Causal Recommendation

Generative AI (GenAI) is transforming creative workflows through the capability to synthesize and manipulate images via high-level prompts. Yet creatives are not well supported to receive recognition or reward for the use of their content in GenAI training. To this end we propose ProMark a causal attribution technique to attribute a synthetically generated image to its training data concepts like objects motifs templates artists or styles. The concept information is proactively embedded into the input training images using imperceptible watermarks and the diffusion models (unconditional or conditional) are trained to retain the corresponding watermarks in generated images. We show that we can embed as many as 2^ 16 unique watermarks into the training data and each training image can contain more than one watermark. ProMark can maintain image quality whilst outperforming correlation-based attribution. Finally several qualitative examples are presented providing the confidence that the presence of the watermark conveys a causative relationship between training data and synthetic images.

Music recommendation for videos attracts growing interest in multi-modal research. However existing systems focus primarily on content compatibility often ignoring the users' preferences. Their inability to interact with users for further refinements or to provide explanations leads to a less satisfying experience. We address these issues with MuseChat a first-of-its-kind dialogue-based recommendation system that personalizes music suggestions for videos. Our system consists of two key functionalities with associated modules: recommendation and reasoning. The recommendation module takes a video along with optional information including previous suggested music and user's preference as inputs and retrieves an appropriate music matching the context. The reasoning module equipped with the power of Large Language Model (Vicuna-7B) and extended to multi-modal inputs is able to provide reasonable explanation for the recommended music. To evaluate the effectiveness of MuseChat we build a large-scale dataset conversational music recommendation for videos that simulates a two-turn interaction between a user and a recommender based on accurate music track information. Experiment results show that MuseChat achieves significant improvements over existing video-based music retrieval methods as well as offers strong interpretability and interactability. The dataset of this work is available at https://dongzhikang.github.io/musechat.

Deep neural networks have demonstrated remarkable performance in point cloud classification. However previous works show they are vulnerable to adversarial perturbations that can manipulate their predictions. Given the distinctive modality of point clouds various attack strategies have emerged posing challenges for existing defenses to achieve effective generalization. In this study we for the first time introduce causal modeling to enhance the robustness of point cloud classification models. Our insight is from the observation that adversarial examples closely resemble benign point clouds from the human perspective. In our causal modeling we incorporate two critical variables the structural information (standing for the key feature leading to the classification) and the hidden confounders (standing for the noise interfering with the classification). The resulting overall framework CausalPC consists of three sub-modules to identify the causal effect for robust classification. The framework is model-agnostic and adaptable for integration with various point cloud classifiers. Our approach significantly improves the adversarial robustness of three mainstream point cloud classification models on two benchmark datasets. For instance the classification accuracy for DGCNN on ModelNet40 increases from 29.2% to 72.0% with CausalPC whereas the best-performing baseline achieves only 42.4%.

RGBT multispectral pedestrian detection has emerged as a promising solution for safety-critical applications that require day/night operations. However the modality bias problem remains unsolved as multispectral pedestrian detectors learn the statistical bias in datasets. Specifically datasets in multispectral pedestrian detection mainly distribute between ROTO (day) and RXTO (night) data; the majority of the pedestrian labels statistically co-occur with their thermal features. As a result multispectral pedestrian detectors show poor generalization ability on examples beyond this statistical correlation such as ROTX data. To address this problem we propose a novel Causal Mode Multiplexer (CMM) framework that effectively learns the causalities between multispectral inputs and predictions. Moreover we construct a new dataset (ROTX-MP) to evaluate modality bias in multispectral pedestrian detection. ROTX-MP mainly includes ROTX examples not presented in previous datasets. Extensive experiments demonstrate that our proposed CMM framework generalizes well on existing datasets (KAIST CVC-14 FLIR) and the new ROTX-MP. Our code and dataset are available open-source.

For safe motion planning in real-world autonomous vehicles require behavior prediction models that are reliable and robust to distribution shifts. The recent studies suggest that the existing learning-based trajectory prediction models do not posses such characteristics and are susceptible to small perturbations that are not present in the training data largely due to overfitting to spurious correlations while learning. In this paper we propose a causal disentanglement representation learning approach aiming to separate invariant (causal) and variant (spurious) features for more robust learning. Our method benefits from a novel intervention mechanism in the latent space that estimates potential distribution shifts resulted from spurious correlations using uncertain feature statistics hence maintaining the realism of interventions. To facilitate learning we propose a novel invariance objective based on the variances of the distributions over uncertain statistics to induce the model to focus on invariant representations during training. We conduct extensive experiments on two large-scale autonomous driving datasets and show that besides achieving state-of-the-art performance our method can significantly improve prediction robustness to various distribution shifts in driving scenes. We further conduct ablative studies to evaluate the design choices in our proposed framework.