Dong Zhang

Recently, adapting Vision Language Models (VLMs) to zero-shot visual classification by tuning class embedding with a few prompts (Test-time Prompt Tuning, TPT) or replacing class names with generated visual samples (support-set) has shown promising results. However, TPT cannot avoid the semantic gap between modalities while the support-set cannot be tuned. To this end, we draw on each other's strengths and propose a novel framework, namely TEst-time Support-set Tuning for zero-shot Video Classification (TEST-V). It first dilates the support-set with multiple prompts (Multi-prompting Support-set Dilation, MSD) and then erodes the support-set via learnable weights to mine key cues dynamically (Temporal-aware Support-set Erosion, TSE). Specifically, i) MSD expands the support samples for each class based on multiple prompts inquired from LLMs to enrich the diversity of the support-set. ii) TSE tunes the support-set with factorized learnable weights according to the temporal prediction consistency in a self-supervised manner to dig pivotal supporting cues for each class. TEST-V achieves state-of-the-art results across four benchmarks and shows good interpretability.

Human pose estimation is a challenging research task in the computer vision community due to the semantic ambiguity problem caused by inevitable occlusions, varying body shapes, and complex articulations. Although deep learning-based methods have significantly improved the performance of this task, existing feature upsampling operations, e.g., bilinear interpolation and transposed convolution, within current convolutional neural networks and Transformer frameworks suffer from a multitude of limitations, including the inability to adapt to specific tasks and the loss of fine-grained semantic details. In this work, we propose a simple yet effective two-step stable feature upsampling (SIU) strategy that addresses these limitations by leveraging a learnable and efficient upsampling operation. Specifically, we first apply periodic shuffling to increase the resolution of the feature maps. Secondly, we utilize convolution layers to adjust the size of feature channels to match those of the input feature maps. The proposed SIU enables the entire network to adapt to the specific feature requirements of the human pose estimation task, making it more effective in preserving spatial information. Quantitatively, extensive experimental results on the challenging COCO-WholeBody dataset validate that our approach outperforms state-of-the-art methods accurately and efficiently, and possesses strong transferability, making it applicable to a wide range of baselines. Moreover, the qualitative results validate that SIU can effectively eliminate the semantic ambiguity problem in challenging pose scenarios, such as occlusions and overlapping¹.

The evolution of advanced artificial intelligence generated content approaches has heightened concerns about deepfake, due to the sophisticated forgeries and concealed appearances they produce. To this end, the pre-trained Vision Transformer (ViT) model has become a de facto choice for deepfake detection, thanks to its powerful learning capability. Despite favorable results achieved by existing ViT-based methods, they have inherent limitations that could result in suboptimal performance in scenarios with continuously evolving forgery techniques, such as overfitting to single forgery patterns or placing excessive emphasis on dominant forgery regions. In this paper, we propose CUTA, a simple yet effective deepfake detection paradigm that utilizes ViT adapters as the medium and fully exploits the spatial- and frequency-domain features of given images to overcome the limitations of existing methods. Specifically, CUTA focuses on frequency domain masking within the input space, which obscures parts of the high-frequency image to intensify the training challenge while preserving subtle forgery cues in the frequency domain to facilitate comprehensive forgery representations. Furthermore, we propose two task-customized modules within the ViT model, i.e., the texture enhancement module and the multi-scale perceptron module, to seamlessly integrate local texture and rich contextual features. These two modules ensure an organic interaction between the task-specific forgery patterns and general semantic features within the pre-trained ViT framework. The experimental results on several publicly available benchmark datasets demonstrate CUTA’s superiority in performance, particularly showcasing its significant advantages in both cross-dataset and cross-manipulation scenarios.

The vision transformer (ViT) architecture offers significant advantages in object detection tasks. However, some limitations affect improving task performance. Firstly, the ViT relies heavily on inflexible position embedding, which causes poor performance when processing images with complex semantic dependencies. Secondly, class imbalance in large-scale datasets can easily cause training instability and inference bias. To overcome these limitations, we propose a generalized concordant ViT scheme for object detection (GCViTDet). Specifically, we first introduce a relevance enhancement strategy (RES) into the encoder-decoder structure, which is composed of the spatial enhanced position embeddings (SEPE) component, the cross multipooling attention (CMPA) component, and a global-local path. This strategy establishes semantic-rich dependencies through enhanced position embedding information and omni-feature representations. Subsequently, a bottom-up feature aggregation pathway is employed, utilizing a cross multi-pooling attention to improve the model’s capacity to capture semantic dependencies. This scheme enables the extraction of high-dimensional features that exhibit complex positional relationships. Besides, we propose a focal unified cross-entropy (FUCE) loss to solve the class imbalance problem during training by introducing a uniform threshold to regulate the similarity between positive and negative samples of different classes. Compared with existing methods, GCViTDet can not only capture more intricate positional relationships and semantic-rich dependencies but also alleviate the class-imbalance problem. Experimental results on the challenging MS-COCO dataset validate that GCViTDet can consistently improve performance over state-of-the-art object detection baseline models.