Tokens on Demand: Token Condensation as Training-free Test-time Adaptation

In this work, we introduce Token Condensation as Adaptation (TCA), a training-free approach designed to mitigate distribution shifts encountered by vision-language models (VLMs) during test-time inference. TCA bridges distribution gaps at the patch level by condensing image tokens that exhibit low attentiveness to the <cls> token. Recognizing the <cls> token may correspond to universal concepts, TCA identifies and tracks the most reliable <cls> tokens that align specifically with target classes from historical data streams. To achieve this, we propose a context token reservoir (CTR), which retains tokens with the lowest uncertainty as ``anchors" to guide the preservation of class-relevant tokens during inference. These anchors, in turn, act as token-level classifiers to correct VLM predictions and improve visual-text alignment. Utilizing anchors sampled from CTR, TCA condenses tokens through two operations: (1) pruning class-irrelevant tokens that consistently rank low across all attention heads to reach cross-head consensus on their irrelevance, and (2) merging the remaining class-ambiguous tokens into representative centers using coreset selection, maintaining linear computational complexity. As the first method to explore token efficiency in test-time adaptation, TCA consistently demonstrates superior performance across cross-dataset and out-of-distribution adaptation tasks, reducing GFLOPs by 12.2% to 48.9% while achieving accuracy improvements up to 21.4% against the strongest baseline without introducing additional parameters.

Mining Your Own Secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models

Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However, in the real world, a user may wish to personalize a model on multiple concepts but one at a time, with no access to the data from previous concepts due to storage/privacy concerns. When faced with this continual learning (CL) setup, most personalization methods fail to find a balance between acquiring new concepts and retaining previous ones -- a challenge that continual personalization (CP) aims to solve. Inspired by the successful CL methods that rely on class-specific information for regularization, we resort to the inherent class-conditioned density estimates, also known as diffusion classifier (DC) scores, for continual personalization of text-to-image diffusion models. Namely, we propose using DC scores for regularizing the parameter-space and function-space of text-to-image diffusion models, to achieve continual personalization. Using several diverse evaluation setups, datasets, and metrics, we show that our proposed regularization-based CP methods outperform the state-of-the-art C-LoRA, and other baselines. Finally, by operating in the replay-free CL setup and on low-rank adapters, our method incurs zero storage and parameter overhead, respectively, over the state-of-the-art.

UIR-LoRA: Achieving Universal Image Restoration through Multiple Low-Rank Adaptation

Existing unified methods typically treat multi-degradation image restoration as a multi-task learning problem. Despite performing effectively compared to single degradation restoration methods, they overlook the utilization of commonalities and specificities within multi-task restoration, thereby impeding the model's performance. Inspired by the success of deep generative models and fine-tuning techniques, we proposed a universal image restoration framework based on multiple low-rank adapters (LoRA) from multi-domain transfer learning. Our framework leverages the pre-trained generative model as the shared component for multi-degradation restoration and transfers it to specific degradation image restoration tasks using low-rank adaptation. Additionally, we introduce a LoRA composing strategy based on the degradation similarity, which adaptively combines trained LoRAs and enables our model to be applicable for mixed degradation restoration. Extensive experiments on multiple and mixed degradations demonstrate that the proposed universal image restoration method not only achieves higher fidelity and perceptual image quality but also has better generalization ability than other unified image restoration models. Our code is available at https://github.com/Justones/UIR-LoRA.

Highway Graph to Accelerate Reinforcement Learning

Reinforcement Learning (RL) algorithms often suffer from low training efficiency. A strategy to mitigate this issue is to incorporate a model-based planning algorithm, such as Monte Carlo Tree Search (MCTS) or Value Iteration (VI), into the environmental model. The major limitation of VI is the need to iterate over a large tensor. These still lead to intensive computations. We focus on improving the training efficiency of RL algorithms by improving the efficiency of the value learning process. For the deterministic environments with discrete state and action spaces, a non-branching sequence of transitions moves the agent without deviating from intermediate states, which we call a highway. On such non-branching highways, the value-updating process can be merged as a one-step process instead of iterating the value step-by-step. Based on this observation, we propose a novel graph structure, named highway graph, to model the state transition. Our highway graph compresses the transition model into a concise graph, where edges can represent multiple state transitions to support value propagation across multiple time steps in each iteration. We thus can obtain a more efficient value learning approach by facilitating the VI algorithm on highway graphs. By integrating the highway graph into RL (as a model-based off-policy RL method), the RL training can be remarkably accelerated in the early stages (within 1 million frames). Comparison against various baselines on four categories of environments reveals that our method outperforms both representative and novel model-free and model-based RL algorithms, demonstrating 10 to more than 150 times more efficiency while maintaining an equal or superior expected return, as confirmed by carefully conducted analyses. Moreover, a deep neural network-based agent is trained using the highway graph, resulting in better generalization and lower storage costs.

CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models

Continual learning (CL) aims to help deep neural networks to learn new knowledge while retaining what has been learned. Recently, pre-trained vision-language models such as CLIP, with powerful generalization ability, have been gaining traction as practical CL candidates. However, the domain mismatch between the pre-training and the downstream CL tasks calls for finetuning of the CLIP on the latter. The deterministic nature of the existing finetuning methods makes them overlook the many possible interactions across the modalities and deems them unsafe for high-risk CL tasks requiring reliable uncertainty estimation. To address these, our work proposes Continual LeArning with Probabilistic finetuning (CLAP). CLAP develops probabilistic modeling over task-specific modules with visual-guided text features, providing more reliable fine-tuning in CL. It further alleviates forgetting by exploiting the rich pre-trained knowledge of CLIP for weight initialization and distribution regularization of task-specific modules. Cooperating with the diverse range of existing prompting methods, CLAP can surpass the predominant deterministic finetuning approaches for CL with CLIP. Lastly, we study the superior uncertainty estimation abilities of CLAP for novel data detection and exemplar selection within CL setups. Our code is available at \url{https://github.com/srvCodes/clap4clip}.

Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning

Continual learning aims to learn from a stream of continuously arriving data with minimum forgetting of previously learned knowledge. While previous works have explored the effectiveness of leveraging the generalizable knowledge from pre-trained models in continual learning, existing parameter-efficient fine-tuning approaches focus on the use of a predetermined or task-wise set of adapters or prompts. However, these approaches still suffer from forgetting due to task interference on jointly used parameters or restricted flexibility. The reliance on a static model architecture may lead to the allocation of excessive parameters that are not essential or, conversely, inadequate adaptation for downstream tasks, given that the scale and distribution of incoming data are unpredictable in continual learning. We propose Self-Expansion of pre-trained models with Modularized Adaptation (SEMA), a novel fine-tuning approach which automatically decides to reuse or add adapter modules on demand in continual learning, depending on whether drastic distribution shift that could not be handled by existing modules is detected at different representation levels. We design each adapter module to consist of an adapter and a representation descriptor, specifically, implemented as an autoencoder. The representation descriptor functions as a distributional shift indicator during training and triggers adapter expansion. For better usage of the adapters, an expandable weighting router is learned jointly for mixture of adapter outputs. By comparing with vision-transformer-based continual learning adaptation methods, we demonstrate that the proposed framework outperforms the state-of-the-art without memory rehearsal.

Identifiable Latent Neural Causal Models

Causal representation learning seeks to uncover latent, high-level causal representations from low-level observed data. It is particularly good at predictions under unseen distribution shifts, because these shifts can generally be interpreted as consequences of interventions. Hence leveraging {seen} distribution shifts becomes a natural strategy to help identifying causal representations, which in turn benefits predictions where distributions are previously {unseen}. Determining the types (or conditions) of such distribution shifts that do contribute to the identifiability of causal representations is critical. This work establishes a {sufficient} and {necessary} condition characterizing the types of distribution shifts for identifiability in the context of latent additive noise models. Furthermore, we present partial identifiability results when only a portion of distribution shifts meets the condition. In addition, we extend our findings to latent post-nonlinear causal models. We translate our findings into a practical algorithm, allowing for the acquisition of reliable latent causal representations. Our algorithm, guided by our underlying theory, has demonstrated outstanding performance across a diverse range of synthetic and real-world datasets. The empirical observations align closely with the theoretical findings, affirming the robustness and effectiveness of our approach.

Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure

In real-world applications, image degeneration caused by adverse weather is always complex and changes with different weather conditions from days and seasons. Systems in real-world environments constantly encounter adverse weather conditions that are not previously observed. Therefore, it practically requires adverse weather removal models to continually learn from incrementally collected data reflecting various degeneration types. Existing adverse weather removal approaches, for either single or multiple adverse weathers, are mainly designed for a static learning paradigm, which assumes that the data of all types of degenerations to handle can be finely collected at one time before a single-phase learning process. They thus cannot directly handle the incremental learning requirements. To address this issue, we made the earliest effort to investigate the continual all-in-one adverse weather removal task, in a setting closer to real-world applications. Specifically, we develop a novel continual learning framework with effective knowledge replay (KR) on a unified network structure. Equipped with a principal component projection and an effective knowledge distillation mechanism, the proposed KR techniques are tailored for the all-in-one weather removal task. It considers the characteristics of the image restoration task with multiple degenerations in continual learning, and the knowledge for different degenerations can be shared and accumulated in the unified network structure. Extensive experimental results demonstrate the effectiveness of the proposed method to deal with this challenging task, which performs competitively to existing dedicated or joint training image restoration methods. Our code is available at https://github.com/xiaojihh/CL_all-in-one.

Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning

Continual learning requires a model to adapt to ongoing changes in the data distribution, and often to the set of tasks to be performed. It is rare, however, that the data and task changes are completely unpredictable. Given a description of an overarching goal or data theme, which we call a realm, humans can often guess what concepts are associated with it. We show here that the combination of a large language model and an image generation model can similarly provide useful premonitions as to how a continual learning challenge might develop over time. We use the large language model to generate text descriptions of semantically related classes that might potentially appear in the data stream in future. These descriptions are then rendered using Stable Diffusion to generate new labelled image samples. The resulting synthetic dataset is employed for supervised pre-training, but is discarded prior to commencing continual learning, along with the pre-training classification head. We find that the backbone of our pre-trained networks can learn representations useful for the downstream continual learning problem, thus becoming a valuable input to any existing continual learning method. Although there are complexities arising from the domain gap between real and synthetic images, we show that pre-training models in this manner improves multiple Class Incremenal Learning (CIL) methods on fine-grained image classification benchmarks. Supporting code can be found at https://github.com/cl-premonition/premonition.

SDGE: Stereo Guided Depth Estimation for 360$^\circ$ Camera Sets

Depth estimation is a critical technology in autonomous driving, and multi-camera systems are often used to achieve a 360$^\circ$ perception. These 360$^\circ$ camera sets often have limited or low-quality overlap regions, making multi-view stereo methods infeasible for the entire image. Alternatively, monocular methods may not produce consistent cross-view predictions. To address these issues, we propose the Stereo Guided Depth Estimation (SGDE) method, which enhances depth estimation of the full image by explicitly utilizing multi-view stereo results on the overlap. We suggest building virtual pinhole cameras to resolve the distortion problem of fisheye cameras and unify the processing for the two types of 360$^\circ$ cameras. For handling the varying noise on camera poses caused by unstable movement, the approach employs a self-calibration method to obtain highly accurate relative poses of the adjacent cameras with minor overlap. These enable the use of robust stereo methods to obtain high-quality depth prior in the overlap region. This prior serves not only as an additional input but also as pseudo-labels that enhance the accuracy of depth estimation methods and improve cross-view prediction consistency. The effectiveness of SGDE is evaluated on one fisheye camera dataset, Synthetic Urban, and two pinhole camera datasets, DDAD and nuScenes. Our experiments demonstrate that SGDE is effective for both supervised and self-supervised depth estimation, and highlight the potential of our method for advancing downstream autonomous driving technologies, such as 3D object detection and occupancy prediction.