Action segmentation is a core challenge in high-level video understanding, aiming to partition untrimmed videos into segments and assign each a label from a predefined action set. Existing methods primarily address single-person activities with fixed action sequences, overlooking multi-person scenarios. In this work, we pioneer textual reference-guided human action segmentation in multi-person settings, where a textual description specifies the target person for segmentation. We introduce the first dataset for Referring Human Action Segmentation, i.e., RHAS133, built from 133 movies and annotated with 137 fine-grained actions with 33h video data, together with textual descriptions for this new task. Benchmarking existing action segmentation methods on RHAS133 using VLM-based feature extractors reveals limited performance and poor aggregation of visual cues for the target person. To address this, we propose a holistic-partial aware Fourierconditioned diffusion framework, i.e., HopaDIFF, leveraging a novel cross-input gate attentional xLSTM to enhance holistic-partial long-range reasoning and a novel Fourier condition to introduce more fine-grained control to improve the action segmentation generation. HopaDIFF achieves state-of-the-art results on RHAS133 in diverse evaluation settings.

The model can easily distinguish A and B according to the background (i.e., the so-called geometric skews [26]), but not according to the features of the class instance itself. However, if there is another class C, which is also in black background. In this tri-classification task (distinguishing A,B, and C), an ideal model should focus on the feature of the instance itself but not the background. This is one of the difficulties: distribution bias on samples, that some beneficial features (e.g., background) may be good for the classification, but not good for understanding the class (in a compositional way). Another difficulty is entanglement of the labels. We provide the labels in a relative way that the label of A is '0' and of B is '1', but not their true textual meanings (e.g., white paper and green leaves). The concept information is entangled and embedded into the label, thus, it is hard for the model to tell which visual features capture the corresponding concepts (i.e., white refers to the color feature and paper refers to the texture feature). We hope our understanding of this issue can inspire researchers to focus more on compositionality and design excellent continual learners.

The field of Fake Image Detection and Localization (FIDL) is highly fragmented, encompassing four domains: deepfake detection (Deepfake), image manipulation detection and localization (IMDL), artificial intelligence-generated image detection (AIGC), and document image manipulation localization (Doc). Although individual benchmarks exist in some domains, a unified benchmark for all domains in FIDL remains blank.

Federated Learning (FL) faces challenges due to data heterogeneity, which limits the global model's performance across diverse client distributions. Personalized Federated Learning (PFL) addresses this by enabling each client to process an individual model adapted to its local distribution. Many existing methods assume that certain global model parameters are difficult to train effectively in a collaborative manner under heterogeneous data. Consequently, they localize or fine-tune these parameters to obtain personalized models. In this paper, we reveal that both the feature extractor and classifier of the global model are inherently strong, and the primary cause of its suboptimal performance is the mismatch between local features and the global classifier.

The model can easily distinguish A and B according to the background (i.e., the so-called geometric skews [26]), but not according to the features of the class instance itself. However, if there is another class C, which is also in black background. In this tri-classification task (distinguishing A,B, and C), an ideal model should focus on the feature of the instance itself but not the background. This is one of the difficulties: distribution bias on samples, that some beneficial features (e.g., background) may be good for the classification, but not good for understanding the class (in a compositional way). Another difficulty is entanglement of the labels. We provide the labels in a relative way that the label of A is '0' and of B is '1', but not their true textual meanings (e.g., white paper and green leaves). The concept information is entangled and embedded into the label, thus, it is hard for the model to tell which visual features capture the corresponding concepts (i.e., white refers to the color feature and paper refers to the texture feature). We hope our understanding of this issue can inspire researchers to focus more on compositionality and design excellent continual learners.

A.1 Proof of Theorem 1 In this proof, we adopt a simplified version of our message-passing function that ignores the skipconnection: The HGNN trained in the experimental results shown in Figure 2 also does not use skip-connections and hence represents a theoretically-exact KTN component. In the real experiments, we use (1) skip-connections, exploiting their usual benefits (12), and (2) the trainable version of KTN. Without loss of generality, we prove the result for the case where R = {(s,t): s,t T }, meaning the type of an edge is identified with the (ordered) types of the neighbor nodes. In other words, there is only one edge modality possible, such as a social networks with multiple node types (e.g. "friendship" and "message"), the result is extended trivially (through with more algebraically-dense forms of ats and qts). The output of Aggregate is a concatenation of edge-type-specific aggregations (see Equation 3).

Neural networks are known to be vulnerable to adversarial attacks - slight but carefully constructed perturbations of the inputs which can drastically impair the network's performance. Many defense methods have been proposed for improving robustness of deep networks by training them on adversarially perturbed inputs. However, these models often remain vulnerable to new types of attacks not seen during training, and even to slightly stronger versions of previously seen attacks. In this work, we propose a novel approach to adversarial robustness, which builds upon the insights from the domain adaptation field. Our method, called Adversarial Feature Desensitization (AFD), aims at learning features that are invariant towards adversarial perturbations of the inputs. This is achieved through a game where we learn features that are both predictive and robust (insensitive to adversarial attacks), i.e. cannot be used to discriminate between natural and adversarial data. Empirical results on several benchmarks demonstrate the effectiveness of the proposed approach against a wide range of attack types and attack strengths. Our code is available at https://github.com/BashivanLab/afd.

A.1 Implementation of the GPs We use the GPyTorch4 package for the computations of GPs and their kernels. The NN linear kernel is implemented in all experiments as a 1-layer MLP with ReLU activations and hidden dimension 16. For the Spectral Mixture Kernel, we use 4 mixtures. A.2 Sines Dataset For the first experiments on sines functions, we use the dataset from [9]. For each task, the input points x are sampled from the range [ 5,5], and the target values y are obtained by applying y = Asin(x ')+, where the amplitude A and phase ' are drawn uniformly at random from ranges [0.1,5] and [0, ], respectively.