooperative trajectory planning algorithm for the coupled USV-UAV system is proposed to ensure that USV can execute a safe and smooth path in the process of autonomous advance in multi-obstacle maps.
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- CERA: Conflict-Explicit Reflective Agent for Multimodal Emotion Reasoning–
Conflict-Explicit Reflective Agent (CERA), a training-free, conflict-aware, and language-driven agentic framework for MER, significantly outperforms state-of-the-art training-free methods in both recognition accuracy and conflict interpretability, providing an effective training-free solution for complex emotional reasoning.
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- Moving object detection based on background image set and sparse analysis–
The simulation results indicate that when the background illumination mutates, the proposed method can effectively eliminate the impact of environment noise and reduce the false detection rate of target detection.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; 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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