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Works84 from public data
- Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain629
A novel Spatial-Phase Shallow Learning (SPSL) method is presented, which combines spatial image and phase spectrum to capture the up-sampling artifacts of face forgery to improve the transferability and theoretically analyze the validity of utilizing the phase spectrum.
- Dual Encoding for Zero-Example Video Retrieval305
This paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own and establishes a new state-of-the-art for zero-example video retrieval.
- Towards Robust Vision Transformer263
Robust Vision Transformer (RVT) is proposed, which is a new vision transformer and has superior performance with strong robustness and generalization ability compared with previous ViTs and state-of-the-art CNNs.
- One-dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications202
The concept-SemiPermeable structure is injected as a Membrane (SPM) into any DM to learn targeted erasing, and mean-time the alteration and erosion phenomenon is effectively mitigated via a novel Latent Anchoring fine-tuning strategy.
- Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink195
This work shows by simply using a laser beam that DNNs are easily fooled, and proposes a novel attack method called Adversarial Laser Beam (AdvLB), which enables manipulation of laser beam’s physical parameters to perform adversarial attack.
- A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking187
A comprehensive robustness benchmark called ARES-Bench is established on the image classification task and several important findings are drawn, including that there exists an intrinsic trade-off between the adversarial and natural robustness of specific noise types for the same model architecture.
- RAMS-Trans: Recurrent Attention Multi-scale Transformer for Fine-grained Image Recognition152
The recurrent attention multi-scale transformer (RAMS-Trans), which uses the transformer's self-attention to recursively learn discriminative region attention in a multi- scale manner, and achieves state-of-the-art results on three benchmark datasets.
- AdvDrop: Adversarial Attack to DNNs by Dropping Information137
This work proposes a novel adversarial attack, named AdvDrop, which crafts adversarial examples by dropping existing information of images, and explores the adversarial robustness of DNN models in a novel perspective by dropping imperceptible de-tails to craft adversarialExamples.
- Self-Supervised Learning for Few-Shot Image Classification134
This paper proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself.
- Monocular 3D Object Detection: An Extrinsic Parameter Free Approach129
A novel method to capture camera pose to formulate the detector free from extrinsic perturbation is proposed and yields the best performance compared with the other state-of-the-arts by a large margin on both KITTI 3D and nuScenes datasets.
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- Progressive Relation Learning for Group Activity Recognition103
A novel method based on deep reinforcement learning to progressively refine the low-level features and high-level relations of group activities and construct a semantic relation graph (SRG) to explicitly model the relations among persons.
- Toward Understanding and Boosting Adversarial Transferability From a Distribution Perspective89
This work proposes a novel method that crafts adversarial examples by manipulating the distribution of the image and can significantly improve the transferability of the crafted attacks and achieves state-of-the-art performance in both untargeted and targeted scenarios.
- Reading-Strategy Inspired Visual Representation Learning for Text-to-Video Retrieval84
This paper aims for the task of text-to-video retrieval, where given a query in the form of a natural-language sentence, it is asked to retrieve videos which are semantically relevant to the given query, from a great number of unlabeled videos.
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- Composite Adversarial Attacks61
A new procedure called Composite Adversarial Attack (CAA) is proposed for automatically searching the best combination of attack algorithms and their hyper-parameters from a candidate pool of 32 base attackers, and multi-objective NSGA-II genetic algorithm is adopted for finding the strongest attack policy with minimum complexity.
- QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval54
This paper makes the first attempt in Query-based Attack against Image Retrieval (QAIR), to completely subvert the top-k retrieval results by measuring the set similarity on theTop-K retrieval results before and after attacks and guide the gradient optimization.
- Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning49
Extensive experiments on three fashion-related datasets, i.e., FashionAI, DARN, and DeepFashion, show the effectiveness of ASEN for fine-grained fashion similarity prediction and its potential for fashion reranking.
- Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association48
A dynamic reweighting scheme to better explore the hard but valuable identities while filtering out the unlearnable identities is proposed and outperforms the previous methods in multiple settings, including voice-face matching, verification and retrieval.
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- MaxMatch: Semi-Supervised Learning With Worst-Case Consistency40
This article presents a generalization bound for SSL, and derives an SSL objective that minimizes the largest inconsistency between an original unlabeled sample and its multiple augmented variants, and provides a simple but effective algorithm to solve the proposed minimax problem.
- Optimizing Two-Way Partial AUC With an End-to-End Framework33
A generic framework to construct surrogate optimization problems, which supports efficient end-to-end training with deep learning, is proposed and theoretical analyses show that the objective function of the surrogate problems will achieve an upper bound of the original problem under mild conditions.
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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 2026-10-15. 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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