Vision-language models (VLMs) often fail on abstract visual reasoning benchmarks such as Bongard problems, raising the question of whether the main bottleneck lies in reasoning or representation.We study this on Bongard-LOGO, a synthetic benchmark of abstract concept learning with ground-truth generative programs, by comparing end-to-end VLMs on raw images with large language models (LLMs) given symbolic inputs derived from those images.Using symbolic inputs as a diagnostic probe rather than a practical multimodal architecture, our Componential-Grammatical (C-G) paradigm reformulates Bongard-LOGO as a symbolic reasoning task based on LOGOstyle action programs or structured descriptions.LLMs achieve large and consistent gains, reaching mid-90s accuracy on Free-form problems, while a strong visual baseline remains near chance under matched task definitions.Ablations on input format, explicit concept prompts, and minimal visual grounding show that these factors matter much less than the shift from pixels to symbolic structure.These results identify representation as a key bottleneck in abstract visual reasoning and show how symbolic input can serve as a controlled diagnostic upper bound.

A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning have driven rapid progress in recent years with state-of-the-art systems now reaching human accuracy on some of these benchmarks. Yet, there remains a major gap between humans and AI systems in terms of the sample efficiency with which they learn new visual reasoning tasks. Humans' remarkable efficiency at learning has been at least partially attributed to their ability to harness compositionality -- allowing them to efficiently take advantage of previously gained knowledge when learning new tasks. Here, we introduce a novel visual reasoning benchmark, Compositional Visual Relations (CVR), to drive progress towards the development of more data-efficient learning algorithms. We take inspiration from fluidic intelligence and non-verbal reasoning tests and describe a novel method for creating compositions of abstract rules and generating image datasets corresponding to these rules at scale. Our proposed benchmark includes measures of sample efficiency, generalization, compositionality, and transfer across task rules. We systematically evaluate modern neural architectures and find that convolutional architectures surpass transformer-based architectures across all performance measures in most data regimes. However, all computational models are much less data efficient than humans, even after learning informative visual representations using self-supervision. Overall, we hope our challenge will spur interest in developing neural architectures that can learn to harness compositionality for more efficient learning.

Visual understanding requires comprehending complex visual relations between objects within a scene. Here, we seek to characterize the computational demands for abstract visual reasoning. We do this by systematically assessing the ability of modern deep convolutional neural networks (CNNs) to learn to solve the synthetic visual reasoning test (SVRT) challenge, a collection of 23 visual reasoning problems. Our analysis reveals a novel taxonomy of visual reasoning tasks, which can be primarily explained by both the type of relations (same-different versus spatial-relation judgments) and the number of relations used to compose the underlying rules. Prior cognitive neuroscience work suggests that attention plays a key role in humans' visual reasoning ability. To test this hypothesis, we extended the CNNs with spatial and feature-based attention mechanisms. In a second series of experiments, we evaluated the ability of these attention networks to learn to solve the SVRT challenge and found the resulting architectures to be much more efficient at solving the hardest of these visual reasoning tasks. Most important, the corresponding improvements on individual tasks partially explained our novel taxonomy. Overall, this work provides a granular computational account of visual reasoning and yields testable neuroscience predictions regarding the differential need for feature-based versus spatial attention depending on the type of visual reasoning problem.

In this paper, a novel method for lossless compression of video is proposed. Almost all the prediction based methods reported in literature are of two pass. In the first pass, motion compensated frame is obtained and in the second, some sophisticated method is used to predict the pixels of the current frame. The proposed method is an efficient replacement for the first method that predicts current pixel using an estimate of deviation from the pixel at same temporal location in the previous frame. In this scheme, causal pixels are divided into bins based on the distance between the current and causal pixels. The novelty of the work is in finding out the fixed coefficients of the bins for a particular type of video sequence. The overall performance of the proposed method is same with much lower computational complexity.

All the methods available in literature for lossless coding comprises of two steps, one is the prediction scheme that results into prediction error and the second one consists of entropy coding of prediction error samples. In this work we introduce a scheme which adds a step prior to entropy coding technique due to which, the achieved gain in terms of compression is high. The additional step works on the predicted error frames obtained after applying any prediction scheme. The error frames so obtained are remapped into another domain of 8 bits per error sample. While remapping, two files are generated which needs to be compressed, One is the remapped error file itself while the second file consists of overhead information needed to get back the original error samples. The second file consists of binary numbers. The remapped file is compressed by CALIC encoder while the other is kept as it is, causing an addition of one bit per pixel overhead to the compressed file. Results show that instead of going in a traditional manner if we follow the proposed path, there will be a gain of about 0.21 bpp on an average. Additionally the comparison has also been made against other prominent codecs like JPEG and CALIC.

In this work, we propose a method of lossless video coding which not has only the decoder simple but encoder is also simple, unlike other reported methods which has computationally complex encoder. The computation is mainly due to not using motion compensation method, which is computationally complex process. The coefficient of the predictors are obtained based on an averaging process and then the thus obtained set of switched predictors is used for prediction. The parameters have been obtained after undergoing a statistical process of averaging so that proper relationship can be established between the predicted pixel and their context.

This paper presents a novel loss less compression method for video. In this work, we propose a novel method for finding motion compensated frame. This is computationally much efficient than other method reported in literature. After finding the motion compensated frame, we propose a new method for efficiently applying LS based predictor on the frames. The predictor structure uses pixels in the current frame and also in the motion compensated frame. Overall performance of the proposed method is significantly better than many competitive methods at significantly reduced computational complexity.