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- 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.
- 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.
- SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction185
This paper proposes an end-to-end method to solve the task of Pair-wise Aspect and Opinion Terms Extraction (PAOTE), and treats the problem from a perspective of joint term and relation extraction rather than under the sequence tagging formulation performed in most prior works.
- 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.
- Towards Face Encryption by Generating Adversarial Identity Masks127
This work proposes a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks which can be overlaid on facial images, such that the original identities can be concealed without sacrificing the visual quality.
- Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning108
It is revealed that large language models are "lazy learners" that tend to exploit shortcuts in prompts for downstream tasks and a surprising finding is uncovered that larger models are more likely to utilize shortcuts in Prompts during inference.
- Towards discovering and understanding task hijacking in android102
Design flaws of Android multitasking are found which make all recent versions of Android vulnerable to task hijacking attacks, and possible mitigation techniques are discussed.
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- Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models91
This work proposes AR-LDM, a latent diffusion model auto-regressively conditioned on history captions and generated images that can generalize to new characters through adaptation and extends the text-conditioned method to multimodal conditioning.
- 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.
- Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network61
An Attribute-Specific Embedding Network (ASEN) is proposed to jointly learn multiple attribute-specific embeddings in an end-to-end manner, thus measure the fine-grained similarity in the corresponding space.
- Verifying security invariants in ExpressOS61
The evaluation shows that the performance of ExpressOS is comparable to an Android-based system, and it is found that ExpressOS adds 16% overhead on average to the page load latency time for nine popular web sites.
- 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.
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- Context-Aware Robust Fine-Tuning44
Context-Aware Robust Fine-tuning (CAR-FT) makes the context-aware ability of CLIP inherited into downstream tasks, and achieves both higher in-distribution (ID) and out-of-distribution (OOD) accuracy.
- ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing43
This paper conducts model debugging in in-distribution data to explore which object attributes a model may be sensitive to, and develops a toolkit for object editing with controls of backgrounds, sizes, positions, and directions, and creates a rigorous benchmark named ImageNet-E(diting) for evaluating the image classifier robustness in terms of object attributes.
- Fighting Against Deepfake: Patch&Pair Convolutional Neural Networks (PPCNN)43
Through the comprehensive evaluations on public datasets, it is demonstrated that the proposed PPCNN model performs better than existing detection methods and show better generalization.
- S-Eval: Towards Automated and Comprehensive Safety Evaluation for Large Language Models38
S-Eval is a novel LLM-based automated Safety Evaluation framework with a newly defined comprehensive risk taxonomy that can be flexibly configured and adapted to the rapid evolution of LLMs and accompanying new safety threats, test generation methods and safety critique methods thanks to the LLM-based architecture.
- Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning33
A novel Dynamic Mixture of Curriculum LoRA Experts (D-MoLE) method, which automatically evolves MLLM's architecture with controlled parameter budgets to continually adapt to new tasks while retaining previously learned knowledge, is proposed, the first study of continual learning for MLLMs from an architectural perspective.
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- Self-Supervised Adversarial Training26
Experimental results show that the self- supervised representation outperforms its supervised version in respect of robustness and self-supervised adversarial training can further improve the defense ability efficiently.
- Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks25
This work proposes an improved conditional layer named Bilinear Residual Layer (BRL) to learning more powerful representations for LBIE task and demonstrates that this method can generate images with higher quality when compared to previous LBIe techniques.
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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