In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate O(1/λT), but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain O(√(log (1/δ)/T)) with d is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately O(log (log (T)/δ)/λT).

Image composition involves seamlessly integrating given objects into a specific visual context. Current training-free methods rely on composing attention weights from several samplers to guide the generator. However, since these weights are derived from disparate contexts, their combination leads to coherence confusion and loss of appearance information. These issues worsen with their excessive focus on background generation, even when unnecessary in this task. This not only impedes their swift implementation but also compromises foreground generation quality. Moreover, these methods introduce unwanted artifacts in the transition area. In this paper, we formulate image composition as a subject-based local editing task, solely focusing on foreground generation. At each step, the edited foreground is combined with the noisy background to maintain scene consistency. To address the remaining issues, we propose PrimeComposer, a faster training-free diffuser that composites the images by well-designed attention steering across different noise levels. This steering is predominantly achieved by our Correlation Diffuser, utilizing its self-attention layers at each step. Within these layers, the synthesized subject interacts with both the referenced object and background, capturing intricate details and coherent relationships. This prior information is encoded into the attention weights, which are then integrated into the self-attention layers of the generator to guide the synthesis process. Besides, we introduce a Region-constrained Cross-Attention to confine the impact of specific subject-related tokens to desired regions, addressing the unwanted artifacts shown in the prior method thereby further improving the coherence in the transition area. Our method exhibits the fastest inference efficiency and extensive experiments demonstrate our superiority both qualitatively and quantitatively. The code is available at https://github.com/CodeGoat24/PrimeComposer.

Hierarchical Fine-Grained Visual Classification (HFGVC) assigns a label sequence (e.g., ["Albatross'', "Laysan Albatross'']) with a coarse to fine hierarchy to each object. It remains challenging to achieve high accuracy and consistency due to the small inter-class difference, large intra-class variance, and difficulty in modeling relationships among classification tasks at different granularities. In this paper, we propose an effective Consistency-Aware Feature Learning (CAFL) method for HFGVC to improve prediction consistency and classification accuracy simultaneously. Our key idea is to encode the prediction consistency constraint into a weak supervision mechanism via forward deduction and backward induction over the label hierarchy. Furthermore, we develop a disentanglement and bidirectional reinforcement classification head to extract the features for the classifiers at different granularities. Together with the stop-gradient policy and attention mechanism, they enable each classifier to exploit the features from the ones at other granularities without suffering from their conflicting gradients in training. We evaluate our method on several commonly-used fine-grained public datasets, including CUB-200-2011, FGVC-Aircraft, and Stanford Cars. The results show that our method not only achieves state-of-the-art classification accuracy but also effectively reduces inconsistency errors by 50% under the hierarchical fine-grained classification setting.

Human face recognition plays an important role in applications such as video surveillance, human computer interface, and face image database management. This paper presents an improved face recognition method for multi-pose face recognition in color images, which addresses the problems of illumination and pose variation. At first, color multi-pose faces image features were extracted based on Gabor wavelet with different orientations and scales filters, then the mean and standard deviation of the filtering image output are computed as features for face recognition. In addition, these features were fed up into support vector machine (SVM) for face recognition. Experimental results show that successful face recognition over a wide range of facial variations in color, position, scale, orientation, 3D pose, and expression in images from stereo-pair database.

Multi-person motion prediction remains a challenging problem due to the intricate motion dynamics and complex interpersonal interactions, where uncertainty escalates rapidly across the forecasting horizon. Existing approaches always overlook the motion dynamic modeling among the prediction frames to reduce the uncertainty, but leave it entirely up to the deep neural networks, which lacks a dynamic inductive bias, leading to suboptimal performance. This paper addresses this limitation by proposing an effective multi-person motion prediction method named Hybrid Supervision Transformer (HSFormer), which formulates the dynamic modeling within the prediction horizon as a novel hybrid supervision task. To be precise, our method performs a rolling predicting process equipped with a hybrid supervision mechanism, which enforces the model to be able to predict the pose in the next frames based on the (typically error-contained) earlier predictions. Addition to the standard supervision loss, two self and auxiliary supervision mechanisms, which minimize the distance of the predictions with error-contained inputs and the predictions with error-free inputs (ground truth) and guide the model to make accurate predictions based on the ground truth, are introduced to improve the robustness of our model to the input deviation in inference and stabilize the training process, respectively. The optimization techniques, such as stop-gradient, are extended to our model to improve the training efficiency. Furthermore, we develop a fine-grained spatio-temporal correlation capture module to assist the feature learning and reduce the uncertainties arising from the intricate and varying interactions among the individuals. Our approach achieves state-of-the-art results on multiple multi-person datasets in both short- and long-term prediction.