Multiple Object Tracking (MOT) in dynamic and densely populated scenes presents significant challenges due to frequent occlusions, erratic object motion, and identity switches. While recent Transformer-based approaches have successfully leveraged global attention for object detection, most rely on temporal reasoning at the decoder level, leaving encoder-stage modeling underexplored. In this work, we propose an encoder-level temporal reasoning Transformer framework that embeds historical object trajectory information into the encoder stage via a tracklet memory. The encoder module, enhanced by Attention-by-Tracking, enriches visual features with temporal priors, while the decoder leverages Tracking-by-Attention to guide identity association using learned tracklet representations. To further improve temporal consistency and object localization, we introduce a gating-based temporal feature fusion mechanism that adaptively integrates multi-frame features based on cosine similarity. Our method is model-agnostic and can be applied to various Transformer-based MOT frameworks. When integrated into existing models such as TransTrack, MeMOTR, and MOTIP, it yields consistent performance improvements. Extensive experiments on DanceTrack and SportsMOT benchmarks demonstrate that our approach achieves superior tracking performance, including a HOTA score of 76.4 on SportsMOT. These results validate the effectiveness of encoder-level temporal integration and adaptive feature fusion for robust multi-object tracking in real-world scenarios.
Image color enhancement is a key component of ISP pipelines, with 3D LUTs widely adopted for nonlinear color transformations and color refinement. Existing learning-based approaches attempt to learn a global mapping from paired pixel-aligned training samples, but this offers limited control and flexibility for practitioners and restricts adaptation to specific images or scenarios. Moreover, the assumption of pixel-aligned supervision requires costly manual retouching to generate ground-truth references. A practical alternative is to use DSLR images as high-quality references, but the inevitable FoV and PoV variations make pixel-level 3D LUT calibration unreliable. We propose ChameleonTuner1, a novel framework that establishes region-level color correspondences to handle FoV/PoV discrepancies. Unlike prior learning-based 3D LUT methods, the proposed framework defines a dedicated search space for LUT tuning and employs multi-objective search to achieve controllable and interpretable optimization. Extensive experiments across multiple perceptual color metrics demonstrate that our method is effective in subjective scenarios (e.g., DPED) with geometric misalignments, while remaining competitive on standard pixel-aligned benchmarks (e.g., MIT-Adobe FiveK). Our code is available at https://github.com/ZjTan4/ChameleonTuner.git.
Generative artificial intelligence has the potential to improve productivity and transform the production of creative content. However, existing research indicates that image generation models are significantly influenced by biases. This work investigates the inherent biases and language-induced biases present in text-to-image models within the context of occupation-related image generation, complementing established metrics with human preference feedback. We present a comprehensive evaluation of five current text-to-image models: Midjourney v6.1, Stable Diffusion 3 Medium, DALL-E 3, Playground v2.5, and FLUX.1-dev , focusing on gender and ethnicity bias, image quality, and prompt alignment. To facilitate this evaluation, we developed the "Battle-Arena for Fair Image Synthesis" (BAFIS), a platform designed to collect human feedback on bias in generated images. Furthermore, we created a dataset comprising 21,140 synthetic images generated using multilingual prompts, which serves as a basis for our analysis. We further place our results within a broader social context by comparing them to official statistics from the German Federal Employment Agency. Our findings reveal systematic biases in text-to-image models, with established evaluation metrics in partial correlation with subjective user ratings. Thus, our research emphasizes the need for including human preferences to develop fairer and more inclusive text-to-image models.
The advanced multimodal processing of current vision language models (VLMs) has prompted rigorous benchmarking across multicultural settings, revealing a clear inclination toward Western culture. While the bias likely stems from the predominance of Western-centric images in the VLM pretraining data, the resulting long-tail distribution problem is only exacerbated in underrepresented cultural settings, such as Bengali. Our work explores this problem through an aspect-based evaluation of several classes of VLMs on the rich Bengali culture. Our BanglaProtha dataset is a VQA dataset, containing images that encapsulate Bengali cultural elements, questions in native Bengali, and semantically similar multiple-choice answer options. Our experiments provide behavioral insights into VLMs across prompting & fine-tuning strategies, cultural aspects, model size, and augmentation methods. Our work serves as a diagnostic tool for addressing and mitigating inequalities in multicultural and multilingual settings, thereby bringing efforts to democratize AI systems. Our code and data are available at https://github.com/farhanishmam/BanglaProtha.
Recent advances in Vision-Language Models (VLMs) and Large Language Models (LLMs) have demonstrated remarkable zero-shot capabilities while becoming increasingly accessible. They have enabled zero-shot text-to-video search, yet they still struggle on real-world queries that demand temporally aligned reasoning across vision and speech. We present VRAgent, an agentic retrieval framework that leverages a central LLM as a planner which (i) decomposes a free-form user query into a tool-instruction set spanning visual, dialogue and other modality-specific retrievers, and (ii) iteratively self-refines this plan by scoring the outputs and rewriting the next tool-instruction set. The resulting closed-loop optimization acts at test time and requires no additional training data or gradient updates.VRAgent is modular by design—adding a new modality is as simple as adding a corresponding foundation model into the toolbox. On our newly proposed MM-MSRVTT and TVR-1200 multimodal benchmarks, VRAgent improves average recall by +8.3% and +1.7% over the best zero-shot baselines, while on single-modality MSR-VTT and DiDeMo it obtains consistent gains of +3.7% and +4.1%. An interactive variant that asks the user up to two multiple-choice questions pushes average recall to 79.7% on MSR-VTT, underscoring the value of on-the-fly human feedback.
