Event-based video reconstruction has garnered increasing attention due to its advantages, such as high dynamic range and rapid motion capture capabilities. However, current methods often prioritize the extraction of temporal information from continuous event flow, leading to an overemphasis on low-frequency texture features in the scene, resulting in over-smoothing and blurry artifacts. Addressing this challenge necessitates the integration of conditional information, encompassing temporal features, low-frequency texture, and high-frequency events, to guide the Denoising Diffusion Probabilistic Model (DDPM) in producing accurate and natural outputs. To tackle this issue, we introduce a novel approach, the Temporal Residual Guided Diffusion Framework, which effectively leverages both temporal and frequency-based event priors. Our framework incorporates three key conditioning modules: a pre-trained low-frequency intensity estimation module, a temporal recurrent encoder module, and an attention-based high-frequency prior enhancement module. In order to capture temporal scene variations from the events at the current moment, we employ a temporal-domain residual image as the target for the diffusion model. Through the combination of these three conditioning paths and the temporal residual framework, our framework excels in reconstructing high-quality videos from event flow, mitigating issues such as artifacts and over-smoothing commonly observed in previous approaches. Extensive experiments conducted on multiple benchmark datasets validate the superior performance of our framework compared to prior event-based reconstruction methods. Our code will be released upon acceptance.
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Idling Neurons, Appropriately Lenient Workload During Fine-tuning Leads to Better Generalization
PDF ↗Pre-training on large-scale datasets has become a fundamental method for training deep neural networks. Pre-training provides a better set of parameters than random initialization, which reduces the training cost of deep neural networks on the target task. In addition, pre-training also provides a large number of feature representations, which may help improve generalization capabilities. However, this potential advantage has not received enough attention and has been buried by rough fine-tuning. Based on some exploratory experiments, this paper rethinks the fine-tuning process and gives a new perspective on understanding fine-tuning. Moreover, this paper proposes some plug-and-play fine-tuning strategies as alternatives for simple fine-tuning. These fine-tuning strategies all preserve pre-trained features better by creating idling of some neurons, leading to better generalization.
Recent studies on inverse problems have proposed posterior samplers that leverage the pre-trained diffusion models as a powerful prior. The attempts have paved the way for using diffusion models in a wide range of inverse problems. However, the existing methods entail computationally demanding iterative sampling procedures and optimize a separate solution for each measurement, which leads to limited scalability and lack of generalization capability across unseen samples. To address these limitations, we propose a novel approach, Diffusion prior-based Amortized Variational Inference (DAVI) that solves inverse problems with a diffusion prior from an amortized variational inference perspective. Specifically, instead of the separate measurement-wise optimization, our amortized inference learns a function that directly maps measurements to the implicit posterior distributions of corresponding clean data, enabling a single-step posterior sampling even for unseen measurements. The proposed method learns the function by minimizing the Kullback-Leibler divergence between the implicit distributions and the true posterior distributions with multiple measurements using objectives derived based on variational inference. Extensive experiments across three image restoration tasks, e.g., Gaussian deblur, 4x super-resolution, and box inpainting with two benchmark datasets, demonstrate our superior performance over strong diffusion model-based methods.
The seamless transformation of textual input into natural and expressive sign language holds profound societal significance. Sign language is not solely about hand gestures. It encompasses vital facial expressions and mouth movements essential for nuanced communication. Achieving both semantic precision and emotional resonance in text-to-sign language translation is of paramount importance. Our work pioneers direct end-to-end translation of text into sign videos, encompassing a realistic representation of the entire body and facial expressions. We go beyond traditional diffusion models by tailoring the multi-model conditions for sign video. Additionally, our modified motion-aware sign generation framework enhances alignment between text and visual cues in sign language, further improving the quality of the generated sign video. Extensive experiments show that our approach significantly outperforms the state-of-the-art approaches in terms of semantic consistency, naturalness, and expressiveness, presenting benchmark quantitative results on the RWTH-2014, RWTH-2014-T, WLASL, CSL-Daily, and AUTSL.
The imperative for smart surveillance systems to robustly detect anomalies poses a unique challenge given the sensitivity of visual data and privacy concerns. We propose a novel Federated Learning framework for Video Anomaly Detection that operates under the constraints of data heterogeneity and privacy preservation. We utilize Federated Visual Consistency Clustering to group clients on the server side. Further innovation is realized with an Adaptive Semantic-Enhanced Distillation strategy that infuses public video knowledge into our framework. During this process, Large Language Models are utilized for semantic generation and calibration of public videos. These video-text pairs are then used to fine-tune a multimodal network, which serves as a teacher in updating the global model. This approach not only refines video representations but also increases sensitivity to anomalous events. Our extensive evaluations showcase FedVAD's proficiency in boosting unsupervised and weakly supervised anomaly detection, rivaling centralized training paradigms while preserving privacy. The code will be made available publicly at https://anonymous.4open.science/r/FedVAD-BF51.
AvatarPose: Avatar-guided 3D Pose Estimation of Close Human Interaction from Sparse Multi-view Videos
PDF ↗Despite progress in human motion capture, existing multi-view methods often face challenges in estimating the 3D pose and shape of multiple closely interacting people. This difficulty arises from reliance on accurate 2D joint estimations, which are hard to obtain due to occlusions and body contact when people are in close interaction. To address this, we propose a novel method leveraging the personalized implicit neural avatar of each individual as a prior, which significantly improves the robustness and precision of this challenging pose estimation task. Concretely, the avatars are efficiently reconstructed via layered volume rendering from sparse multi-view videos. The reconstructed avatar prior allows for direct optimization of 3D poses based on color and silhouette rendering loss, bypassing the issues associated with noisy 2D detections. To handle interpenetration, we propose a collision loss on the overlapping shape region of avatars to add penetration constraints. Moreover, both 3D poses and avatars are optimized in an alternating manner. Our experimental results demonstrate state-of-the-art performance on several public datasets.
For image generation with diffusion models (DMs), a negative prompt (n) can be used to complement the text prompt (p), helping define properties not desired in the synthesized image. While this improves prompt adherence and image quality, finding good negative prompts is challenging. We argue that this is due to a semantic gap between humans and DMs, which makes good negative prompts for DMs appear unintuitive to humans. To bridge this gap, we propose a new diffusion-negative prompting (DNP) strategy. DNP is based on a new procedure to sample images that are least compliant with p under the distribution of the DM, denoted as diffusion-negative sampling (DNS). Given p, one such image is sampled, which is then translated into natural language by the user or a captioning model, to produce the negative prompt (n*). The pair (p, n*) is finally used to prompt the DM. DNS is straightforward to implement and requires no training. Experiments and human evaluations show that DNP performs well both quantitatively and qualitatively and can be easily combined with several DM variants.
Exemplar-free class-incremental learning using a backbone trained from scratch and starting from a small first task presents a significant challenge for continual representation learning. Prototype-based approaches, when continually updated, face the critical issue of semantic drift due to which the old class prototypes drift to different positions in the new feature space. Through an analysis of forgetting in prototype-based continual learning, we show that forgetting is not due to diminished discriminative power of the feature extractor, and can potentially be corrected by drift compensation. To address this, we propose a novel feature drift correction method called Learnable Drift Compensation (LDC). LDC can effectively mitigate drift in any moving backbone, whether supervised or unsupervised. LDC is fast and straightforward to integrate on top of existing continual learning approaches. Finally, we showcase how LDC can be applied in combination with self-supervised CL methods, resulting in the first exemplar-free semi-supervised continual learning approach. We achieve state-of-the-art performance in both supervised and semi-supervised settings across multiple datasets.
Recently, diffusion models have increasingly demonstrated their capabilities in vision understanding. By leveraging prompt-based learning to construct sentences, these models have shown proficiency in classification and visual grounding tasks. However, existing approaches primarily showcase their ability to perform sentence-level localization, leaving the potential for leveraging contextual information for phrase-level understanding largely unexplored. In this paper, we utilize Panoptic Narrative Grounding (PNG) as a proxy task to investigate this capability further. PNG aims to segment object instances mentioned by multiple noun phrases within a given narrative text. Specifically, we introduce the DiffPNG framework, a straightforward yet effective approach that fully capitalizes on the diffusion's architecture for segmentation by decomposing the process into a sequence of localization, segmentation, and refinement steps. The framework initially identifies anchor points using cross-attention mechanisms and subsequently performs segmentation with self-attention to achieve zero-shot PNG. Moreover, we introduce a refinement module based on SAM to enhance the quality of the segmentation masks. Our extensive experiments on the PNG dataset demonstrate that DiffPNG achieves strong performance in the zero-shot PNG task setting, conclusively proving the diffusion model's capability for context-aware, phrase-level understanding.
In this paper, we introduce a novel text-to-avatar generation method that separately generates the human body and the clothes and allows high-quality animation on the generated avatar. While recent advancements in text-to-avatar generation have yielded diverse human avatars from text prompts, these methods typically combine all elements—clothes, hair, and body—into a single 3D representation. Such an entangled approach poses challenges for downstream tasks like editing or animation. To overcome these limitations, we propose a novel disentangled 3D avatar representation named Sequentially Offset-SMPL (SO-SMPL), building upon the SMPL model. SO-SMPL represents the human body and clothes with two separate meshes but associates them with offsets to ensure the physical alignment between the body and the clothes. Then, we design a Score Distillation Sampling (SDS)-based distillation framework to generate the proposed SO-SMPL representation from text prompts. In comparison with existing text-to-avatar methods, our approach not only achieves higher texture and geometry quality and better semantic alignment with text prompts, but also significantly improves the visual quality of character animation, virtual try-on, and avatar editing. We further show that our method also allows for generating complex garments, such as multi-layered clothes and skirts.
While recent advancements in model fine-tuning predominantly emphasize the utilization of low-rank adaptation (LoRA), we propose an alternative approach centered on reducing the precision of adaptation matrices. In particular, we depart from the common viewpoint that considers adaptation matrices solely as weight differences, and reinterpret them as "control variables'' to perturb pre-trained ViT systems. This new perspective enables the establishment of a control-oriented framework, facilitating the exploration of optimal controls guided by the Pontryagin Maximum Principle. Furthermore, we demonstrate that for bounded control sets such as hypercubes, the optimal controls often take on boundary values, leading naturally to a binary controller design. Theoretical analysis reveals that employing a binary control strategy achieves the same reachable state as its full-precision counterpart in the continuous idealisation of deep residual structures, a finding corroborated by later empirical investigations. Our studies further indicate that the controller's rank holds greater significance than its precision. As such, opting for low-precision yet high-rank controls is demonstrated to obtain better performance for practical vision tasks.
Existing end-to-end trackers for vision-based 3D perception suffer from performance degradation due to the conflict between detection and tracking tasks. In this work, we get to the bottom of this conflict, which was vaguely attributed to incompatible task-specific object features previously. We find the conflict between the two tasks lies in their partially conflicted classification gradients, which stems from their subtle difference in positive sample assignments. Based on this observation, we propose to coordinate those conflicted gradients by accurately identifying object queries with contradicted positivity in the two tasks. We also dynamically mask all attention between contracted object queries and modify the tracking classification loss to suppress inaccurate predictions. To this end, we propose OneTrack, the first one-stage joint detection and tracking model that bridges the gap between detection and tracking under a unified object feature representation. On the nuScenes camera-based object tracking benchmark, OneTrack outperforms previous works by 6.9% AMOTA on the validation set and by 3.3% AMOTA on the test set. The code will be released.
Gait recognition is to seek correct matches for query individuals by their unique walking patterns. However, current methods focus solely on extracting individual-specific features, overlooking ``interpersonal" relationships. In this paper, we propose a novel Relation Descriptor that captures not only individual features but also relations between test gaits and pre-selected gait anchors. Specifically, we reinterpret classifier weights as gait anchors and compute similarity scores between test features and these anchors, which re-expresses individual gait features into a similarity relation distribution. In essence, the relation descriptor offers a holistic perspective that leverages the collective knowledge stored within the classifier's weights, emphasizing meaningful patterns and enhancing robustness. Despite its potential, relation descriptor poses dimensionality challenges since its dimension depends on the training set's identity count. To address this, we propose Farthest gait-Anchor Selection to identify the most discriminative gait anchors and an Orthogonal Regularization Loss to increase diversity within gait anchors. Compared to individual-specific features extracted from the backbone, our relation descriptor can boost the performance nearly without any extra costs. We evaluate the effectiveness of our method on the popular GREW, Gait3D, OU-MVLP, CASIA-B, and CCPG, showing that our method consistently outperforms the baselines and achieves state-of-the-art performance.
In the application of Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) classification, attention mechanisms often focus on a subset of discriminative instances, which are closely linked to overfitting. To mitigate overfitting, we present Attention-Challenging MIL (ACMIL). ACMIL combines two techniques based on separate analyses for attention value concentration. Firstly, UMAP of instance features reveals various patterns among discriminative instances, with existing attention mechanisms capturing only some of them. To remedy this, we introduce Multiple Branch Attention (MBA) to capture more discriminative instances using multiple attention branches. Secondly, examination of the cumulative value of Top-K attention scores indicates that a tiny number of instances dominate the majority of attention. In response, we present Stochastic Top-K Instance Masking (STKIM), which masks out a portion of instances with Top-K attention values and allocates their attention values to the remaining instances. The extensive experimental results on three WSI datasets with two pre-trained backbones reveal that our ACMIL outperforms state-of-the-art methods. Additionally, through heatmap visualization and UMAP visualization, this paper extensively illustrates ACMIL's effectiveness in suppressing attention value concentration and overcoming the overfitting challenge. The source code is available in the Supplementary Materials.
The target duration of a synthesized human motion is a critical attribute that requires modeling control over the motion dynamics and style. Speeding up an action performance is not merely fast-forwarding it. However, state-of-the-art techniques for human behavior synthesis have limited control over the target sequence length. We introduce the problem of generating length-aware 3D human motion sequences from textual descriptors, and we propose a novel model to synthesize motions of variable target lengths, which we dub "Length-Aware Latent Diffusion” (LADiff). LADiff consists of two new modules: 1) a length-aware variational auto-encoder to learn motion representations with length-dependent latent codes; 2) a length-conforming latent diffusion model to generate motions with a richness of details that increases with the required target sequence length. LADiff significantly improves over the state-of-the-art across most of the existing motion synthesis metrics on the two established benchmarks of HumanML3D and KIT-ML. The code will be open-sourced.
Text-to-image models encounter safety issues, including concerns related to copyright and Not-Safe-For-Work (NSFW) content. Despite several methods have been proposed for erasing inappropriate concepts from diffusion models, they often exhibit incomplete erasure, lack robustness against red-teaming tools, and inadvertently damage generation ability. In this work, we introduce Reliable and Efficient Concept Erasure (RECE), a novel approach that modifies the model without necessitating additional training. Specifically, RECE efficiently leverages a closed-form solution to compute new embeddings capable of regenerating erased concepts on the model subjected to concept erasure. To mitigate inappropriate content potentially represented by derived embeddings, RECE further projects them onto harmless concepts in cross-attention layers. The generation and erasure of new representation embeddings are conducted iteratively to achieve a thorough erasure of inappropriate concepts. Besides, to preserve the model's generation ability, RECE introduces an additional regularization term to the closed-form solution, resulting in minimizing the impact on unrelated concepts during the erasure process. Benchmarking against previous approaches, our method achieves more efficient and thorough erasure with small damage to generation ability and demonstrates enhanced robustness against red-teaming tools.
WRIM-Net: Wide-Ranging Information Mining Network for Visible-Infrared Person Re-Identification
PDF ↗For the visible-infrared person re-identification (VI-ReID) task, one of the primary challenges lies in significant cross-modality discrepancy. Existing methods struggle to conduct modality-invariant information mining. They often focus solely on mining singular dimensions like spatial or channel, and overlook the extraction of specific-modality multi-dimension information. To fully mine modality-invariant information across a wide range, we introduce the Wide-Ranging Information Mining Network (WRIM-Net), which mainly comprises a Multi-dimension Interactive Information Mining (MIIM) module and an Auxiliary-Information-based Contrastive Learning (AICL) approach. Empowered by the proposed Global Region Interaction (GRI), MIIM comprehensively mines non-local spatial and channel information through intra-dimension interaction. Moreover, Thanks to the low computational complexity design, separate MIIM can be positioned in shallow layers, enabling the network to better mine specific-modality multi-dimension information. AICL, by introducing the novel Cross-Modality Key-Instance Contrastive (CMKIC) loss, effectively guides the network in extracting modality-invariant information. We conduct extensive experiments not only on the well-known SYSU-MM01 and RegDB datasets but also on the latest large-scale cross-modality LLCM dataset. The results demonstrate WRIM-Net's superiority over state-of-the-art methods.
Combining machine clustering with deep models has shown remarkable superiority in deep clustering, which modifies the data processing pipeline into two alternating phases: feature clustering and model training. However, such alternating schedule may lead to instability and computational burden issues. To tackle these challenges, we propose a centerless clustering algorithm called Probability Aggregation Clustering (PAC) to proactively adapt deep learning technologies, enabling easy deployment in online deep clustering. PAC circumvents the cluster center and aligns the probability space and distribution space by formulating clustering as an optimization problem with a novel objective function. Based on the computation mechanism of the PAC, we propose a general online probability aggregation mperform stable feature clustering overodule to mini-batch data and further construct a deep visual clustering framework deep PAC (DPAC). Extensive experiments demonstrate that PAC has superior clustering robustness and performance and DPAC remarkably outperforms the state-of-the-art deep clustering methods.
We present a method to create diffusion-based Video models from pretrained Text-to-Image (T2I) models, overcoming limitations of existing methods. We propose a unique architecture, incorporating a mapping network and frame-wise tokens, tailored for video generation while maintaining the diversity and creativity of the original T2I model. Key innovations include novel loss functions for temporal smoothness and a mitigating gradient sampling technique, ensuring realistic and temporally consistent video generation. Our method, built on the frozen StableDiffusion model, simplifies training processes and allows for seamless integration with off-the-shelf models like ControlNet and DreamBooth. We demonstrate superior performance through extensive experiments and comparisons.
Large foundation models have recently emerged as a prominent focus of interest, attaining superior performance in widespread scenarios. Due to the scarcity of 3D data, many efforts have been made to adapt pre-trained transformers from vision to 3D domains. However, such 2D-to-3D approaches are still limited, due to the potential loss of spatial geometries and high computation cost. More importantly, their frameworks are mainly designed for 2D models, lacking a general any-to-3D paradigm. In this paper, we introduce Any2Point, a parameter-efficient method to empower any-modality large models (vision, language, audio) for 3D understanding. Given a frozen transformer from any source modality, we propose a 3D-to-any (1D or 2D) virtual projection strategy that correlates the input 3D points to the original 1D or 2D positions within the source modality. This mechanism enables us to assign each 3D token with a positional encoding paired with the pre-trained model, which avoids 3D geometry loss caused by the true projection and better motivates the transformer for 3D learning with 1D/2D positional priors. Then, within each transformer block, we insert an any-to-3D guided adapter module for parameter-efficient fine-tuning. The adapter incorporates prior spatial knowledge from the source modality to guide the local feature aggregation of 3D tokens, compelling the semantic adaption of any-modality transformers. We conduct extensive experiments to showcase the effectiveness and efficiency of our method.