Traditional short video recommendations primarily enhance user retention by reinforcing existing user preferences, potentially leading to information cocoons. Conversely, proactive recommendations aim to diversify user interests by exposing users to content beyond their historical preferences. However, current proactive approaches face three limitations: (1) homogeneous receptivity assumption, neglecting individual differences in users' openness to new interests; (2) short-term item exposure without interest anchoring, focusing on item-level shifts rather than interest evolution; and (3) static feedback utilization, failing to incorporate dynamic user feedback during the recommendation adequately. To address these challenges, we propose ProRec-Video, a proactive framework that guides hierarchical interest transitions through three innovations. First, User Receptivity Profiling assesses individual openness for new interests, ensuring personalized transition pacing. Second, Hierarchical Interest Transition Planning decomposes complex interest shifts into intermediate steps to generate smooth interest transition paths and semantically coherent video sequences, addressing overemphasis on item exposure. Third, Dynamic Feedback Adaptation integrates agent-based simulation and Reflexion mechanisms to refine interest transition paths and video sequences based on real-time user feedback, enhancing adaptability and satisfaction. Extensive experiments on two datasets demonstrate that ProRec-Video achieves a significant improvement in proactive recommendation performance, with an interest transition success rate of 85% and a user satisfaction rate of 78.3%.

While large language models (LLMs) have shown remarkable capabilities in natural language processing, they struggle with complex, multi-step reasoning tasks involving knowledge graphs (KGs). Existing approaches that integrate LLMs and KGs either underutilize the reasoning abilities of LLMs or suffer from prohibitive computational costs due to tight coupling. To address these limitations, we propose a novel collaborative framework named EffiQA that can strike a balance between performance and efficiency via an iterative paradigm. EffiQA consists of three stages: global planning, efficient KG exploration, and self-reflection. Specifically, EffiQA leverages the commonsense capability of LLMs to explore potential reasoning pathways through global planning. Then, it offloads semantic pruning to a small plug-in model for efficient KG exploration. Finally, the exploration results are fed to LLMs for self-reflection to further improve global planning and efficient KG exploration. Empirical evidence on multiple KBQA benchmarks shows EffiQA’s effectiveness, achieving an optimal balance between reasoning accuracy and computational costs. We hope the proposed new framework will pave the way for efficient, knowledge-intensive querying by redefining the integration of LLMs and KGs, fostering future research on knowledge-based question answering.

Instruction tuning (IT) improves large language models (LLMs) by aligning their outputs with human instructions, but its success depends critically on training data quality, and datasets such as Alpaca often contain noisy or suboptimal examples that undermine fine-tuning.Prior selection strategies score samples using general-purpose LLMs (e.g., GPT), leveraging their strong language understanding yet introducing inherent biases that misalign with the target model's behavior and yield unstable downstream performance.Influence-based methods address this by estimating each example's marginal contribution to overall performance, but they typically assume additive contributions and therefore overlook higher-order interactions among samples.To overcome these limitations, we propose JI 2 S, a novel framework that jointly models both marginal and combinatorial influences within sample groups.Applying JI 2 S to select the top 1,000 most influential examples from Alpaca, we fine-tune LLaMA2-7B, Mistral-7B, and LLaMA2-13B and evaluate them on Open LLM Benchmarks, MT-Bench, and GPT-4-judged pairwise comparisons.Our experiments show that JI 2 S consistently outperforms full-dataset training and strong baselines, highlighting the value of capturing joint influence for high-quality instruction fine-tuning.We provide our code in this GitHub repository.

Image fusion aims to extract valuable information from each modality to create a fused image. Currently, state-of-the-art image fusion approaches tend to initially decompose each modality into distinct yet complementary features, and transfer beneficial information through carefully hand-crafted or learned fusion rules to the target. Nevertheless, previous approaches treat each modality in isolation before fusion, potentially under-utilising the complementary information available across modalities. In this word, we introduce a novel method called MEFusion that pioneers cross-modality mutual enhancement before feature decomposition. By harnessing the individual strengths of each modality, MEFusion elevates the overall quality and comprehensiveness of the fusion outcome. To facilitate a bidirectional enhancement for each feature across modalities, we have designed a pluggable co-attention mechanism that seamlessly integrates into a lightweight dual-path transformer. Furthermore, to enrich the details of each modality, we propose an unsupervised cross-modality mutual enhancement loss, which overcomes the limitations of requiring paired training data for enhancement tasks. Extensive experiments conducted on several benchmark datasets demonstrate the superiority of our proposed MEFusion method in terms of traditional fusion metrics and perceptual quality improvement of fused images.

Under-allocating or over-allocating computation resources (e.g., CPU cores) can prolong the completion time of data-parallel jobs in a distributed system. We present a predictor, ReLocag, to find the near-optimal number of CPU cores to minimize job completion time (JCT). ReLocag includes a graph convolutional network (GCN) and a fully-connected network (FCNN). The GCN learns the dependency between operations from the workflow of a job, and then the FCNN takes the workflow dependency together with other features (e.g., the input size, the number of CPU cores, the amount of memory, and the number of computation tasks) as input for JCT prediction. The prediction result can guide the user to determine the near-optimal number of CPU cores. Besides, we propose two effective strategies to overcome the time-consuming issue of training sample collection in big data applications. First, we develop an adaptive sampling method to collect essential samples judiciously. Second, we further design a cross-application transfer learning model to exploit the training samples collected from other applications. We conduct extensive experiments in a Spark cluster for 7 types of exemplary Spark applications. Results show that ReLocag improves the JCT prediction accuracy by 4-14 percent. Moreover, the CPU core consumption decreases by 58.2 percent.

Distillation-based learning boosts the performance of the miniaturized neural network based on the hypothesis that the representation of a teacher model can be used as structured and relatively weak supervision, and thus would be easily learned by a miniaturized model. However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a higher lower bound of congruence loss. In this work, we consider the knowledge distillation from the perspective of curriculum learning by teacher's routing. Instead of supervising the student model with a converged teacher model, we supervised it with some anchor points selected from the route in parameter space that the teacher model passed by, as we called route constrained optimization (RCO). We experimentally demonstrate this simple operation greatly reduces the lower bound of congruence loss for knowledge distillation, hint and mimicking learning. On close-set classification tasks like CIFAR and ImageNet, RCO improves knowledge distillation by 2.14% and 1.5% respectively. For the sake of evaluating the generalization, we also test RCO on the open-set face recognition task MegaFace. RCO achieves 84.3% accuracy on one-to-million task with only 0.8 M parameters, which push the SOTA by a large margin.

Most teacher-student frameworks based on knowledge distillation (KD) depend on a strong congruent constraint on instance level. However, they usually ignore the correlation between multiple instances, which is also valuable for knowledge transfer. In this work, we propose a new framework named correlation congruence for knowledge distillation (CCKD), which transfers not only the instance-level information but also the correlation between instances. Furthermore, a generalized kernel method based on Taylor series expansion is proposed to better capture the correlation between instances. Empirical experiments and ablation studies on image classification tasks (including CIFAR-100, ImageNet-1K) and metric learning tasks (including ReID and Face Recognition) show that the proposed CCKD substantially outperforms the original KD and other SOTA KD-based methods. The CCKD can be easily deployed in the majority of the teacher-student framework such as KD and hint-based learning methods.

Face recognition (FR) has recently made remark- able progress given the extraordinary capabilities of modern deep learning(DL) models. Though superior performance of DL based methods over human has been reported on benchmark dataset, it remains an open problem how those systems work in real-world condition with variations such as head pose, lighting, occlusion and image noises, in particular, in comparison to conventional FR approaches. It is hard to answer this question in a quantitative manner as current benchmark datasets are in lack of full range of variations. In this paper we propose a flexible approach to simulate face images under different variations with controllable degrees and based on the simulated dataset we quantitatively study how modern DL based and conventional FR methods perform. Based on the observations on a large number of synthesized face images, we draw several conclusions such as how head pose pitch and yaw variations will influence the FR systems and which part on the face is the most significant region, and in which situation conventional methods still show some advantages. The findings will not only be useful to assess current FR system in a quantitative manner but also shed light on future FR system design and data augmentation.

Deep clustering aims to cluster unlabeled data by embedding them into a subspace based on deep model. The key challenge of deep clustering is to learn discriminative representations for input data with high dimensions. In this paper, we present a deep discriminative clustering network for clustering the real-world images. We use a convolutional auto-encoder stacked with a softmax layer to predict clustering assignments. To learn a discriminative representations, the proposed approach adds discriminative loss as embedded regularization with relative entropy minimization. With the discriminative loss, the network can not only produce clustering assignments, but also learn discriminative features by reducing intra-cluster distance and increasing inter-cluster distance. We evaluate the proposed method on three datasets: MNIST-full, YTF and FRGC-v2.0. We outperform state-of-the-art results on MNIST-full and FRGC-v2.0 and achieve competitive result on YTF. The source code has been made publicly available at https://github.com/shaoxuying/DeepDiscriminativeClusteringNetwork.