CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
We evaluate the performance of Zero-TPrune on vision tasks by applying it to various vision Transformer backbones and testing them on ImageNet. Without any fine-tuning Zero-TPrune reduces the FLOPs cost of DeiT-S by 34.7% and improves its throughput by 45.3% with only 0.4% accuracy loss. Compared with state-of-the-art pruning methods that require fine-tuning Zero-TPrune not only eliminates the need for fine-tuning after pruning but also does so with only 0.1% accuracy loss. Compared with state-of-the-art fine-tuning-free pruning methods Zero-TPrune reduces accuracy loss by up to 49% with the same or higher throughput.
Continual learning (CL) aims to empower models to learn new tasks without forgetting previously acquired knowledge. Most prior works concentrate on the techniques of architectures replay data regularization etc. However the category name of each class is largely neglected. Existing methods commonly utilize the one-hot labels and randomly initialize the classifier head. We argue that the scarce semantic information conveyed by the one-hot labels hampers the effective knowledge transfer across tasks. In this paper we revisit the role of the classifier head within the CL paradigm and replace the classifier with semantic knowledge from pretrained language models (PLMs). Specifically we use PLMs to generate semantic targets for each class which are frozen and serve as supervision signals during training. Such targets fully consider the semantic correlation between all classes across tasks. Empirical studies show that our approach mitigates forgetting by alleviating representation drifting and facilitating knowledge transfer across tasks. The proposed method is simple to implement and can seamlessly be plugged into existing methods with negligible adjustments. Extensive experiments based on eleven mainstream baselines demonstrate the effectiveness and generalizability of our approach to various protocols.
The proposed method is simple to implement and can seamlessly be plugged into existing methods with negligible adjustments. Extensive experiments based on eleven mainstream baselines demonstrate the effectiveness and generalizability of our approach to various protocols. For example under the class-incremental learning setting on ImageNet-100 our method significantly improves the Top-1 accuracy by 3.2% to 6.1% while reducing the forgetting rate by 2.6% to 13.1%.
The rapid expansion of large-scale text-to-image diffusion models has raised growing concerns regarding their potential misuse in creating harmful or misleading content. In this paper we introduce MACE a finetuning framework for the task of mass concept erasure. This task aims to prevent models from generating images that embody unwanted concepts when prompted. Existing concept erasure methods are typically restricted to handling fewer than five concepts simultaneously and struggle to find a balance between erasing concept synonyms (generality) and maintaining unrelated concepts (specificity). In contrast MACE differs by successfully scaling the erasure scope up to 100 concepts and by achieving an effective balance between generality and specificity. This is achieved by leveraging closed-form cross-attention refinement along with LoRA finetuning collectively eliminating the information of undesirable concepts. Furthermore MACE integrates multiple LoRAs without mutual interference. We conduct extensive evaluations of MACE against prior methods across four different tasks: object erasure celebrity erasure explicit content erasure and artistic style erasure. Our results reveal that MACE surpasses prior methods in all evaluated tasks. Code is available at https://github.com/Shilin-LU/MACE.
We present Dive Into the Boundaries (DIBS) a novel pretraining framework for dense video captioning (DVC) that elaborates on improving the quality of the generated event captions and their associated pseudo event boundaries from unlabeled videos. By leveraging the capabilities of diverse large language models (LLMs) we generate rich DVC-oriented caption candidates and optimize the corresponding pseudo boundaries under several meticulously designed objectives considering diversity event-centricity temporal ordering and coherence. Moreover we further introduce a novel online boundary refinement strategy that iteratively improves the quality of pseudo boundaries during training. Comprehensive experiments have been conducted to examine the effectiveness of the proposed technique components. By leveraging a substantial amount of unlabeled video data such as HowTo100M we achieve a remarkable advancement on standard DVC datasets like YouCook2 and ActivityNet. We outperform the previous state-of-the-art Vid2Seq across a majority of metrics achieving this with just 0.4% of the unlabeled video data used for pre-training by Vid2Seq.
Recently some large kernel convnets strike back with appealing performance and efficiency. However given the square complexity of convolution scaling up kernels can bring about an enormous amount of parameters and the proliferated parameters can induce severe optimization problem. Due to these issues current CNNs compromise to scale up to 51x51 in the form of stripe convolution (i.e. 51x5+5x51) and start to saturate as the kernel size continues growing. In this paper we delve into addressing these vital issues and explore whether we can continue scaling up kernels for more performance gains. Inspired by human vision we propose a human-like peripheral convolution that efficiently reduces over 90% parameter count of dense grid convolution through parameter sharing and manage to scale up kernel size to extremely large. Our peripheral convolution behaves highly similar to human reducing the complexity of convolution from O(K^2) to O(logK) without backfiring performance. Built on this we propose Parameter-efficient Large Kernel Network (PeLK). Our PeLK outperforms modern vision Transformers and ConvNet architectures like Swin ConvNeXt RepLKNet and SLaK on various vision tasks including ImageNet classification semantic segmentation on ADE20K and object detection on MS COCO.
Our PeLK outperforms modern vision Transformers and ConvNet architectures like Swin ConvNeXt RepLKNet and SLaK on various vision tasks including ImageNet classification semantic segmentation on ADE20K and object detection on MS COCO. For the first time we successfully scale up the kernel size of CNNs to an unprecedented 101x101 and demonstrate consistent improvements.
Expressive human pose and shape estimation (a.k.a. 3D whole-body mesh recovery) involves the human body hand and expression estimation. Most existing methods have tackled this task in a two-stage manner first detecting the human body part with an off-the-shelf detection model and then inferring the different human body parts individually. Despite the impressive results achieved these methods suffer from 1) loss of valuable contextual information via cropping 2) introducing distractions and 3) lacking inter-association among different persons and body parts inevitably causing performance degradation especially for crowded scenes. To address these issues we introduce a novel all-in-one-stage framework AiOS for multiple expressive human pose and shape recovery without an additional human detection step. Specifically our method is built upon DETR which treats multi-person whole-body mesh recovery task as a progressive set prediction problem with various sequential detection. We devise the decoder tokens and extend them to our task. Specifically we first employ a human token to probe a human location in the image and encode global features for each instance which provides a coarse location for the later transformer block.
We devise the decoder tokens and extend them to our task. Specifically we first employ a human token to probe a human location in the image and encode global features for each instance which provides a coarse location for the later transformer block. Then we introduce a joint-related token to probe the human joint in the image and encoder a fine-grained local feature which collaborates with the global feature to regress the whole-body mesh. This straightforward but effective model outperforms previous state-of-the-art methods by a 9 reduction in NMVE on AGORA a 30 reduction in PVE on EHF a 10 reduction in PVE on ARCTIC and a 3 reduction in PVE on EgoBody.
Reasoning from visual dynamics scenes has many real world applications. However existing video reasoning benchmarks are still inadequate since they were mainly designed for factual or situated reasoning and rarely involve broader knowledge in the real world. Our work aims to delve deeper into reasoning evaluations specifically within dynamic open-world and structured context knowledge. We propose a new benchmark (SOK-Bench) consisting of 44K questions and 10K situations with instance-level annotations depicted in the videos. The reasoning process is required to understand and apply situated knowledge and general knowledge for problem-solving. To create such a dataset we propose an automatic and scalable generation method to generate question-answer pairs knowledge graphs and rationales by instructing the combinations of LLMs and MLLMs. Concretely we first extract observable situated entities relations and processes from videos for situated knowledge and then extend to open-world knowledge beyond the visible content. The task generation is facilitated through multiple dialogues as iterations and subsequently corrected and refined by our designed self-promptings and demonstrations. With a corpus of both explicit situated facts and implicit commonsense we generate associated question-answer pairs and reasoning processes finally followed by manual reviews for quality assurance.
The task generation is facilitated through multiple dialogues as iterations and subsequently corrected and refined by our designed self-promptings and demonstrations. With a corpus of both explicit situated facts and implicit commonsense we generate associated question-answer pairs and reasoning processes finally followed by manual reviews for quality assurance. We evaluated recent mainstream large vision language models on the benchmark and found several insightful conclusions. For more information please refer to our benchmark at www.bobbywu.com/SOKBench.
Deep learning models particularly those based on transformers often employ numerous stacked structures which possess identical architectures and perform similar functions. While effective this stacking paradigm leads to a substantial increase in the number of parameters pos- ing challenges for practical applications. In today's land- scape of increasingly large models stacking depth can even reach dozens further exacerbating this issue. To miti- gate this problem we introduce LORS (LOw-rank Residual Structure). LORS allows stacked modules to share the majority of parameters requiring a much smaller num- ber of unique ones per module to match or even surpass the performance of using entirely distinct ones thereby significantly reducing parameter usage. We validate our method by applying it to the stacked decoders of a query- based object detector and conduct extensive experiments on the widely used MS COCO dataset. Experimental re- sults demonstrate the effectiveness of our method as even with a 70% reduction in the parameters of the decoder our method still enables the model to achieve comparable or even better performance than its original.
In recent years there has been a significant shift in the field of digital avatar research towards modeling animating and reconstructing clothed human representations as a key step towards creating realistic avatars. However current 3D cloth generation methods are garment specific or trained completely on synthetic data hence lacking fine details and realism. In this work we make a step towards automatic realistic garment design and propose Design2Cloth a high fidelity 3D generative model trained on a real world dataset from more than 2000 subject scans. To provide vital contribution to the fashion industry we developed a user-friendly adversarial model capable of generating diverse and detailed clothes simply by drawing a 2D cloth mask. Under a series of both qualitative and quantitative experiments we showcase that Design2Cloth outperforms current state-of-the-art cloth generative models by a large margin. In addition to the generative properties of our network we showcase that the proposed method can be used to achieve high quality reconstructions from single in-the-wild images and 3D scans. Dataset code and pre-trained model will become publicly available.
Scene text recognition (STR) in the wild frequently encounters challenges when coping with domain variations font diversity shape deformations etc. A straightforward solution is performing model fine-tuning tailored to a specific scenario but it is computationally intensive and requires multiple model copies for various scenarios. Recent studies indicate that large language models (LLMs) can learn from a few demonstration examples in a training-free manner termed "In-Context Learning" (ICL). Nevertheless applying LLMs as a text recognizer is unacceptably resource-consuming. Moreover our pilot experiments on LLMs show that ICL fails in STR mainly attributed to the insufficient incorporation of contextual information from diverse samples in the training stage. To this end we introduce E2STR a STR model trained with context-rich scene text sequences where the sequences are generated via our proposed in-context training strategy. E2STR demonstrates that a regular-sized model is sufficient to achieve effective ICL capabilities in STR. Extensive experiments show that E2STR exhibits remarkable training-free adaptation in various scenarios and outperforms even the fine-tuned state-of-the-art approaches on public benchmarks. The code is released at https://github.com/bytedance/E2STR.
Our brain can effortlessly recognize objects even when partially hidden from view. Seeing the visible of the hidden is called amodal completion; however this task remains a challenge for generative AI despite rapid progress. We propose to sidestep many of the difficulties of existing approaches which typically involve a two-step process of predicting amodal masks and then generating pixels. Our method involves thinking outside the box literally! We go outside the object bounding box to use its context to guide a pre-trained diffusion inpainting model and then progressively grow the occluded object and trim the extra background. We overcome two technical challenges: 1) how to be free of unwanted co-occurrence bias which tends to regenerate similar occluders and 2) how to judge if an amodal completion has succeeded. Our amodal completion method exhibits improved photorealistic completion results compared to existing approaches in numerous successful completion cases. And the best part? It doesn't require any special training or fine-tuning of models. Project page and code: https://k8xu.github.io/amodal/
Diffusion models have demonstrated unprecedented capabilities in image generation. Yet they incorporate and amplify the data bias (e.g. gender age) from the original training set limiting the diversity of generated images. In this paper we propose a diversity-oriented fine-tuning method using reinforcement learning (RL) for diffusion models under the guidance of an image-set-based reward function. Specifically the proposed reward function denoted as Diversity Reward utilizes a set of generated images to evaluate the coverage of the current generative distribution w.r.t. the reference distribution represented by a set of unbiased images. Built on top of the probabilistic method of distribution discrepancy estimation Diversity Reward can measure the relative distribution gap with a small set of images efficiently. We further formulate the diffusion process as a multi-step decision-making problem (MDP) and apply policy gradient methods to fine-tune diffusion models by maximizing the Diversity Reward. The proposed rewards are validated on a post-sampling selection task where a subset of the most diverse images are selected based on Diversity Reward values. We also show the effectiveness of our RL fine-tuning framework on enhancing the diversity of image generation with different types of diffusion models including class-conditional models and text-conditional models e.g. StableDiffusion.
We present Diff3F as a simple robust and class-agnostic feature descriptor that can be computed for untextured input shapes (meshes or point clouds). Our method distills diffusion features from image foundational models onto input shapes. Specifically we use the input shapes to produce depth and normal maps as guidance for conditional image synthesis. In the process we produce (diffusion) features in 2D that we subsequently lift and aggregate on the original surface. Our key observation is that even if the conditional image generations obtained from multi-view rendering of the input shapes are inconsistent the associated image features are robust and hence can be directly aggregated across views. This produces semantic features on the input shapes without requiring additional data or training. We perform extensive experiments on multiple benchmarks (SHREC'19 SHREC'20 FAUST and TOSCA) and demonstrate that our features being semantic instead of geometric produce reliable correspondence across both isometric and non-isometrically related shape families. Code is available via the project webpage at https://diff3f.github.io/
Microscopic traffic simulation plays a crucial role in transportation engineering by providing insights into individual vehicle behavior and overall traffic flow. However creating a realistic simulator that accurately replicates human driving behaviors in various traffic conditions presents significant challenges. Traditional simulators relying on heuristic models often fail to deliver accurate simulations due to the complexity of real-world traffic environments. Due to the covariate shift issue existing imitation learning-based simulators often fail to generate stable long-term simulations. In this paper we propose a novel approach called learner-aware supervised imitation learning to address the covariate shift problem in multi-agent imitation learning. By leveraging a variational autoencoder simultaneously modeling the expert and learner state distribution our approach augments expert states such that the augmented state is aware of learner state distribution. Our method applied to urban traffic simulation demonstrates significant improvements over existing state-of-the-art baselines in both short-term microscopic and long-term macroscopic realism when evaluated on the real-world dataset pNEUMA.
Federated Learning (FL) facilitates clients to collaborate on training a shared machine learning model without exposing individual private data. Nonetheless FL remains susceptible to utility and privacy attacks notably evasion data poisoning and model inversion attacks compromising the system's efficiency and data privacy. Existing FL defenses are often specialized to a particular single attack lacking generality and a comprehensive defender's perspective. To address these challenges we introduce Federated Cryptography Defense (FCD) a unified single framework aligning with the defender's perspective. FCD employs row-wise transposition cipher based data encryption with a secret key to counter both evasion black-box data poisoning and model inversion attacks. The crux of FCD lies in transferring the entire learning process into an encrypted data space and using a novel distillation loss guided by the Kullback-Leibler (KL) divergence. This measure compares the probability distributions of the local pretrained teacher model's predictions on normal data and the local student model's predictions on the same data in FCD's encrypted form. By working within this encrypted space FCD eliminates the need for decryption at the server resulting in reduced computational complexity.
This measure compares the probability distributions of the local pretrained teacher model's predictions on normal data and the local student model's predictions on the same data in FCD's encrypted form. By working within this encrypted space FCD eliminates the need for decryption at the server resulting in reduced computational complexity. We demonstrate the practical feasibility of FCD and apply it to defend against evasion utility attack on benchmark datasets (GTSRB KBTS CIFAR10 and EMNIST). We further extend FCD for defending against model inversion attack in split FL on the CIFAR100 dataset. Our experiments across the diverse attack and FL settings demonstrate practical feasibility and robustness against utility evasion (impact >30) and privacy attacks (MSE >73) compared to the second best method.
Deep learning-based methods have achieved significant successes on solving the blind super-resolution (BSR) problem. However most of them request supervised pre-training on labelled datasets. This paper proposes an unsupervised kernel estimation model named dynamic kernel prior (DKP) to realize an unsupervised and pre-training-free learning-based algorithm for solving the BSR problem. DKP can adaptively learn dynamic kernel priors to realize real-time kernel estimation and thereby enables superior HR image restoration performances. This is achieved by a Markov chain Monte Carlo sampling process on random kernel distributions. The learned kernel prior is then assigned to optimize a blur kernel estimation network which entails a network-based Langevin dynamic optimization strategy. These two techniques ensure the accuracy of the kernel estimation. DKP can be easily used to replace the kernel estimation models in the existing methods such as Double-DIP and FKP-DIP or be added to the off-the-shelf image restoration model such as diffusion model. In this paper we incorporate our DKP model with DIP and diffusion model referring to DIP-DKP and Diff-DKP for validations.
In this paper we incorporate our DKP model with DIP and diffusion model referring to DIP-DKP and Diff-DKP for validations. Extensive simulations on Gaussian and motion kernel scenarios demonstrate that the proposed DKP model can significantly improve the kernel estimation with comparable runtime and memory usage leading to state-of-the-art BSR results. The code is available at https://github.com/XYLGroup/DKP.
In the evolving landscape of digital media and video production the precise manipulation and reproduction of visual elements like camera movements and character actions are highly desired. Existing SLAM methods face limitations in dynamic scenes and human pose estimation often focuses on 2D projections neglecting 3D statuses. To address these issues we first introduce a reverse filming behavior estimation technique. It optimizes camera trajectories by leveraging NeRF as a differentiable renderer and refining SMPL tracks. We then introduce a cinematic transfer pipeline that is able to transfer various shot types to a new 2D video or a 3D virtual environment. The incorporation of 3D engine workflow enables superior rendering and control abilities which also achieves a higher rating in the user study.