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- MovieChat: From Dense Token to Sparse Memory for Long Video Understanding685
The MovieChat achieves state-of-the-art performance in long video understanding, along with the released MovieChat-1K benchmark with 1K long video and 14K manual annotations for validation of the effectiveness of the effectiveness of the method.
- StableVideo: Text-driven Consistency-aware Diffusion Video Editing243
This paper develops a novel inter-frame propagation mechanism for diffusion video editing, which leverages the concept of layered representations to propagate the appearance information from one frame to the next.
- Exploit the Connectivity197
This paper proposes an innovative and effective tracking method called TrackletNet Tracker (TNT) that combines temporal and appearance information together as a unified framework and achieves promising results on MOT16 and MOT17 benchmark datasets compared with other state-of-the-art methods.
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- AI Assisted Fashion Design: A Review113
The primary results highlight the transformative potential of enhanced personalization and seamless multimodal interfaces, empowering designers and consumers to co-create unique and personalized designs within the fashion industry.
- Multi-Camera Tracking of Vehicles based on Deep Features Re-ID and Trajectory-Based Camera Link Models110
An MCT system, which combines single- camera tracking (SCT) and inter-camera tracking (ICT) which includes trajectory-based camera link model and deep feature reidentification and is evaluated on CVPR AI City Challenge 2019 City Flow dataset, achieving IDF1 70.59%, which outperforms competing methods.
- MovieChat+: Question-Aware Sparse Memory for Long Video Question Answering85
This work proposes MovieChat within a training-free memory consolidation mechanism to overcome challenges in long video understanding, and lifts pre-trained large multi-modal models for understanding long videos without additional trainable modules, employing a zero-shot approach.
- Deep learning-enabled 3D multimodal fusion of cone-beam CT and intraoral mesh scans for clinically applicable tooth-bone reconstruction85
Deep Dental Multimodal Fusion is presented, an automatic multimodal framework that reconstructs 3D tooth-bone structures using CBCT and IOS using a novel pixel representation learning architectures, prior knowledge-guided losses, and geometry-based 3D fusion techniques.
- Beware of the generic machine learning-based scoring functions in structure-based virtual screening81
Overall, generic MLSFs in VS could hardly achieve satisfactory results for any dataset, and they could even not outperform the baseline of classical SFs such as Glide SP.
- Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection75
A novel distillation method with cross-task consistent protocols, tailored for the dense object detection, and an IoU-based Localization Distillation Loss that is free from specific network structures and can be compared with existing localization distillation losses are proposed.
- Track without Appearance: Learn Box and Tracklet Embedding with Local and Global Motion Patterns for Vehicle Tracking74
A novel approach is proposed that tackles the association issue for long-term tracking with the exclusive fully-exploited motion information with the proposed reconstruct-to-embed strategy based on deep graph convolutional neural networks (GCN).
- Language Adaptive Weight Generation for Multi-Task Visual Grounding73
An active perception Visual Grounding framework based on Language Adaptive Weights, called VG-LAW, which serves as an expression-specific feature extractor through dynamic weights generated for various expressions and does not require additional modules for cross-modal interaction.
- Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions73
A comparative assessment of 25 commonly used classical SFs was conducted, and the results indicated that the superiority of the ML-based SFs could be fully guaranteed when sufficient similar targets were contained in the training set.
- DiffFashion: Reference-Based Fashion Design With Structure-Aware Transfer by Diffusion Models68
This work presents a novel diffusion model-based unsupervised structure-aware transfer method, namely DiffFashion, which outperforms state-of-the-art baseline models, generating more realistic images in the fashion design task.
- Rethinking of Radar’s Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment68
A new dataset, named CRUW, with a systematic annotator and performance evaluation system to address the radar object detection (ROD) task, which aims to classify and localize the objects in 3D purely from radar’s radio frequency (RF) images.
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- STSC-SNN: Spatio-Temporal Synaptic Connection with temporal convolution and attention for spiking neural networks61
This work takes inspiration from biological synapses and proposes a Spatio-Temporal Synaptic Connection SNN (STSC-SNN) model to enhance the spatio-temporal receptive fields of synaptic connections, thereby establishing temporal dependencies across layers.
- Split and Connect: A Universal Tracklet Booster for Multi-Object Tracking59
Combined with the proposed tracklet booster, existing trackers can achieve large improvements on the IDF1 score, which shows the effectiveness of the proposed TBooster.
- Anomaly Candidate Identification and Starting Time Estimation of Vehicles from Traffic Videos55
A novel two-stage framework for anomaly event detection in road traffic based on anomaly candidate identification and starting time estimation of vehicles is proposed.
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- MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided Diffusion with Visual Invariant52
MedM2G is the first medical generative model that unifies medical generation tasks of text-to-image, image-to-text, and unified generation of medical modalities (CT, MRI, X-ray), and it performs 5 medical generation tasks across 10 datasets, consistently outperforming various state-of-the-art works.
- Sustainable Road Pothole Detection: A Crowdsourcing Based Multi-Sensors Fusion Approach52
This study proposes a method that utilizes the fusion of accelerometer data and in-vehicle video data, which is uploaded by the participating users, and provides a novel approach for large-scale pavement anomaly detection.
- DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes49
The DIVOTrack is introduced: a novel baseline cross-view tracking method with a unified joint detection and cross-view tracking framework named CrossMOT, which learns object detection, single-view association, and cross-view matching with an all-in-one embedding model.
- Deep Vision Multimodal Learning: Methodology, Benchmark, and Trend48
The types of architectures used in multimodal learning, including feature extraction, modality aggregation, and multi-modality loss functions, are reviewed, and it is indicated that pretraining paradigm, unified multitask framework, missing and noisy modality, and multimodale task diversity could be the future trends and challenges in the deep vision multimodAL learning field.
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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 2026-10-14. 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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