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- Adversarial Domain Adaptation with Domain Mixup520
This paper presents adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains.
- Deep Industrial Image Anomaly Detection: A Survey514
This paper provides a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets.
- DSFD: Dual Shot Face Detector506
A novel detection network named Dual Shot face Detector (DSFD) is proposed which inherits the architecture of SSD and introduces a Feature Enhance Module (FEM) for transferring the original feature maps to extend the single shot detector to dual shot detector.
- Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework436
This paper proposes a purely point-based framework for joint crowd counting and individual localization, called Point to Point Network (P2PNet), and proposes a new metric, called density Normalized Average Precision (nAP), to provide more comprehensive and more precise performance evaluation.
- Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking382
The two major novelties: chained structure and paired attentive regression, make CTracker simple, fast and effective, setting new MOTA records on MOT16 and MOT17 challenge datasets (67.6 and 66.6, respectively), without relying on any extra training data.
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- A Diffusion-Based Framework for Multi-Class Anomaly Detection336
A Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection, which consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion’s denoising network, and a feature-space pre-trained feature extractor.
- Learning Salient Boundary Feature for Anchor-free Temporal Action Localization329
This paper proposes the first purely anchor-free temporal localization method, which is both efficient and effective, and beats all anchor-based and actionness-guided methods with a remarkable margin on THUMOS14, achieving state-of-the-art results, and comparable ones on ActivityNet v1.3.
- Multimodal Industrial Anomaly Detection via Hybrid Fusion297
This paper proposes Multi-3D-Memory (M3DM), a novel multimodal anomaly detection method with hybrid fusion scheme that outperforms the state-of-the-art (SOTA) methods on both detection and segmentation precision on MVTec- 3D AD dataset.
- Rethinking Mobile Block for Efficient Attention-based Models296
This work rethinks lightweight infrastructure from efficient IRB and effective components of Transformer from a unified perspective, extending CNN-based IRB to attention-based models and abstracting a one-residual Meta Mobile Block (MMB) for lightweight model design.
- TEINet: Towards an Efficient Architecture for Video Recognition275
The proposed TEINet can achieve a good recognition accuracy on these datasets but still preserve a high efficiency, and is able to capture temporal structure flexibly and effectively, but also efficient for model inference.
- Surface Representation for Point Clouds250
Umbrella RepSurf surpasses the previous state-of-the-art by a large margin for classification, segmentation and detection on various benchmarks in terms of performance and efficiency, and two variants of Repsurf, Triangular Rep surf and UmbrellaRepSurf inspired by triangle meshes and umbrella curvature in computer graphics are explored.
- Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection243
A large-scale, Real-world, and multi-view Industrial Anomaly Detection dataset, named Real- I AD, which contains 150K high-resolution images of 30 different objects, an order of magnitude larger than existing datasets, making it more challenging than previous datasets.
- IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation232
This work devise an efficient encoder-decoder based network, termed IFRNet, for fast in-termediate frame synthesizing, which first extracts pyramid features from given inputs, and then refines the bilateral in-Termediate flow fields together with a powerful intermedi-ate feature until generating the desired output.
- Fast Learning of Temporal Action Proposal via Dense Boundary Generator228
An efficient and unified framework to generate temporal action proposals named Dense Boundary Generator (DBG), which draws inspiration from boundary-sensitive methods and implements boundary classification and action completeness regression for densely distributed proposals.
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- Learning by Analogy: Reliable Supervision From Transformations for Unsupervised Optical Flow Estimation218
This work twists the general unsupervised learning pipeline by running another forward pass with transformed data from augmentation, along with using transformed predictions of original data as the self-supervision signal, and introduces a lightweight network with multiple frames by a highly-shared flow decoder.
- To Choose or to Fuse? Scale Selection for Crowd Counting209
This paper proposes a Scale-Adaptive Selection Network (SASNet), which automatically learns the internal correspondence between the scales and the feature levels, and considers the predictions from other feature levels via weighted average, which helps to mitigate the gap between discrete feature levels and continuous scale variation.
- Learning a Few-shot Embedding Model with Contrastive Learning207
This paper proposes a novel contrastive training scheme dubbed infoPatch, exploiting the patch-wise relationship to substantially improve the popular infoNCE and investigates the contrastive learning with Noise Contrastive Estimation in a supervised manner for training a few-shot embedding model.
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- HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping173
This work proposes 3D shape-aware identity to control the face shape with the geometric supervision from 3DMM and 3D face reconstruction method and introduces the Semantic Facial Fusion module to optimize the combination of encoder and decoder features and make adaptive blending, which makes the results more photo-realistic.
- Deepfake Generation and Detection: A Benchmark and Survey158
This survey comprehensively reviews the latest developments in deepfake generation and detection, summarizing and analyzing current state-of-the-arts in this rapidly evolving field and benchmarking representative methods on widely adopted datasets to provide a comprehensive and up-to-date evaluation of the most influential published works.
- Efficient multimodal large language models: a survey157
This survey summarizes the timeline of representative efficient MLLMs, the current state of research in structures and strategies, and the applications, and the limitations of current efficient MLLM research and promising future directions are discussed.
- Real3D-AD: A Dataset of Point Cloud Anomaly Detection155
Reg3D-AD, a registration-based 3D anomaly detection method incorporating a novel feature memory bank that preserves local and global representations is proposed, addressing the limitations in the field of high-precision point cloud anomaly detection.
- SoftPatch: Unsupervised Anomaly Detection with Noisy Data144
Comprehensive experiments demonstrate that SoftPatch outperforms the state-of-the-art AD methods on the MVTecAD and BTAD benchmarks and is comparable to those methods under the setting without noise.
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 2026-10-12. 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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