CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Recent advances in large video-language models have displayed promising outcomes in video comprehension. Current approaches straightforwardly convert video into language tokens and employ large language models for multi-modal tasks. However this method often leads to the generation of irrelevant content commonly known as "hallucination" as the length of the text increases and the impact of the video diminishes. To address this problem we propose Vista-LLaMA a novel framework that maintains the consistent distance between all visual tokens and any language tokens irrespective of the generated text length. Vista-LLaMA omits relative position encoding when determining attention weights between visual and text tokens retaining the position encoding for text and text tokens. This amplifies the effect of visual tokens on text generation especially when the relative distance is longer between visual and text tokens. The proposed attention mechanism significantly reduces the chance of producing irrelevant text related to the video content. Furthermore we present a sequential visual projector that projects the current video frame into tokens of language space with the assistance of the previous frame. This approach not only captures the temporal relationship within the video but also allows less visual tokens to encompass the entire video. Our approach significantly outperforms various previous methods (e.g.
This approach not only captures the temporal relationship within the video but also allows less visual tokens to encompass the entire video. Our approach significantly outperforms various previous methods (e.g. Video-ChatGPT MovieChat) on four challenging open-ended video question answering benchmarks. We reach an accuracy of 60.7 on the zero-shot NExT-QA and 60.5 on the zero-shot MSRVTT-QA setting a new state-of-the-art performance.
The task of face reenactment is to transfer the head motion and facial expressions from a driving video to the appearance of a source image which may be of a different person (cross-reenactment). Most existing methods are CNN-based and estimate optical flow from the source image to the current driving frame which is then inpainted and refined to produce the output animation. We propose a transformer-based encoder for computing a set-latent representation of the source image(s). We then predict the output color of a query pixel using a transformer-based decoder which is conditioned with keypoints and a facial expression vector extracted from the driving frame. Latent representations of the source person are learned in a self-supervised manner that factorize their appearance head pose and facial expressions. Thus they are perfectly suited for cross-reenactment. In contrast to most related work our method naturally extends to multiple source images and can thus adapt to person-specific facial dynamics. We also propose data augmentation and regularization schemes that are necessary to prevent overfitting and support generalizability of the learned representations. We evaluated our approach in a randomized user study. The results indicate superior performance compared to the state-of-the-art in terms of motion transfer quality and temporal consistency.
Multi-task learning for dense prediction has emerged as a pivotal area in computer vision enabling simultaneous processing of diverse yet interrelated pixel-wise prediction tasks. However the substantial computational demands of state-of-the-art (SoTA) models often limit their widespread deployment. This paper addresses this challenge by introducing network binarization to compress resource-intensive multi-task dense predictors. Specifically our goal is to significantly accelerate multi-task dense prediction models via Binary Neural Networks (BNNs) while maintaining and even improving model performance at the same time. To reach this goal we propose a Binary Multi-task Dense Predictor Bi-MTDP and several variants of \bimtdp in which a multi-task dense predictor is constructed via specified binarized modules. Our systematical analysis of this predictor reveals that performance drop from binarization is primarily caused by severe information degradation. To address this issue we introduce a deep information bottleneck layer that enforces representations for downstream tasks satisfying Gaussian distribution in forward propagation. Moreover we introduce a knowledge distillation mechanism to correct the direction of information flow in backward propagation.
To address this issue we introduce a deep information bottleneck layer that enforces representations for downstream tasks satisfying Gaussian distribution in forward propagation. Moreover we introduce a knowledge distillation mechanism to correct the direction of information flow in backward propagation. Intriguingly one variant of Bi-MTDP outperforms full-precision (FP) multi-task dense prediction SoTAs ARTC (CNN-based) and InvPT (ViT-based). This result indicates that Bi-MTDP is not merely a naive trade-off between performance and efficiency but is rather a benefit of the redundant information flow thanks to the multi-task architecture.
In many practical applications 3D point cloud analysis requires rotation invariance. In this paper we present a learnable descriptor invariant under 3D rotations and reflections i.e. the O(3) actions utilizing the recently introduced steerable 3D spherical neurons and vector neurons. Specifically we propose an embedding of the 3D spherical neurons into 4D vector neurons which leverages end-to-end training of the model. In our approach we perform TetraTransform---an equivariant embedding of the 3D input into 4D constructed from the steerable neurons---and extract deeper O(3)-equivariant features using vector neurons. This integration of the TetraTransform into the VN-DGCNN framework termed TetraSphere negligibly increases the number of parameters by less than 0.0002%. TetraSphere sets a new state-of-the-art performance classifying randomly rotated real-world object scans of the challenging subsets of ScanObjectNN. Additionally TetraSphere outperforms all equivariant methods on randomly rotated synthetic data: classifying objects from ModelNet40 and segmenting parts of the ShapeNet shapes.
Additionally TetraSphere outperforms all equivariant methods on randomly rotated synthetic data: classifying objects from ModelNet40 and segmenting parts of the ShapeNet shapes. Thus our results reveal the practical value of steerable 3D spherical neurons for learning in 3D Euclidean space. The code is available at https://github.com/pavlo-melnyk/tetrasphere.
We present ZeroRF a novel per-scene optimization method addressing the challenge of sparse view 360deg reconstruction in neural field representations. Current breakthroughs like Neural Radiance Fields (NeRF) have demonstrated high-fidelity image synthesis but struggle with sparse input views. Existing methods such as Generalizable NeRFs and per-scene optimization approaches face limitations in data dependency computational cost and generalization across diverse scenarios. To overcome these challenges we propose ZeroRF whose key idea is to integrate a tailored Deep Image Prior into a factorized NeRF representation. Unlike traditional methods ZeroRF parametrizes feature grids with a neural network generator enabling efficient sparse view 360deg reconstruction without any pretraining or additional regularization. Extensive experiments showcase ZeroRF's versatility and superiority in terms of both quality and speed achieving state-of-the-art results on benchmark datasets. ZeroRF's significance extends to applications in 3D content generation and editing. Project page: https://sarahweiii.github.io/zerorf/
The value of roadside perception which could extend the boundaries of autonomous driving and traffic management has gradually become more prominent and acknowledged in recent years. However existing roadside perception approaches only focus on the single-infrastructure sensor system which cannot realize a comprehensive understanding of a traffic area because of the limited sensing range and blind spots. Orienting high-quality roadside perception we need Roadside Cooperative Perception (RCooper) to achieve practical area-coverage roadside perception for restricted traffic areas. Rcooper has its own domain-specific challenges but further exploration is hindered due to the lack of datasets. We hence release the first real-world large-scale RCooper dataset to bloom the research on practical roadside cooperative perception including detection and tracking. The manually annotated dataset comprises 50k images and 30k point clouds including two representative traffic scenes (i.e. intersection and corridor). The constructed benchmarks prove the effectiveness of roadside cooperation perception and demonstrate the direction of further research. Codes and dataset can be accessed at: https://github.com/AIR-THU/DAIR-RCooper.
This work proposes a novel representation of injective deformations of 3D space which overcomes existing limitations of injective methods namely inaccuracy lack of robustness and incompatibility with general learning and optimization frameworks. Our core idea is to reduce the problem to a "deep" composition of multiple 2D mesh-based piecewise-linear maps. Namely we build differentiable layers that produce mesh deformations through Tutte's embedding (guaranteed to be injective in 2D) and compose these layers over different planes to create complex 3D injective deformations of the 3D volume. We show our method provides the ability to ef?ciently and accurately optimize and learn complex deformations outperforming other injective approaches. As a main application we produce complex and artifact-free NeRF and SDF deformations.
Synthesizing natural human motions that enable a 3D human avatar to walk and reach for arbitrary goals in 3D space remains an unsolved problem with many applications. Existing methods (data-driven or using reinforcement learning) are limited in terms of generalization and motion naturalness. A primary obstacle is the scarcity of training data that combines locomotion with goal reaching. To address this we introduce WANDR a data-driven model that takes an avatar's initial pose and a goal's 3D position and generates natural human motions that place the end effector (wrist) on the goal location. To solve this we introduce novel intention features that drive rich goal-oriented movement. Intention guides the agent to the goal and interactively adapts the generation to novel situations without needing to define sub-goals or the entire motion path. Crucially intention allows training on datasets that have goal-oriented motions as well as those that do not. WANDR is a conditional Variational Auto-Encoder (c-VAE) which we train using the AMASS and CIRCLE datasets. We evaluate our method extensively and demonstrate its ability to generate natural and long-term motions that reach 3D goals and generalize to unseen goal locations.
WANDR is a conditional Variational Auto-Encoder (c-VAE) which we train using the AMASS and CIRCLE datasets. We evaluate our method extensively and demonstrate its ability to generate natural and long-term motions that reach 3D goals and generalize to unseen goal locations. Our models and code are available for research purposes at wandr.is.tue.mpg.de
Structural model pruning is a prominent approach used for reducing the computational cost of Convolutional Neural Networks (CNNs) before their deployment on resource-constrained devices. Yet the majority of proposed ideas require a pretrained model before pruning which is costly to secure. In this paper we propose a novel structural pruning approach to jointly learn the weights and structurally prune architectures of CNN models. The core element of our method is a Reinforcement Learning (RL) agent whose actions determine the pruning ratios of the CNN model's layers and the resulting model's accuracy serves as its reward. We conduct the joint training and pruning by iteratively training the model's weights and the agent's policy and we regularize the model's weights to align with the selected structure by the agent. The evolving model's weights result in a dynamic reward function for the agent which prevents using prominent episodic RL methods with stationary environment assumption for our purpose. We address this challenge by designing a mechanism to model the complex changing dynamics of the reward function and provide a representation of it to the RL agent. To do so we take a learnable embedding for each training epoch and employ a recurrent model to calculate a representation of the changing environment.
We address this challenge by designing a mechanism to model the complex changing dynamics of the reward function and provide a representation of it to the RL agent. To do so we take a learnable embedding for each training epoch and employ a recurrent model to calculate a representation of the changing environment. We train the recurrent model and embeddings using a decoder model to reconstruct observed rewards. Such a design empowers our agent to effectively leverage episodic observations along with the environment representations to learn a proper policy to determine performant sub-networks of the CNN model. Our extensive experiments on CIFAR-10 and ImageNet using ResNets and MobileNets demonstrate the effectiveness of our method.
In noisy label learning estimating noisy class posteriors plays a fundamental role for developing consistent classifiers as it forms the basis for estimating clean class posteriors and the transition matrix. Existing methods typically learn noisy class posteriors by training a classification model with noisy labels. However when labels are incorrect these models may be misled to overemphasize the feature parts that do not reflect the instance characteristics resulting in significant errors in estimating noisy class posteriors. To address this issue this paper proposes to augment the supervised information with part-level labels encouraging the model to focus on and integrate richer information from various parts. Specifically our method first partitions features into distinct parts by cropping instances yielding part-level labels associated with these various parts. Subsequently we introduce a novel single-to-multiple transition matrix to model the relationship between the noisy and part-level labels which incorporates part-level labels into a classifier-consistent framework. Utilizing this framework with part-level labels we can learn the noisy class posteriors more precisely by guiding the model to integrate information from various parts ultimately improving the classification performance. Our method is theoretically sound while experiments show that it is empirically effective in synthetic and real-world noisy benchmarks.
Vision-Language Models (VLMs) such as CLIP are trained on large amounts of image-text pairs resulting in remarkable generalization across several data distributions. However in several cases their expensive training and data collection/curation costs do not justify the end application. This motivates a vendor-client paradigm where a vendor trains a large-scale VLM and grants only input-output access to clients on a pay-per-query basis in a black-box setting. The client aims to minimize inference cost by distilling the VLM to a student model using the limited available task-specific data and further deploying this student model in the downstream application. While naive distillation largely improves the In-Domain (ID) accuracy of the student it fails to transfer the superior out-of-distribution (OOD) generalization of the VLM teacher using the limited available labeled images. To mitigate this we propose Vision-Language to Vision - Align Distill Predict (VL2V-ADiP) which first aligns the vision and language modalities of the teacher model with the vision modality of a pre-trained student model and further distills the aligned VLM representations to the student.
To mitigate this we propose Vision-Language to Vision - Align Distill Predict (VL2V-ADiP) which first aligns the vision and language modalities of the teacher model with the vision modality of a pre-trained student model and further distills the aligned VLM representations to the student. This maximally retains the pre-trained features of the student while also incorporating the rich representations of the VLM image encoder and the superior generalization of the text embeddings. The proposed approach achieves state-of-the-art results on the standard Domain Generalization benchmarks in a black-box teacher setting as well as a white-box setting where the weights of the VLM are accessible.
Diffusion generative modeling has become a promising approach for learning robotic manipulation tasks from stochastic human demonstrations. In this paper we present Diffusion-EDFs a novel SE(3)-equivariant diffusion-based approach for visual robotic manipulation tasks. We show that our proposed method achieves remarkable data efficiency requiring only 5 to 10 human demonstrations for effective end-to-end training in less than an hour. Furthermore our benchmark experiments demonstrate that our approach has superior generalizability and robustness compared to state-of-the-art methods. Lastly we validate our methods with real hardware experiments.
Pre-trained vision-language models have shown impressive success on various computer vision tasks with their zero-shot generalizability. Recently prompt learning approaches have been explored to efficiently and effectively adapt the vision-language models to a variety of downstream tasks. However most existing prompt learning methods suffer from task overfitting since the general knowledge of the pre-trained vision language models is forgotten while the prompts are finetuned on a small data set from a specific target task. To address this issue we propose a Prompt Meta-Regularization (ProMetaR) to improve the generalizability of prompt learning for vision-language models. Specifically ProMetaR meta-learns both the regularizer and the soft prompts to harness the task-specific knowledge from the downstream tasks and task-agnostic general knowledge from the vision-language models. Further ProMetaR augments the task to generate multiple virtual tasks to alleviate the meta-overfitting. In addition we provide the analysis to comprehend how ProMetaR improves the generalizability of prompt tuning in the perspective of the gradient alignment. Our extensive experiments demonstrate that our ProMetaR improves the generalizability of conventional prompt learning methods under base-to-base/base-to-new and domain generalization settings.
In addition we provide the analysis to comprehend how ProMetaR improves the generalizability of prompt tuning in the perspective of the gradient alignment. Our extensive experiments demonstrate that our ProMetaR improves the generalizability of conventional prompt learning methods under base-to-base/base-to-new and domain generalization settings. The code of ProMetaR is available at https://github.com/mlvlab/ProMetaR.
Vision-Language Models (VLMs) such as CLIP exhibit strong image-text comprehension abilities facilitating advances in several downstream tasks such as zero-shot image classification image-text retrieval and text-to-image generation. However the compositional reasoning abilities of existing VLMs remains subpar. The root of this limitation lies in the inadequate alignment between the images and captions in the pretraining datasets. Additionally the current contrastive learning objective fails to focus on fine-grained grounding components like relations actions and attributes resulting in "bag-of-words" representations. We introduce a simple and effective method to improve compositional reasoning in VLMs. Our method better leverages available datasets by refining and expanding the standard image-text contrastive learning framework. Our approach does not require specific annotations and does not incur extra parameters. When integrated with CLIP our technique yields notable improvement over state-of-the-art baselines across five vision-language compositional benchmarks.
The rebroadcasting of screen-recaptured document images introduces a significant risk to the confidential documents processed in government departments and commercial companies. However detecting recaptured document images subjected to distortions from online social networks (OSNs) is challenging since the common forensics cues such as moir ?e pattern are weakened during transmission. In this work we first devise a pixel-level distortion model of the screen-recaptured document image to identify the robust features of color artifacts. Then we extract a chromaticity map from the recaptured image to highlight the presence of color artifacts even under low-quality samples. Based on the prior understanding we design a chromaticity map adapter (CMA) to efficiently extract the chromaticity map and feed it into the transformer backbone as multi-modal prompt tokens. To evaluate the performance of the proposed method we collect a recaptured office document image dataset with over 10K diverse samples. Experimental results demonstrate that the proposed CMA method outperforms a SOTA approach (with RGB modality only) reducing the average EER from 26.82% to 16.78%.
Experimental results demonstrate that the proposed CMA method outperforms a SOTA approach (with RGB modality only) reducing the average EER from 26.82% to 16.78%. Robustness evaluation shows that our method achieves 0.8688 and 0.7554 AUCs under samples with JPEG compression (QF=70) and resolution as low as 534x503 pixels.
While large language models (LLMs) excel in a simulated world of texts they struggle to interact with the more realistic world without perceptions of other modalities such as visual or audio signals. Although vision-language models (VLMs) integrate LLM modules (1) aligned with static image features and (2) may possess prior knowledge of world dynamics (as demonstrated in the text world) they have not been trained in an embodied visual world and thus cannot align with its dynamics. On the other hand training an embodied agent in a noisy visual world without expert guidance is often challenging and inefficient. In this paper we train a VLM agent living in a visual world using an LLM agent excelling in a parallel text world. Specifically we distill LLM's reflection outcomes (improved actions by analyzing mistakes) in a text world's tasks to finetune the VLM on the same tasks of the visual world resulting in an Embodied Multi-Modal Agent (EMMA) quickly adapting to the visual world dynamics. Such cross-modality imitation learning between the two parallel worlds is achieved by a novel DAgger-DPO algorithm enabling EMMA to generalize to a broad scope of new tasks without any further guidance from the LLM expert.
Such cross-modality imitation learning between the two parallel worlds is achieved by a novel DAgger-DPO algorithm enabling EMMA to generalize to a broad scope of new tasks without any further guidance from the LLM expert. Extensive evaluations on the ALFWorld benchmark's diverse tasks highlight EMMA's superior performance to SOTA VLM-based agents e.g. 20%-70% improvement in the success rate.
The booming use of text-to-image generative models has raised concerns about their high risk of producing copyright-infringing content. While probabilistic copyright protection methods provide a probabilistic guarantee against such infringement in this paper we introduce Virtually Assured Amplification Attack (VA3) a novel online attack framework that exposes the vulnerabilities of these protection mechanisms. The proposed framework significantly amplifies the probability of generating infringing content on the sustained interactions with generative models and a non-trivial lower-bound on the success probability of each engagement. Our theoretical and experimental results demonstrate the effectiveness of our approach under various scenarios.These findings highlight the potential risk of implementing probabilistic copyright protection in practical applications of text-to-image generative models. Code is available at https://github.com/South7X/VA3.